Categories   >   Big Data Analytics Tools
Last Reviewed: November 21st, 2024

Best Big Data Analytics Tools Of 2024

What are Big Data Analytics Tools?

Big Data Analytics tools empower organizations to glean insights from massive, complex datasets – often exceeding traditional processing capabilities. These tools tackle challenges like data storage, integration, and analysis, unlocking hidden patterns and trends. Their importance lies in driving data-driven decision making, predicting future outcomes, optimizing operations and uncovering new opportunities. Key functionalities include data ingestion, storage, processing, visualization, and reporting. Emerging features focus on real-time analytics, machine learning integration, and advanced visualization techniques. Industries like finance, healthcare, retail, and manufacturing benefit most, using these tools for fraud detection, personalized medicine, targeted marketing, and predictive maintenance. Limitations include scalability demands, data privacy concerns, and the need for skilled professionals. Overall, Big Data Analytics tools offer immense value by transforming vast amounts of data into actionable insights, empowering businesses to operate more efficiently, anticipate trends, and gain a competitive edge.

What Are The Key Benefits of Big Data Analytics Tools?

  • Data-driven decisions
  • Enhanced operational efficiency
  • Future trend prediction
  • Hidden opportunity discovery
  • Improved customer understanding
  • Reduced operational costs
  • Fraud detection and prevention
  • Personalized product/service offering
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SelectHub Award Winners - Array

Our Research Analysts evaluated 78 solutions and determined the following solutions are the best Big Data Analytics Tools overall:

Overall

SAS Viya Award
SAS Viya
SAS Viya
Start Price
$10,000
Annual
Analyst Rating
94
SelectHub’s Analyst Rating for SAS Viya, is based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked SAS Viya

SAS Viya is an AI-powered data management and visual analytics platform with a robust, scalable architecture. All users who reviewed data source connectivity said that it connects to multiple sources and integrates easily with business applications, giving a seamless user experience. With fast in-memory processing of big data sets, it leverages the power of R to enable visual statistics. All users who mentioned predictive analysis said that it enables automated forecasting through what-if scenarios, goal-seeking, text mining and decision trees. Citing ease of use, all users say that the platform is intuitive and enables easy data modeling and self-service visual analytics. All users who mentioned support said that they are responsive and knowledgeable. Around 71% of the users who comment on its functionality say that it is a robust, scalable and flexible platform that enables visualization and analysis of business data, though some users say visual statistics need improvement. On the flip side, all users who review its cost say that the tool is expensive.
In summary, SAS Viya is an analytics tool that provides data management, visualization and AI-powered analytics to enterprises for improved decision making, though small organizations and startups might find it cost-prohibitive.

Pros & Cons

  • Ease of Use: All users who mention its interface say that it makes autonomous analysis and data modeling accessible to users of all skill levels.
  • Support: All users who review support say that representatives are responsive and helpful in resolving issues and queries.
  • Functionality: Around 71% of the users who comment on its feature set say that the software helps discover data insights through powerful visualizations and on-the-fly calculations.
  • Cost: All users who discuss its pricing say that the cost of acquisition is high.

Key Features

  • Visualization and Reporting: Dig into data for in-depth analysis and view key business metrics through autonomous data exploration and manipulation. Create and customize interactive reports and charts to share with others across the organization for collaborative insight. Get suggestions on graphics best suited to display pertinent data through auto charting. 
  • Data Modeling: Analyze data with predictive models through regression, clustering and neural networks. Ensure version control by tracking data models from creation through usage by registering, validating and monitoring each version. Creates snapshots of model properties and files and retains them for the future. 
  • Visual Statistics: Build diverse scenarios simultaneously and refine them with what-if analyses to uncover insights through experimentation. Unifies all business tools, irrespective of the language they support, into a common visual analytics solution. 
  • Cloud Integrations: Develop low-code technologies by porting SAS open-source models into mobile and business applications through its cloud-native capability. Optimize analytics workloads on clouds like Microsoft Azure and ensure cost-efficient migration of analytics to the cloud through a workload management tool. 
  • ML-Based Insights: Get valuable insights from new data types by combining structured and unstructured data in integrated machine learning programs. Choose the desired ML algorithm from a range of options and easily find the optimal parameter settings. Use Python within Jupyter notebooks for deep learning functions like computer vision, natural language processing, forecasting and speech processing. 
IBM Watson Studio
IBM Watson Studio
Start Price
$30
Monthly
Analyst Rating
92
SelectHub’s Analyst Rating for IBM Watson Studio, is based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Why We Picked IBM Watson Studio

User reviews of IBM Watson Studio provide valuable insights into its strengths and weaknesses. The platform is lauded for its advanced analytics capabilities, allowing users to conduct in-depth data analysis and modeling. Collaboration features are appreciated for enabling effective teamwork, fostering knowledge sharing among data scientists, analysts, and stakeholders. AutoAI is a standout feature, automating machine learning tasks and making it accessible to users with varying skill levels. Users find the data visualization tools helpful for creating compelling visualizations that communicate insights effectively. Model deployment within the platform simplifies the transition from development to production environments. On the downside, complexity is cited as a drawback, particularly for newcomers to data science. Resource demands for handling large datasets can be challenging for organizations with limited computational resources. The platform's effectiveness is highly dependent on data quality, which can pose issues with inaccurate or incomplete data. Some users note challenges in interpreting highly complex machine learning models, especially in regulated industries where model transparency is crucial. Integration and customization efforts may be complex and require advanced expertise. In comparison to similar products, IBM Watson Studio is often seen as a robust contender, offering a comprehensive suite of data science and machine learning tools. However, the learning curve and resource requirements may be factors for consideration. User reviews reflect a mix of praise for its capabilities and challenges in mastering its advanced functionalities.

Pros & Cons

  • Advanced Analytics: Users appreciate the platform's robust data analytics and modeling capabilities, allowing them to extract meaningful insights from their data.
  • Collaboration: Watson Studio's collaborative environment is well-received, enabling teams to work together effectively on data science projects.
  • AutoAI: Users value the AutoAI feature, which automates machine learning tasks and accelerates model development, making it accessible to users with varying skill levels.
  • Data Visualization: The platform's data visualization tools help users create informative visualizations, simplifying the communication of insights to stakeholders.
  • Model Deployment: Users find it convenient to deploy machine learning models within the platform, streamlining the process of putting models into production.
  • Integration: Watson Studio's integration capabilities with various data sources and services receive praise for their flexibility and ease of use.
  • Security: Users appreciate the platform's robust security features, ensuring the protection of sensitive data and compliance with regulations.
  • Customization: Watson Studio's customization options allow users to tailor the platform to their specific needs and workflows, enhancing its adaptability.
  • Community Support: Many users benefit from the active and helpful user community, which provides resources and assistance for problem-solving and knowledge sharing.
  • Documentation: IBM's comprehensive documentation is seen as a valuable resource, aiding users in effectively utilizing the platform's features.
  • Complexity: Some users find the platform complex, especially for beginners in data science, which may require a steep learning curve.
  • Resource Demands: Handling large datasets and complex analyses can be resource-intensive, posing challenges for organizations with limited computational resources.
  • Data Quality Dependency: The effectiveness of Watson Studio relies heavily on the quality and cleanliness of input data. Inaccurate or incomplete data can impact analysis outcomes.
  • Interpretability Challenges: Highly complex machine learning models can be challenging to interpret fully, especially in regulated industries where interpretability is crucial.
  • Integration Efforts: Integrating Watson Studio into existing IT environments can require significant effort, particularly for organizations with complex tech stacks.
  • Customization Complexity: Extensive customization may demand advanced knowledge and development skills, potentially limiting accessibility for some users.
  • Scalability Management: While scalable, effectively managing scaling processes, especially for large enterprises, can be complex and require specialized expertise.
  • Documentation Gaps: Users have reported occasional gaps in documentation and support resources, which can hinder troubleshooting and development efforts.
  • Model Deployment Challenges: Deploying models in production environments, particularly in highly regulated industries, can require additional considerations and expertise, posing challenges.
  • Algorithm Selection: Choosing the right algorithm for specific use cases can be challenging, demanding a deep understanding of the platform and algorithm nuances.

Key Features

  • Data Preparation Tools: IBM Watson Studio offers a range of data preparation tools that enable users to clean, transform, and shape data for analysis. These tools simplify the data preprocessing stage, ensuring that data is in the right format for analysis.
  • Collaborative Environment: The platform provides a collaborative workspace where data scientists, analysts, and business stakeholders can work together. It supports version control, project sharing, and real-time collaboration, enhancing teamwork and knowledge sharing.
  • AutoAI: AutoAI is a feature that automates the machine learning pipeline. It automates tasks such as feature engineering, model selection, and hyperparameter tuning, making it easier for users to build and deploy machine learning models without extensive manual work.
  • Model Building and Training: IBM Watson Studio includes tools for building and training machine learning models. Users can access a wide range of algorithms and frameworks, allowing them to create predictive models for various applications.
  • Data Visualization: The platform offers data visualization tools that help users create interactive charts and graphs. These visualizations make it easy to communicate insights and patterns in the data to both technical and non-technical stakeholders.
  • Deployment and Monitoring: Users can deploy machine learning models into production environments directly from the platform. Additionally, IBM Watson Studio provides monitoring capabilities to track model performance and make adjustments as needed.
  • Integration: The platform offers seamless integration with various data sources, databases, and cloud services. This ensures that users can access and analyze data from a wide range of systems, enhancing data availability and flexibility.
  • Security and Compliance: IBM Watson Studio prioritizes data security and compliance. It includes features like access controls, encryption, and audit trails to protect sensitive data and maintain compliance with industry regulations.
  • Customization and Extensibility: Users can customize and extend the platform's functionality using open APIs and integration options. This flexibility allows organizations to tailor IBM Watson Studio to their specific needs and workflows.
  • AutoML: AutoML capabilities automate the machine learning process, making it accessible to users with varying levels of expertise. It simplifies model development and accelerates the time-to-value for AI and machine learning projects.
Spotfire Award
Spotfire
Spotfire
Start Price
$0.99
Monthly, Quote-based
Analyst Rating
92
SelectHub’s Analyst Rating for Spotfire, is based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Why We Picked Spotfire

In online reviews, Spotfire emerges as a user-friendly big data platform. Most users found data exploration easy with a drag-and-drop interface. Some users said the UI was dated, though, and said it could use a revamp. Most users praised its interactive visualizations and dashboards, saying they helped them interpret data better. But, a few said they would love to have more visuals to choose from.

A user mentioned they did the calculations in Excel and imported them into Spotfire for visualization. It's a common scenario when a steep learning curve slows down adoption, and teams fall back on Excel. Most users said Spotfire takes time to learn. You might have to opt for a balance of multiple platforms to balance your departmental and enterprise needs.

Spotfire surpasses Excel in data management, especially data prep. Customizable visualizations and custom Mods give you enough freedom to work within the platform.

Though 72% of reviewers were happy with the integrations, Spotfire lacks some standard connectors, such as for Apache Kafka, forcing users to rely on workarounds.

A majority of users found its pricing structure complex, especially as users increased. In such cases, organizations often tend to opt for a cheaper alternative for less advanced use cases while using the pricier platform for the critical ones. We advise doing a deep dive into the vendor's pricing plans to avoid making your tech stack top-heavy.

Ultimately, Spotfire's appeal lies in its balance. It's visually captivating and user-friendly for casual users while offering enough depth for seasoned analysts. However, its pricing and learning curve might deter organizations on a tight budget.

Pros & Cons

  • Data Visualization: About 86% of reviewers were satisfied with the available options when designing dashboards.
  • Support: Around 74% of users praised vendor support for their timely response and helpful attitude.
  • Integration: Almost 72% of users were satisfied that it integrates with their preferred systems.
  • Friendly Interface: Around 68% of reviewers said the platform was easy to use.
  • Functionality: About 64% of users said it had a rich feature set.
  • Cost: Around 96% of the user reviews said it the price was high and licensing complex.
  • Adoption: 90% of reviewers said there was a significant learning curve and users would need specialized knowledge of data science and statistics.

Key Features

  • Spotfire Actions: Decide what to do with and act instantly — no need to switch to your procurement application to pause new orders. This powerful feature allows you to run scripts within analytics workflows. You can also trigger actions in your external system through visualization. Spotfire can set up over 200 commercial connections and has 1800 community connectors.
  • Mods: Build reusable workflows and visualization components, much like apps in Power BI and Qlik Sense. They allow your users to tailor their analytical processes so they don’t have to start from scratch every time. Based on code, they run in a sandbox with limited access to system resources for security. Users can share them through the Spotfire library. Mods improve efficiency and collaboration.
  • Batch Edits: Make similar changes to multiple files in one go. Write custom scripts to call the Spotfire API that’ll make changes to the files. Update the IronPython version to the latest one or embed the Spotfire JQueryUI library instead of its references.
  • Recurring Jobs: Simplify event scheduling to better manage your time and tasks. Improve efficiency and deliver reports at the same time on the same day of the week or month. The latest Spotfire version allows you to set recurring automation jobs to occur every X hours, days, weeks or months.
  • Web Player REST API: Share insight with clients and partners without them needing to sign up for a paid Spotfire account. Engage them via data visualizations on the web browser, thanks to Spotfire Web Player. Update analyses on the web with real-time data in the latest Spotfire version.
  • Roles: Invest wisely — opt for licenses that align with user roles. Choose Spotfire Analyst for data analysts, scientists and power users who need deep-dive analysis. Get the Business Author license for enterprise users, analysts and power users to create and consume insights without deep expertise. Choose consumer licenses for users who’ll interact with and consume data. They include the C-suite and non-technical users within the organization.
  • Information Designer: Prepare fully governed data sources for business users in a dedicated wizard. Set up their preferred data sources and define in advance how Spotfire will query and import data into storage. Specify which columns to load and which filters, joins and aggregations to apply.
  • Audio and Image Processing: Add user feedback from customer calls and videos. Interpret public sentiment about your product by analyzing social media pictures and videos. Spotfire enables writing code to extract text from audio and image files. You can then import the data into the platform for analysis.
  • IoT Analytics: Gain insight at lightning speed; build microservices and deploy them at the edge. With Spotfire, you can add IoT data to your regular data for the complete picture.
Dataiku Award
Dataiku
Dataiku
Start Price
$4,000
Monthly
Analyst Rating
91
SelectHub’s Analyst Rating for Dataiku, is based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked Dataiku

User reviews for Dataiku reveal a mixed sentiment, with notable strengths and weaknesses. Users appreciate Dataiku's comprehensive feature set, user-friendly interface, and its effectiveness in facilitating collaboration among diverse teams. Scalability is another advantage, making it suitable for various organizational sizes. AutoML capabilities and real-time insights are well-received for their accessibility and timeliness. However, several users express concerns about a steep learning curve, especially for newcomers to data science. The platform's resource-intensive nature can be challenging, and the cost of licensing may be a barrier for smaller organizations. Some users find limitations in the free community edition and face integration challenges with legacy systems or non-standard data sources. Data quality dependency and customization complexity are other reported cons. Dataiku is often compared to similar products in a competitive market, and users stress the importance of evaluating it against specific needs and budgets. Security-conscious organizations may need additional measures when handling sensitive data. Despite its limitations, Dataiku maintains a strong user base due to its robust feature set and collaborative capabilities, enabling data-driven decision-making in various industries.

Pros & Cons

  • Comprehensive Feature Set: Users appreciate Dataiku's wide range of features, from data preparation to advanced machine learning, enabling end-to-end data analytics.
  • Intuitive Interface: Dataiku's user-friendly interface receives praise for its ease of use, making it accessible to both data professionals and business users.
  • Effective Collaboration: Many users find Dataiku's collaborative environment conducive to teamwork, facilitating cross-functional collaboration on data projects.
  • Scalability: Dataiku's scalability is highly regarded, making it suitable for small teams and large enterprises, adapting to evolving data requirements.
  • AutoML Capabilities: Users value Dataiku's AutoML functionality, which simplifies machine learning, making it accessible to users with varying levels of expertise.
  • Real-Time Insights: Dataiku's ability to provide real-time insights is a significant benefit, enabling timely decision-making based on up-to-date data.
  • Data Governance: Dataiku's robust data governance features are highly regarded, helping maintain data quality and ensuring compliance with regulations.
  • Community and Support: Users appreciate the Dataiku community and support resources, which provide valuable assistance and guidance.
  • Integration Capabilities: Many users highlight Dataiku's seamless integration with other tools and systems, enhancing their data workflows.
  • Transparency and Explainability: Dataiku's focus on model transparency and explainability is praised, enhancing trust in machine learning models.
  • Steep Learning Curve: Some users find Dataiku's learning curve to be relatively steep, particularly for those new to data science and machine learning.
  • Resource Intensive: Running complex operations and large-scale data processing in Dataiku can be resource-intensive, potentially requiring substantial computing power.
  • Costly Licensing: The cost of Dataiku's licensing can be a concern for small organizations or startups with limited budgets.
  • Limited Free Version: The free community edition of Dataiku has limitations in terms of features and scalability, which may not meet the needs of larger enterprises.
  • Integration Challenges: Some users encounter challenges when integrating Dataiku with certain legacy systems or non-standard data sources, requiring additional effort and customization.
  • Dependency on Data Quality: The effectiveness of Dataiku's analysis and modeling heavily relies on the quality of input data, which can be a challenge if data is not well-maintained.
  • Customization Complexity: Highly customized data workflows may require a deeper understanding of the platform, potentially making customization more complex.
  • Real-Time Processing: Dataiku may not be the ideal choice for applications requiring real-time data processing, as it primarily focuses on batch processing.
  • Competitive Market: Dataiku operates in a competitive market with various alternatives, making it essential for users to evaluate if it aligns with their specific needs and budget.
  • Security Concerns: While Dataiku offers security features, organizations handling highly sensitive data may need additional security measures to meet compliance standards.

Key Features

  • Data Integration: Dataiku offers seamless data integration capabilities, allowing users to connect to various data sources, import data, and prepare it for analysis.
  • Machine Learning Studio: Users can build, train, and deploy machine learning models in a user-friendly visual interface, streamlining the data science workflow.
  • Data Preparation: Dataiku provides robust data preparation tools, enabling users to clean, transform, and enrich data efficiently for analysis.
  • AutoML: The AutoML functionality automates model selection and hyperparameter tuning, simplifying the process of creating accurate machine learning models.
  • Collaboration Hub: Dataiku offers a collaborative environment where data scientists, analysts, and business users can work together on data projects.
  • Model Deployment: Users can easily deploy machine learning models into production, ensuring that insights generated from data are put into action.
  • Visual Data Exploration: Dataiku enables users to visually explore data through interactive charts and graphs, facilitating data discovery and insights.
  • Data Governance: The platform includes robust data governance features, such as data lineage, access controls, and auditing, ensuring data quality and compliance.
  • Automated Reporting: Users can create automated reports and dashboards to share insights with stakeholders, streamlining communication.
  • Scalability: Dataiku is designed to scale with an organization's data needs, making it suitable for both small teams and large enterprises.
H2O.ai Award
H2O.ai
H2O.ai
Start Price
$6,900
Annually
Analyst Rating
89
SelectHub’s Analyst Rating for H2O.ai, is based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked H2O.ai

User reviews for H2O.ai offer insights into both its strengths and weaknesses. Many users praise its efficient AutoML capabilities, making machine learning more accessible and saving time in model development. The scalability of H2O.ai is also widely appreciated, accommodating various data volumes for businesses. Users find its model interpretability tools valuable, particularly in regulated industries, for understanding complex models. The platform's open-source foundation fosters collaboration and transparency, drawing positive remarks. Its comprehensive ecosystem and support for advanced algorithms are additional strengths, enabling users to extend and customize their workflows effectively. On the downside, some users highlight a steep learning curve, particularly for newcomers to machine learning. The resource-intensive nature of H2O.ai, especially when dealing with large datasets, can be a limitation for those with limited computational resources. Data quality dependencies impact model performance, and complex model interpretation remains a challenge. Integrating H2O.ai into existing IT environments can be labor-intensive, and extensive customization may demand advanced knowledge. Effective scalability management can also pose complexities. Occasional gaps in documentation and support resources have been noted, affecting troubleshooting and development efforts. Compared to similar products, users see H2O.ai as a robust contender, offering a rich set of features and a vibrant open-source community. However, its learning curve and resource requirements may be factors for consideration. Ultimately, user reviews reflect a mix of praise for H2O.ai's capabilities and challenges faced in mastering its advanced functionalities.

Pros & Cons

  • Efficient AutoML: Users appreciate H2O.ai's AutoML capabilities, which streamline the machine learning process, making it accessible to a broader audience and saving time on model development.
  • Scalability: Many users highlight H2O.ai's ability to handle large datasets and scale seamlessly, accommodating the needs of businesses with varying data volumes.
  • Model Interpretability: H2O.ai's interpretability tools are praised for helping users understand and trust complex machine learning models, particularly in regulated industries.
  • Open Source Foundation: Users appreciate the open-source nature of H2O.ai, which fosters collaboration, transparency, and a thriving community of users and developers.
  • Comprehensive Ecosystem: The platform's extensive ecosystem of tools and integrations is a pro, as it allows users to extend and customize their workflows to suit their specific needs.
  • Advanced Algorithms: H2O.ai's rich library of advanced machine learning algorithms is well-received by users, enabling them to tackle complex problems effectively.
  • Model Management: Users find the model management capabilities of H2O.ai robust, making it easier to monitor, update, and deploy models for ongoing success.
  • Flexible Deployment Options: The flexibility to deploy models in various environments, including cloud and on-premises, is seen as a significant advantage by users, aligning with their infrastructure needs.
  • Support for Diverse Data Sources: Users value H2O.ai's support for a wide range of data sources, enabling them to leverage their entire data ecosystem for machine learning projects.
  • Time-to-Value: Many users report that H2O.ai accelerates their time-to-value for data-driven initiatives, allowing them to derive insights and achieve results more quickly.
  • Steep Learning Curve: Some users find H2O.ai challenging to learn, particularly for those new to machine learning, due to its advanced features and concepts.
  • Resource Intensive: Users with limited computational resources may encounter challenges when working with large datasets or complex models, as it can demand substantial computing power.
  • Data Quality Dependencies: H2O.ai's effectiveness relies heavily on high-quality, clean data, and users report that poor data quality can impact model performance and accuracy.
  • Complexity of Interpretation: While the platform offers interpretability tools, users mention that fully understanding highly complex machine learning models can still be challenging.
  • Integration Efforts: Integrating H2O.ai into existing IT environments can require significant effort and technical expertise, which may be a drawback for some organizations.
  • Customization Complexity: Extensive customization can be complex, and users with specific use cases may need advanced knowledge of the platform to tailor solutions effectively.
  • Scalability Management: While H2O.ai is highly scalable, effectively managing scaling processes, particularly for large enterprises, can be complex and may require specialized expertise.
  • Documentation Gaps: Some users have noted occasional gaps in documentation and support resources, which can hinder troubleshooting and development efforts.
  • Model Deployment Challenges: Deploying models in production environments, especially in highly regulated industries, can require additional considerations and expertise, which users find challenging.
  • Algorithm Selection: Choosing the right algorithm for specific use cases can be a hurdle, as it demands a deep understanding of the platform and the nuances of different algorithms.

Key Features

  • AutoML: H2O.ai's AutoML functionality automates the end-to-end machine learning process. It handles everything from data preprocessing to model selection and hyperparameter tuning, making machine learning accessible to a broader range of users.
  • Scalability: H2O.ai's platform is highly scalable, capable of handling both small and large datasets with ease. This ensures that organizations can work with data of varying sizes, accommodating growth and evolving needs.
  • Interpretability: Model interpretability is crucial for understanding how and why a model makes predictions. H2O.ai provides tools and techniques for explaining complex machine learning models, enhancing transparency and trust.
  • Efficiency: H2O.ai streamlines the machine learning workflow, reducing the time and resources required to develop and deploy models. This efficiency accelerates the delivery of data-driven insights and solutions.
  • Open Source: H2O.ai embraces an open-source philosophy, fostering collaboration and innovation. The platform is built on open-source technologies, allowing users to contribute to its development and benefit from a thriving community.
  • Flexible Deployment: Users can deploy models generated with H2O.ai in a variety of environments, including cloud, on-premises, and edge computing. This flexibility ensures that the solution aligns with an organization's specific infrastructure and security requirements.
  • Data Source Compatibility: H2O.ai supports diverse data sources, including structured and unstructured data, enabling organizations to leverage their entire data ecosystem for machine learning projects.
  • Comprehensive Ecosystem: H2O.ai offers a comprehensive ecosystem of tools and integrations that enhance its functionality. Users can extend and customize the platform to integrate seamlessly into their existing workflows and technology stack.
  • Advanced Algorithms: The platform includes a rich set of advanced machine learning algorithms, empowering data scientists and analysts to tackle complex problems and extract valuable insights from their data.
  • Model Management: H2O.ai provides robust model management capabilities, allowing users to monitor, update, and deploy models effectively. This feature ensures that models remain relevant and accurate over time.
SAP Analytics Cloud
SAP Analytics Cloud
Start Price
$36
Per User, Monthly
Analyst Rating
87
SelectHub’s Analyst Rating for SAP Analytics Cloud, is based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked SAP Analytics Cloud

SAP Analytics Cloud is a powerful data visualization tool that helps users explore data in an easy-to-understand format and discover metrics that help them make informed decisions.

Many users who reviewed visualization said that the platform has rich graphics libraries and a range of templates for story presentation of business metrics. Citing its aesthetic design, a majority of users said that the solution’s interface is intuitive for programmers and non-technical users alike. Mentioning its strong predictive and augmented analysis capabilities, a lot of users said that the platform is a strong planning tool and helps them gain business insights easily. Many users said that the tool works well on a plethora of devices, including mobile, and is accessible on multiple browsers, though some users said that its offline mode capabilities are limited. Most of the users who reviewed data integrations said that the tool handles large amounts of data seamlessly, though quite a few users said its compatibility with live connection data sources is limited to other SAP products. Reviewing functionality, a majority of users said that the tool is versatile and scalable to ensure a great end-to-end user experience, though some said that it needs more features, such as improved live data analytics.

The solution does have its limitations, though. Around 85% of users who reviewed its performance said that it struggles at the enterprise level, though quite a few users said that it performs exceedingly well within the SAP ecosystem. A majority of the users who mentioned reporting said that the solution lacks automatic report distribution and broadcasting capabilities, though some said that it has excellent analytical and data-oriented report creation. Many users who discussed data management complained that the process for updating an existing data model needs a lot of rework, and that the solution needs more versatile modeling features.

Overall, SAP Analytics Cloud is a planning tool with strong BI capabilities that brings the power of cloud computing to self-service data analysis for gaining actionable insights into business metrics.

Pros & Cons

  • Data Visualization: Approximately 91% of users who reviewed visualization said that they can customize dashboards effortlessly with the solution’s wide range of templates and graphics libraries.
  • Ease of Use: Citing the platform’s modern and intuitive interface, around 89% of users said that the platform makes it easy to develop stories without technical knowledge thanks to Smart Discovery.
  • Data Analysis: Mentioning its forecasting and predictive analysis capabilities, approximately 87% of users said that the solution enables collaborative enterprise planning via statistical and predictive analysis.
  • Web and Mobile Accessibility: Discussing its easy accessibility on multiple browsers, around 72% users said that the platform works well on a wide range of devices.
  • Data Connectivity: Approximately 70% of the users who mentioned integration said that the tool, with strong live data connections, integrates well with other applications and data sources.
  • Functionality: Around 55% of the users who discussed features said that the tool is scalable and quite versatile, with frequent updates.
  • Performance: Approximately 85% of users who reviewed performance said that some features do not work well at the enterprise level.
  • Reporting: Around 58% of users who mentioned reporting said that report creation with the tool is complicated and needs IT support.
  • Data Management: Approximately 52% of users who discussed data preparation said that the platform needs more ubiquitous data modeling capabilities.

Key Features

  • Data Preparation: Alerts as to possible errors and automatically cleans data through machine learning technology to enhance data for deeper insights. Offers context-based suggestions for better data preparation and data visualizations through Smart Transformations.
  • Chat With Data: Type questions into the Search to Insight bar in natural language to instantly create visualizations — no syntax mastery necessary. Analyze data easily, similar to using a search engine.
  • Instant Data Visualization: See the full picture in seconds; the solution generates interactive charts and graphs automatically upon query.
  • Predictive Forecasting: Generates prediction models that learn from historical data to automatically detect risks and predict what is likely to happen in the future.
  • What-If Analysis: Create what-if simulations through the power of self-service machine learning. Plan for the next steps by fine-tuning prediction scenarios based on confidence levels.
  • Highlight KPIs: Answer the “why” and “what” questions through augmented analytics. Identify the top contributors to business performance — the people, products and processes that contribute to the overall story.
  • Analyze Anomalies: Take corrective action aided by Smart Discovery, which automatically points out outlying data.
  • Hybrid Analytics: Protect technology investments by continuing to manage on-premise systems and existing data infrastructure, while simultaneously moving towards the future by leveraging the cloud. Supports a wide range of data connections, ensuring import and access to data from any source to maintain a 360-degree view of the business.
  • Partners: Over 100 partners offer upwards of 430 analytical extensions in the SAP App Center that provide additional value and industry specific applications to the platform.
  • Built-In Collaboration: Create discussions, assign tasks and deadlines and comment on data points. Share information and interact with each other continuously on screen.
  • Mobile App: Stay connected to the Cloud via a responsive, interactive iOS app.
Oracle Analytics Cloud Award
Oracle Analytics Cloud
Oracle Analytics Cloud
Start Price
$16
Per User, Monthly
Analyst Rating
87
SelectHub’s Analyst Rating for Oracle Analytics Cloud, is based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
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Deployment
Cloud On-Premise
Platform
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Why We Picked Oracle Analytics Cloud

Oracle Analytics Cloud is among the vendor’s many data services, including a business intelligence suite and a data intelligence platform. Besides, Oracle offers bespoke solutions for HCM, supply chain and customer experience. What differentiates Oracle Analytics is that extra dash of augmented capabilities.

Embedded BI is where it truly shines, giving you natural language insights with a single click. This feature extends to its mobile app, and it outperforms many leading platforms with natural language queries and podcasts on mobile.

According to our researchers, Oracle Analytics Cloud has fewer out-of-the-box features than its competitors, such as Power BI and Qlik Sense. Plus, licensing becomes complex when combining the database, middleware and analytics applications.

It’s common for large vendors to offer specialized platforms, but the downside is that they can be out of reach of small organizations. But there’s a silver lining. Many vendors offer customized solutions, so we advise reaching out to the vendor for quotes.

Users appreciate its regular updates, but some report initial bugs due to its relative newness. Despite a positive user experience, the learning curve can be steep. Some users found technical support slow and inadequate, as did I. They took two business days to get back to me when I needed assistance with my account.

Oracle Analytics, though a robust platform, is suitable for mid- and large organizations. If you seek a powerful, scalable platform, consider opting for a trial, but be prepared for sticker shock, especially if you’re new to the Oracle ecosystem.

Pros & Cons

  • User-Friendly: Citing its interface, about 91% of users agreed that a drag-and-drop UI makes it easy to use.
  • Machine Learning: Around 86% of users who discussed augmented analytics were impressed with its ML capabilities.
  • Integrations: Approximately 84% of users who mentioned connectivity said the platform worked well with other systems, especially Oracle products.
  • Functionality: According to 83% of users who reviewed capabilities, it has all the required features to support data tasks.
  • Data Visualization: Around 73% of users who mentioned visualization praised the platform for its storytelling features.
  • Price: About 88% of reviews citing pricing said that it’s too expensive.
  • Adoption: Approximately 87% of users who discussed onboarding said there’s a significant learning curve.

Key Features

  • Deployment: Install and run anywhere, including as a hybrid solution. Scale the instance depending on your workload — deploy OCPUs in multiples of two, extending up to 52. Or pause it when idle. Though identity management is available, there is the option to use one’s own SSO provider. Admins can set user, group and role-based permissions.
  • Connectivity: Make decisions based on data; connect to social media feeds, data lakes and IoT sources. Store and process data at scale, irrespective of its volume, velocity and variety. Get started as soon as you log in with over 40 readymade connectors.
  • Direct Query: Oracle Analytics Cloud uses live queries and data caching to fetch responses. Each has its downside. Live connections are heavier on the system, and you might have to compromise on data freshness with data caching. A combination of both might be best. Consider live queries for critical KPIs and data caching for less frequent queries.
  • Data Preparation: Enrich data from the interface — get data quality insights as you work. Remove the grunt work — create reusable flows for transforming data you can test, share and schedule. Add custom calculations or write regular expressions in the dataset editor.
  • Semantic Data Modeling: Engage business, dev and data teams in meaningful discussions. Give them data views with a presentation layer that simplifies metrics. Hide the physical data structure with a logical one that speaks the business language. Give stakeholders the power to explore data independently.
  • AI/ML: Boost productivity with embedded machine learning and natural language insights every step of the way. Display quick forecasts, trend lines and clusters from a popup menu with one click. View the basic facts, key drivers and anomalies with the Explain option. Hit the ground running with recommendations on dimensions, measures and attributes to use when you don’t know where to start.
  • Oracle Analytics Publisher: Generate reports from any dataset or semantic model. Create formatted documents unique to your business, be it shipping labels, checks, letters or PDF forms.
  • Data Visualizations: Put your best foot forward with suitable charts and graphs that convey your message effectively. Modify them to answer users’ questions better. Choose from over 45 visualization types, or build your own using extensions from its vast library.
  • Embedded AI On Mobile: Get real-time alerts and intelligent recommendations on mobile. The Oracle Analytics mobile app captures your preferences and location. Upload datasets just like on the desktop or create a workbook from existing data. Powerful searches enable access to your favorite worksheets; add them to your home screen for a quick view. Use voice-enabled searches and listen to the results as a podcast.
RapidMiner
RapidMiner
Start Price
$10
Annual, Free
Analyst Rating
86
SelectHub’s Analyst Rating for RapidMiner, is based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked RapidMiner

Rapidminer is an end-to-end data science platform that performs a wide range of functions, from data prep to machine learning to predictive modeling. According to most of the users who reviewed the tool’s support, online communities are responsive in answering queries and helping resolve issues. Many of the users who discussed the interface said that, with an intuitive layout and great design, the UI offers easy drag-and-drop functionality for rapid prototyping - no programming experience needed. A majority of the users who mentioned online resources said that crisp and informative tutorials and videos are readily available online, and that the vendor’s website offers up-to-date information on the tool. According to many users who discussed data management, the platform works well for clustering, fast cleaning and data preparation with its built-in functions and algorithms. Many of the users who reviewed its analytic capabilities said that the solution uses machine learning for data exploration and visualization to derive insights from almost any source of data, though some users said that more statistical models are needed. With new functionalities being introduced from time to time, many users said that the platform stays versatile and has powerful data processing capabilities.
On the flip side, many users who reviewed speed and performance said that the platform is resource-intensive and slows down when running complex data models. Reviewing adoption, some users said that there is an initial learning curve and tutorials should be built within the tool for prompt troubleshooting. Quite a few users who reviewed the tool’s data prep capabilities said that better ETL features are needed, especially for plots and graphs, and extensive dataset modeling may require higher computing power that can slow down the platform.
In summary, RapidMiner, with its rich libraries, functions and algorithms, helps in AI-driven data exploration and mining for self-service data model development to drive advanced predictive analytics for enterprises.

Pros & Cons

  • Online Community: Around 95% of the users who reviewed support said that the online communities are helpful, proactive and knowledgeable.
  • Ease of Use: Citing its great layout and design, approximately 93% of users said that the interface offers a no-programming, user-friendly experience.
  • Training: Around 78% of the users who reviewed training resources said that a plethora of tutorials, videos and guides are readily available online.
  • Data Management: According to 77% of the users who discussed data management, the platform has built-in functions for fast and intuitive data cleaning and data preparation.
  • Data Analysis: Around 70% of the users who reviewed analytics said that the platform has powerful machine learning capabilities with a multitude of built-in algorithms for advanced predictive analysis.
  • Functionality: Mentioning a wide range of add-ons and toolboxes, approximately 55% users said that the solution is versatile, with regular updates and powerful data processing capabilities.
  • Performance and Speed: Around 88% of the users who reviewed its performance said that the platform is resource-hungry and slows down when processing complex datasets.

Key Features

  • Visual Workflow Designer: Create an end-to-end analytic workflow through a drag-and-drop, singular interface that requires little coding. 
  • Data Visualization: It has an internal framework for producing more than 30 interactive data visualizations, with the capability to add more. Explore and drill down into data to digest trends and patterns more easily. 
  • Data Management: Use the Turbo Prep app to streamline data preparation. Ingest, load and store data from more than 40 file types, and scrape data from URLs, NoSQL databases, business applications and cloud storage. 
  • Automatic Modeling and Validation: Deploy data models without coding. Automatically generate models and compare them to similar models to predict the best possible direction for a project to take. 
  • Apache Integration: RapidMiner Radoop is a user-friendly interface for connecting and utilizing Apache Hadoop for distributed analytics and scaling, without having to program in Spark. Increase processing limits and tap into advanced processes like machine learning without leaving the RapidMiner interface. 
  • Data Preparation: Prepare, cleanse, blend and wrangle data through the Turbo Prep interface. Get an in-depth view of the dataset at each step. Make changes in real time, visible in pivot tables. 
KNIME
KNIME
Start Price
$0
Open-Source
Analyst Rating
86
SelectHub’s Analyst Rating for KNIME, is based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Company Size
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Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked KNIME

KNIME is a robust open-source solution with cross-platform interoperability. It integrates with a range of software, such as JS, R, Python and Spark. With a variety of nodes and functions, it can process large datasets with a decent level of control in each step. Workflows are displayed as connected nodes, making it easy to isolate and fix specific steps. It also contains built-in tools to create and test supervised and unsupervised machine learning models. Users found the UI very intuitive and flexible. On the flip side, they found the tool visually lacking and primitive. The system also has performance and stability issues. Processing big data is very time consuming since the platform isn’t cloud-based. Users reported excessive memory usage as well. It also lacks reporting or monitoring features. Decent technical knowledge is required to fully leverage its capabilities.

Pros & Cons

  • Functionality: It provides a comprehensive set of nodes and functions to process large quantities of data, as noted by 69% of users who referred to functionality.
  • User Friendly: It is intuitive and easy to use, as noted by 79% of reviewers who refer to ease of use.
  • Connectivity: Around 77% of users who talked about connectivity mentioned its ability to seamlessly connect and integrate with multiple sources.
  • Cost: All users were happy that the solution is available free of charge, with no data limits.
  • Performance: Nearly 95% of reviewers who mentioned performance said that the solution runs slowly and uses too much CPU and memory.
  • Visualization: Approximately 67% of users who specified visualization talked about its lack of proper visualization options.
  • Support: About 67% of users who reviewed support mentioned how hard it is to get proper documentation or support.
  • Learning Curve: KNIME has a steep learning curve, according to about 64% of users who mentioned the learning curve.

Key Features

  • Sharing and Collaboration: KNIME Hub is an online repository for existing workflows, nodes and extensions that can be easily installed into a user’s workflow. Upload workflows and search for the components needed for projects. 
  • In-database or Distributed Processing: Process data in-database or through a distributed cluster like Apache Spark for increasing scale. It has prebuilt workflows for in-database processing, like SQL Servers. 
  • Model Predictions and Validation: Using machine learning and AI, produce predictive and prescriptive models. Use performance metrics such as AUC and R2 to verify models.  
  • Visual Workflows: Using a drag-and-drop interface, compose a workflow with little to no coding. Prebuilt generic workflows and components can be downloaded from KNIME Hub. 
  • Data Management: Handles all steps of the extract, transform and load processes. It can ingest, blend, prepare, cleanse and store structured and unstructured data. It can combine data types, including PDF, JSON, CSV and unstructured types like documents and images. 
  • Data Visualizations: Compile analyses into reports with heat graphs, bar charts, scatter plots and more. Visualizations can be exported as PDFs, PowerPoints or other formats.  
  • Tool Blending: Tools with unique domains can be combined within a workflow via native nodes. These include Python or R scripting, processing connectors, machine learning and AI. 
Alteryx Award
Alteryx
Alteryx
Start Price
$99
Per User, Monthly
Analyst Rating
86
SelectHub’s Analyst Rating for Alteryx, is based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked Alteryx

Alteryx is a data science solution that leverages the power of AI and ML to blend, parse, transform and visualize big business data to promote self-serve analysis of business metrics.
Many users who reviewed data analysis said that the tool performs statistical, spatial and predictive analysis in the same workflow. Most of the users who reviewed data processing said that, with a lightweight ETL tool, the platform has strong data manipulation and modeling efficiencies, though some users said that it can be tricky to use SQL queries. Citing integration with Power BI, Tableau and Python, most of the users said that the tool connects seamlessly to data from databases and files, apps, and third-party data sources, among others, to expand the reach of search-based and AI-driven analytics. Most of the users who discussed ease of use said that the tool is intuitive with drag-and-drop functionality and a well-designed interface, though some users said error handling can be challenging for automated workflows. Most of the users who reviewed support said that online communities are helpful in providing answers to queries. Citing automated workflows, many users said that the tool helps save time, though some users said that these can be overly complex and need improvement.
On the flip side, many users who reviewed pricing said that its expensive licenses and add-ons are cost-prohibitive, and cost per core is high for enterprises looking to scale. A majority of users who reviewed its visualization capabilities said that they need to export data to visually stronger applications, such as Tableau or Power BI, to make the reports presentation-worthy. Citing slow runtimes when executing complex workflows, especially with large datasets, many users said that performance-wise, the solution is prone to infrequent crashes. Most of the users who discussed learning said that with documentation not being in sync with latest releases, training is a must to optimally use the tool.
Overall, Alteryx is a data science tool that, with its low-code approach and strong data wrangling capabilities, makes the journey from data acquisition to data insights seamless and promotes data literacy across organizations, though it might be better suited for medium- to large-sized organizations.

Pros & Cons

  • Data Analysis: All users who reviewed analytics said that the platform adds value to data through features such as statistical modeling and predictive analysis.
  • Data Processing: Around 86% of the users who mentioned data processing said that, with a lightweight ETL tool, the solution excels at data wrangling for further analysis.
  • Data Integration: Citing strong integration with multiple data sources and tools, around 84% of the users said that it works well with big data.
  • Ease of Use: Approximately 83% of the users who mentioned ease of use said that the platform’s low-code approach, with drag-and-drop functionality, makes the interface user-friendly.
  • Online Community: The online community is responsive and helpful, according to around 74% of users who discussed support for the platform.
  • Functionality: With fuzzy matching and join capabilities, the platform is feature-rich and versatile, said approximately 63% of users who discussed functionality.
  • Cost: In addition to the high cost of licenses, the price of add-ons is limiting, said around 89% of the users who reviewed pricing.
  • Data Visualization: Around 75% of users who reviewed its presentation capabilities said that with outdated graphics, the platform lags behind other solutions in data visualization.
  • Performance: The solution is prone to infrequent crashes, especially when processing large amounts of data, as said by 65% of users who discussed performance.
  • Training: Approximately 54% of the users who reviewed learning said that with the documentation not being up to date with latest features, there is a steep learning curve and training is required.

Key Features

  • Internal Data Visualization: Display data insights at each stage of ETL, enabling validation and verification at every step of analysis through its in-platform data visualization solution, Visualytics. 
  • Data Visualization Export: Export to data visualizers like Qlikview and Tableau in several formats seamlessly, if the platform’s in-house visualization capabilities don’t satisfy the business’s needs. 

COMPARE THE BEST Big Data Analytics Tools

Select up to 5 Products from the list below to compare

 
Product
Score
i
SelectHub’s Analyst Rating is based on data from our 400+ point analysis of Big Data Analytics Tools, user reviews, and our own crowd-sourced data from our free software selection platform. Scores are periodically updated via ongoing selection projects and user activity within our platform.
Start Price
Free Trial
Company Size
Deployment
Platform
Logo
SAS Viya
94
$10,000
Annual
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
SAS Viya
IBM Watson Studio
92
$30
Monthly
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
IBM Watson Studio
Spotfire
92
$0.99
Monthly, Quote-based
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Spotfire
Dataiku
91
$4,000
Monthly
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Dataiku
H2O.ai
89
$6,900
Annually
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
H2O.ai
SAP Analytics Cloud
87
$36
Per User, Monthly
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
SAP Analytics Cloud
Oracle Analytics Cloud
87
$16
Per User, Monthly
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Oracle Analytics Cloud
RapidMiner
86
$10
Annual, Free
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
RapidMiner
KNIME
86
$0
Open-Source
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
KNIME
Alteryx
86
$99
Per User, Monthly
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Alteryx

All Big Data Analytics Tools (78 found)

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Talend

by Talend, Inc.
Talend
Talend is an open-source data integration and management platform that enables big data ingestion, transformation and mapping at the enterprise level. The vendor provides cross-network connectivity, data quality and master data management in a single, unified hub – the Data Fabric. Based on industry standards like Eclipse, Java and SQL, it helps businesses create reusable pipelines – build once and use anywhere, with no proprietary lock-in.The open-source version is free, with the cloud data integration module available for a monthly and annual fee. The price of Data Fabric is available on request. Pros Open source affordable Visual drag-and-drop UI Wide range of connectors Scalable cloud-ready Active community support Cons Steeper learning curve Limited native profiling Customization can be complex Debugging can be challenging Enterprise features require paid editions
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
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Looker Studio

by Google
Looker Studio
Google Looker Studio is a robust data analytics and business intelligence platform designed to help organizations extract valuable insights from their data. It offers a user-friendly interface for data exploration, powerful visualization tools, and real-time data monitoring. Key features include customizable dashboards, SQL-based queries, and seamless data integration from various sources. Users can collaborate within the platform, fostering a data-driven culture. Looker Studio empowers users to make data-informed decisions, with the ability to create tailored reports and leverage machine learning for predictive analytics. It suits businesses of all sizes and industries, facilitating scalable analytics and promoting data-driven decision-making.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
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Infor Birst

by Infor, Inc.
Infor Birst
Infor Birst is a cloud-based analytics software tool that aims to help users discover insights without the need for analyst input. It unifies IT-managed enterprise data with user-owned data, supporting the blending of both in a top-down and bottom-up manner. It uses consistent business metrics to structure raw data into organized sets and visualizations. It helps users identify patterns and better understand their organization’s KPIs. It offers a seamless, integrated UI that allows users to perform every step of the data analysis process in a single interface, enabling a smooth experience. It can be deployed either from the cloud or self-hosted on-premise. Users can purchase it in three available formats: per-user fee, by department or business unit or by end-customer in embedded scenarios.To explore other popular solutions that Infor offers, visit our Infor company page.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
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KNIME

by KNIME
KNIME
KNIME is an open-source end-to-end data analytics solution. It utilizes visual workflows with drag-and-drop functionality and thousands of nodes to lessen the data analytics learning curve data, with more than 1,800 prebuilt default workflows for streamlined setup. It allows for data ingestion, preparing, cleansing, analyzing and visualizing. It can be scaled for deeper analytics through integrations with sophisticated data modeling capabilities. It can be hosted on-premise or in the cloud through Microsoft Azure.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 or less
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Airflow

by Apache Software Foundation
Airflow
Airflow is an open-source Python framework that allows authoring, scheduling and monitoring of complex data sourcing tasks for big data pipelines. Aligned with the DevOps mantra of “Configuration as Code,” it allows developers to orchestrate workflows and programmatically handle execution dependencies such as job retries and alerting. Through the use of Directed Acyclic Graphs (DAGs), developers can customize pipeline processes as needed by using multi-step workflows. They can run part of the workflow at any time, even when tasks are being updated in real time. Besides out-of-the-box integrations with MySQL, Microsoft Server and SaaS platforms, it also provides custom connections to plugins. Robust and flexible, it is free to download for all users.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 or less
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Dataiku

by Dataiku
Dataiku
Dataiku is a powerful data analytics platform designed to empower organizations with data-driven insights and machine learning capabilities. It offers a comprehensive suite of features, including data integration, preparation, and advanced machine learning, all within a user-friendly interface. Dataiku facilitates collaboration among data professionals and business users, streamlining the data analytics process. Its AutoML capabilities simplify machine learning model development, making it accessible to users with varying levels of expertise. Real-time insights and scalability are key benefits, allowing organizations to make timely decisions and adapt to changing data requirements. Despite some learning curve challenges, Dataiku remains a favored choice for medium and large businesses seeking robust data analytics solutions.
User Sentiment User satisfaction level icon: excellent
Cost Breakdown
$1,000 or more
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Qlik Sense

by QLIK TECHNOLOGIES INC.
Qlik Sense
Qlik Sense is a self-service analytics solution that supports enterprise decisions and strategy. Its value lies in interactive dashboards and reports for exploring data independently. Embedded analytics lets your users work freely with data within their applications and websites.Like QlikView, Qlik Sense has an associative engine. It links data tables in memory, keeping the information ready for when your users build models. Plus, its cognitive engine uses natural language for data tasks.The Enterprise subscription includes Talend Data Integration and Qlik Advanced Analytics in Qlik Cloud. You can also choose Qlik Data Integration. Qlik Sense Enterprise SaaS includes 5000 automations and 100 reports. It might be a good fit if your data loads are predictable and don't change much. The vendor charges for the data you move or the volumes you analyze; you can choose between the two.Qlik Sense deploys on Windows, Mac, Linux and any phone device. By contrast, Tableau runs on Windows, Mac and mobile, while Power BI is available only on Windows and Android devices.Individual users can choose the desktop edition, though they’ll need a Qlik Cloud account to download it. A 30-day trial period is available, after which it'll cost $30 per user monthly.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 - $100
Company Size
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Platform
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SageMaker

by Amazon
SageMaker
Amazon SageMaker is a comprehensive machine learning platform by Amazon Web Services (AWS) designed to simplify the entire machine learning lifecycle. It empowers businesses to build, train, deploy, and manage machine learning models efficiently. Key features include robust data preprocessing tools, a wide selection of machine learning algorithms, and automated hyperparameter tuning. SageMaker's scalability ensures it's suitable for both small experiments and large-scale production deployments. It offers cost-efficiency with a pay-as-you-go pricing model and facilitates model management and monitoring. The platform integrates seamlessly with the AWS ecosystem, providing security and compliance features. SageMaker's AutoML capabilities make machine learning accessible to users of varying expertise. Overall, it streamlines the machine learning process, enabling organizations to harness the power of AI for improved decision-making and innovation.
Cost Breakdown
$10 or less
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Platform
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Mashvisor

by Mashvisor
Mashvisor
Mashvisor is a well-known software system that ranks 29 among all Big Data Analytics Tools according to our research analysts and 1 crowd-sourced review from 1 source. Starting from $17.99, Mashvisor is priced more competitively, commonly offers a free trial and is most fitting for small-to-mid-sized companies. Enterprises who have used Mashvisor include Assam Oil, Coca-Cola and VISA. Mashvisor can be deployed online and on-premise and is accessible from a few platforms including Windows and Linux devices. Similar alternatives to Mashvisor include InPhase, DBSync and Oracle Analytics Cloud.
User Sentiment User satisfaction level icon: excellent
Cost Breakdown
$10 - $100
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Deployment
Cloud On-Premise
Platform
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Zepto

by Zepto
Zepto
Zepto is a popular software solution that ranks 30 among all Big Data Analytics Tools according to our research analysts. Starting from $69, Zepto is priced fairly and is most suitable for any sized business. Enterprises who have used Zepto include Analytical Instruments, Capital Alliance Investments and Dynawash. Similar alternatives to Zepto include DataMelt, Statistix and Analyse-it.
Cost Breakdown
$10 - $100
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Platform
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Vertica

by Micro Focus - HM - LB
Vertica
Vertica is an analytics and data exploration platform designed to ingest massive quantities of data, parse it, and then return business insights as reports and interactive graphics. Elastically scalable, it provides batch as well as streaming analytics with massively parallel processing, ANSI-compliant SQL querying and ACID transactions. Deployable in the cloud, on-premise, on Apache Hadoop and as a hybrid model, its resource manager enables concurrent job runs with reduced CPU and memory usage and data compression for storage optimization. A serverless setup and advanced data trawling techniques help users store and access their data with ease.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 or less
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Deployment
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Platform
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QlikView

by QLIK TECHNOLOGIES INC.
QlikView
QlikView is a data discovery and customer insight platform from Qlik, a leader in the insight and intelligence space. However, it is not available for purchase any longer. Qlik Sense, Qlik’s next-generation offering, is available for new customers. It offers self-service data that can help drive decisions and generate significant ROI for technical skill level users. It’s built from the ground up to be affordable, scalable and adaptable. It can ingest data from diverse sources like big data streams, file-based data, and on-premise or cloud data. It is well-known for its data associations and relationship functionality, keeping data in context automatically. It delivers results quickly via its patented in-memory data processing module, processing data down to as little as 10% of its original size. Pros Intuitive interface Fast data visualization Easy data exploration User-friendly for non-technical users Strong community and support Cons Limited data modeling capabilities Licensing costs can be high Customization can be challenging Version control can be a concern Performance can slow with large datasets
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
Company Size
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Deployment
Cloud On-Premise
Platform
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Quantcast

by Quantcast
Quantcast
Quantcast is a popular software tool that ranks 33 among all Big Data Analytics Tools according to our research analysts and 21 crowd-sourced reviews from 1 source. Quantcast is most fitting for any company. Companies who have used Quantcast include Buzzfeed, Kellog’s and Royal Carribean. Quantcast can be deployed online. Similar alternatives to Quantcast include DataLux, Data Sandbox and Alooma.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
Company Size
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Deployment
Cloud On-Premise
Platform
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IBM Watson Analytics

by IBM, America
IBM Watson Analytics
IBM Watson Analytics is a software platform that leverages artificial intelligence and machine learning to empower businesses with data-driven insights for improved decision-making. It caters to businesses of all sizes and across various industries seeking to enhance their competitive edge. Benefits include optimized decision-making, increased productivity, cost reduction, and improved customer satisfaction. Popular features encompass predictive and prescriptive analytics, natural language processing, and data visualization. Pricing is determined on a per-user basis, taking into account the number of users, features utilized, and the chosen subscription plan. User experiences suggest that IBM Watson Analytics offers a user-friendly interface and robust capabilities, making it a valuable tool for businesses seeking to harness the power of data analytics. However, it's recommended to explore alternative options and consider factors such as pricing, features, and user reviews to determine the best fit for your specific business needs.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
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Qubole

by Qubole
Qubole
Qubole is a cloud-based data lake management solution that enables fast data lake adoption for businesses. It allows continuous collaboration by ingesting and processing continuously generated data. Connect to a variety of structured and unstructured data sources and perform ad hoc and streaming analytics, build and test machine learning models and explore data.Explore, build, orchestrate and deliver data pipelines with ease while minimizing cost and maximizing performance. Users can choose a data format best suited to their workflow. It includes a centralized workspace, development tools, an inbuilt notebook environment and extensive integrations to provide end-to-end service with near-zero maintenance.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
Company Size
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Deployment
Cloud On-Premise
Platform
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Essbase

by Oracle America
Essbase
Essbase is a cloud-based enterprise performance management (EPM) solution designed for organizations of all sizes. It helps businesses automate and streamline financial consolidation, budgeting, planning, and forecasting processes. Key benefits include improved data accuracy and consistency, faster time to close, and better decision-making. Popular features include multi-dimensional analysis, workflow management, and integration with other enterprise applications. User reviews highlight its scalability, ease of use, and robust reporting capabilities. Pricing starts at a monthly subscription fee per user, with additional fees depending on features and storage requirements. Pros Scalable Easy to use Robust reporting Fast consolidation Improved data accuracy Cons Limited customization Steep learning curve for advanced features High cost Limited integration with other applications Not ideal for small businesses
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 - $100
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Azure Big Data

by Microsoft Corporation
Azure Big Data
Azure Big Data from Microsoft provides robust analytics and AI services with cloud computing. Build, test, deploy and manage services and applications via public and private cloud data centers managed by Microsoft. It runs both on-premise and through the cloud, using powerful modules and frameworks to create scalable business options. It provides a range of services which include IaaS, PaaS and SaaS.
Cost Breakdown
$100 - $500
Company Size
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Deployment
Cloud On-Premise
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Azure Databricks

by Microsoft
Azure Databricks
Azure Databricks is a unified big data analytics platform that provides data management, machine learning and data science to businesses through integration with Apache Spark. Integrating with a host of data sources, it pulls data from a wide variety of sources, transforms and then analyzes it through visualizations. In addition to setting up ETL flows, it empowers enterprises to create data models for predictive analysis, forecasting and future planning. The vendor offers three workloads based on the stages of analytics workflows — Jobs Compute and Jobs Light Compute for data engineers, and the All-Purpose Compute workload for data scientists.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
Company Size
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Ayasdi

by Ayasdi, Inc.
Ayasdi
Owned by the SymphonyAI Group since 2019, Ayasdi is a machine intelligence platform that leverages statistical calculations and mathematical algorithms to deliver analytics to enterprises. It helps businesses connect the dots by segmenting complex, related datasets into groups through topological data analysis (TDA). Organizations can detect financial risk areas and fraud through automated machine learning workflows, and uncover previously undiscovered patterns and risks.Its sequential data processing and augmented analytics inclusions enable healthcare providers to create consistent patient care strategies. Running on Linux and Hadoop, it can be deployed on premises or in the cloud as a Software-as-a-Service (SaaS).
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
Company Size
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Cloud On-Premise
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Azure Synapse Analytics

by Microsoft
Azure Synapse Analytics
Microsoft Azure Synapse Analytics is a comprehensive cloud-based analytics service designed to empower organizations with robust data warehousing and data analytics capabilities. It seamlessly integrates data warehousing and big data analytics, enabling users to ingest, prepare, manage, and serve data for immediate business insights. Key features include scalable resources, real-time analytics, machine learning integration, and a collaborative workspace for data professionals. Azure Synapse Analytics offers benefits such as improved scalability, enhanced data security, and simplified data integration. It's particularly valuable for organizations looking to harness the power of data to make informed decisions and gain a competitive edge in today's data-driven landscape.
Cost Breakdown
$10 or less
Company Size
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Buyer's Guide

Big Data Analytics Tools is All About New-age Insight 

By Ritinder Kaur, Senior Technical Content Writer at SelectHub

Big Data Analytics Software Buyer's Guide Intro Header

Every organization worth its money invests in big data analytics to monetize its digital assets. With massive volumes of business information lying unused in silos, businesses are aware that they lose out on opportunities to the competition. If this is you, you should look for the best big data tools in the market.

This buyer’s guide provides the information to assist you in your software search, with handy resources and tips to ask the right questions before you decide. 

Executive Summary

  • Big data analytics software has the edge over traditional systems in processing large volumes of information, including unstructured digital assets.
  • Creating a requirements checklist by identifying your implementation goals assists in software selection.
  • Separating requirements into must-have and nice-to-have features helps stay on track with budget and business needs.
  • Data sharing and IoT analytics are some current big data analytics trends.
  • Ask the right questions within your organization and vendors for the tool comparison.
What This Guide Covers:
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What Is Big Data Analytics Software?

Big data analytics solutions are software that enables analyzing complex and varied information to gain meaningful insight that’ll benefit your organization. High volume, velocity and variety of information overwhelm traditional processing and analytics systems, creating the demand for modern big data software.

These tools have an advantage over traditional software by integrating and analyzing unstructured information. In an economy ruled by big data and business analytics, a good piece of software that can process vast amounts of information is priceless. Organizations can't do without big data tools with sources as disparate as social media comments, images, and audio and video feeds.

Mobile insight, connected IoT devices, cloud computing traffic and technologies like artificial intelligence (AI) contribute to the big data boom.

Fortune Business Insights predicts the big data analytics market will grow to $655.53 billion by 2029.

Big Data Analytics Market Chart

Big data analytics is a discipline that falls under the umbrella term business intelligence, with business analytics, embedded analytics and enterprise reporting. Descriptive, predictive and prescriptive analytics are different ways of looking at information for business analytics.

 

Deployment Methods

Would you prefer to deploy on-premise or move to the cloud? Or would a hybrid solution work better, considering your legacy infrastructure?

On-premise

Installing on local infrastructure means you pay once for the cost of ownership. Plus, internet connectivity isn’t a deal-breaker when the software is in-house. You have complete control with full autonomy to plan maintenance, downtimes and upgrades. You can buy more storage when needed.

But setup and maintenance don’t come cheap. Scaling can be a pain, and the cost of technical resources for upgrades and patches adds to your overhead.

Cloud-based

Cloud software vendors offer flexible subscription plans with the option to pick and choose features, like the number of licenses and storage capacity. Managed analysis tools make you worry-free as the vendor handles upgrades and fixes.

Information security is a primary enterprise concern when considering cloud-based software tools. But not to worry, all leading vendors provide encryption and authorization protocols.

But, slow internet can cause latency issues and performance lag. Besides, a periodic subscription might be less than the cost of owning the software, but costs can pile on fast when opting for additional licenses, add-ons and upgrades.

Hybrid

A hybrid solution combines the benefits of information security with the perks of the cloud. Hybrid analytics solutions maintain consistent performance, scaling with larger workloads.

Pay-as-you-go plans are available, though at slightly higher prices than the cost of a public cloud. They prove viable for companies with fluctuating workloads.

Whatever your preferred deployment method, whether the software integrates with your existing software and hardware systems can be a deal-breaker.

So, which model fits you best?

A SaaS analytics solution might be a good fit for small businesses with limited infrastructure and IT resources. Larger enterprises can afford greater control over their information, so they might find on-premise or hybrid solutions appealing.

However, these aren’t set-in-stone rules, so consider your business’ unique situation when choosing a deployment method.

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Primary Benefits

With big data tools, you can pivot with fluctuating market trends by realigning strategies and redesigning campaigns. In the process, you can look closely at your business’ performance to identify inefficiencies and successes.

Let’s see how.

Benefits of Big Data Analytics Software

Maximize Your ROI

Customer feedback and operational performance metrics help downstream inventory, supply chain and workforce management processes. You come closer to identifying what customers want and can realign internal processes accordingly. It saves a lot of heartburn later trying to catch up with the competition.

You can boost revenue by consistently delivering to buyers, which helps establish a reputation as a serious player in the market. Analytics tools enable you to base your business strategy on hardcore metrics, irrespective of their volume and type.

Innovate

Big data analytics drives innovation by providing easy access to hidden insight through self-service methods like natural language processing. Data democratization increases the chances of finding previously undiscovered correlations between metrics.

Big data means more comprehensive information — and harnessing it gives a clearer picture of what’s working and not. This insight into the business’s strengths and weaknesses can help you decide which new products to launch and whether it’s the right time to diversify.

Improve Customer Experience

Customer service calls, chats, surveys, and social media comments and impressions drive the consumer experience. Big data software can capture this information to provide clear and current market insight.

Was the setup process intuitive, and did the customers find the help documentation useful? Were they able to generate the desired reports? For consumer products, the feedback could range from a star-based rating to text-based comments about whether the buyer would recommend the product to others.

Customer feedback is the linchpin around which you arrange your service or product design. It's how you build and retain a customer base and acquire new buyers.

Manage Risk

All business decision-making involves risk, especially when diversifying or growing your business. Risk management identifies and assesses these potential blockers and reduces their adverse impact through mitigation and monitoring. Big data combined with analytics systems can highlight hurdles in your business journey, reducing decision-making uncertainty.

Its applications include vendor risk management, money laundering, fraud prevention, credit risk assessment and identifying customer churn.

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Implementation Goals

Listing what you want the product to achieve will give you the clarity to shortlist suitable software. Here’s a list of common implementation goals to add to yours.

Goal 1

Stay Ahead in the Market
  • You want to outperform the competition.
  • You wish to know if performance aligns with your long-term roadmap.
  • You want to prepare for the future.

Goal 2

Improve Business Performance
  • You want your bottom line to improve.
  • You hope to boost business efficiencies with insight into where you’re underperforming and why.
  • You need access to the latest insight at all times.

Goal 3

Increase Customer Engagement
  • You want to prevent customer churn and gain new buyers.
  • You want accurate customer journey insights to target potential buyers better.

Goal 4

Manage Digital Assets

Poor information management practices cost enterprises thousands of dollars every year.

  • You want to utilize all possible information, including unused assets, in your repositories.
  • You need processing pipelines with automated workflows and the option of autonomous process authoring.
  • You want end-to-end information management with proper disposal of assets when no longer needed.

Goal 5

Secure Business Information
  • You want to enable independent information access, irrespective of technical skills.
  • You don’t want information falling into the wrong hands.
  • You want to give information access to only authorized users.
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Key Features & Functionality

Vendors advertise solutions with many shiny, new features, but do you need them? If your stakeholders approve, sure, go for them. But, if you’re working within strict budget constraints, it helps to focus on the basic features.

Digital Asset Integration

Connect to web and local files, and information warehouses and lakes in the cloud and on-premise. You should have access to near real-time information when you need it.
Dashboarding and Visualization

Create visualizations and personalize them with custom themes, formatting, styles, colors and fonts. Insights are helpful when consumers can understand them, irrespective of their skill levels.
Report Sharing

Share information with your teams and clients with big data reporting tools. Does the tool provide report bursting? Automated refreshes and interactive controls give you a clear view of critical metrics.
Security and Information Governance

Check if the software complies with PCI DSS, HIPAA and ISO 27001. Restrict access by assigning permissions at the column and row levels. Audit usage and access with activity logs and versioning.
Embeddability

Add analytics to user consoles by embedding the software tool into business applications. Personalize the dashboards and reports to give users a familiar interface and establish your brand identity with company logos, themes and colors.
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Advanced Features & Functionality

At some point, the core features mentioned above won’t be enough. Eventually, your business will outgrow its basic software needs and require something more specialized. Or you might want to get advanced features at the onset to prepare for the future.

Even if you’re not planning for them now, it helps to know about the advanced features these tools offer.

Forecasting

Any big data analytics tool worth its name will provide forecasting features. Predictive analytics lets you plan for upcoming opportunities through modeling and regression techniques.

Ask the vendor if the solution supports machine learning and automated recommendations for guided analysis.
Scalability

Your analytics tool should seamlessly connect to a greater number and variety of sources and integrate with newer add-ons. It should scale to take regular vendor updates with minimal downtime and zero impact on end users.
Collaboration

The best big data software should let you collaborate with others on specific dashboards and visualizations through comments and mentions. It’ll help avoid switching between different apps to get the desired metrics.

How much work you can get done as a team within your dashboards will determine the tool’s efficacy.

It’ll reduce the need for many run-of-the-mill reports, saving your team’s time.
Mobility

The best big data analytics software vendors offer mobile insight. Check which specific features they offer, like interactivity, sharing, in-app alerts and push notifications.
Augmented Analytics

Many enterprises want to reduce the time to insight by delegating analytics tasks to respective teams. It reduces the burden on managers and top stakeholders and encourages shared ownership of project goals.

If this is you, augmented analytics can help analyze metrics using machine learning and natural language. These modules might be á la carte and so cost extra. Do check with vendors before you decide.
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Current Trends

Knowing the latest software trends helps fine-tune your requirements checklist and compare products.

Industry-driven bots capture user information and execute tasks, generating job reports and driving processing pipelines. Virtual assistants like Siri and Google Assistant, and social media platforms capture and generate information by the second, producing huge repositories to fuel the analytics market.

Big Data Analytics Trends

IoT Will Raise the Bar for Data Management

Due to the growth in machine learning, AI and IoT analytics, there is an upsurge/increase in the number of connected devices like machine sensors. Statista predicts that IoT-connected devices will grow to 29 billion by 2030.

IoT is in use in all industries like gas, steam, water supply and waste management utility companies, among others. According to Statista (linked above), consumer companies like retail, manufacturing, real estate and healthcare accounted for 60 percent of all IoT-connected devices in 2020.

IoT big data differs from traditional IoT as it needs more agile, scalable platforms, flexible storage and robust analytical tools than conventional infrastructure. Managing this information securely and providing transparency to users and regulatory bodies will be challenging for enterprises going ahead.

Edge Computing and AI Will Drive Security Standards

Edge computing refers to information processing at or near the source, which lightens the workload burden on servers and networks. Edge devices like smartphones, autonomous vehicles and smart grids add to the already ballooning information online.

Edge computing raises logistical challenges like migrating network security protocols and complying with privacy and governance regulations. The secure access service edge (SASE) framework is a network protection protocol for information processed in edge devices. But it's new and needs to become mainstream to have a large-scale impact on information security.

IoT and machine learning are a formidable combination. With edge devices added to the mix, secure data access and governance compliance will be the next challenge for businesses.

Information Sharing Will Fuel Industry Verticals and Consumer Markets

Previous privacy protocols restricted sharing of consumer information with third parties. But, CEOs realize that siloed information benefits only a select few, while shared information helps everyone. Decrypting shared information isn’t even an issue now that processing is possible on encrypted sets.

Data marketplaces monetize information sharing with subscription-based licensing, offering connectivity through the data fabric. They build separate databases with on-demand connectivity for entities willing to store and share their digital assets.

With data sharing promising to be a trend next year and beyond, big data in verticals is a commodity that adds to enterprises’ bottom line. Big data analytics tools with open architectures will be in demand.

Augmented Insights Will Be a Primary Business Requirement

Verified Market Research predicts the augmented analytics industry will be worth $66.61 billion by 2030. The increase in information volumes and complexity drives the need for self-service BI and analytics, which drives augmented tech.

The software sector is the largest consumer of artificial intelligence and machine learning technologies. Modern big data analytics software with machine learning speeds up information discovery through model building, clustering and regression techniques. Automation support is a primary advantage of augmented technologies.

Augmented analytics is a primary requirement for many enterprises when buying software. It’ll be interesting to see how it evolves in tandem with regulatory and data quality concerns.

How To Perform a Software Comparison

Comparing software solutions can be difficult without the right tools and resources. We are here to help.

Start your software selection process with our requirements template, or create your own checklist listing your business needs. Compare vendors and big data analytics solutions with our software comparison report.

Cost & Pricing Considerations

The cost of a software solution can make or break your buying decision. While building your product list, check the deployment cost, especially whether implementation support is available. If not, you should pencil in the cost of technical resources.

Ask about necessary and optional add-ons and which ones are free. Verify whether support is free with the product and what it entails, and check into paid support plans.

Get the pricing details from the vendor’s website or ask for a quote. Our pricing guide helps you determine the best big data software per your budget.

Big Data Analytics Software Report

Expert recommendations and analysis on the top software

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Questions to Ask Yourself

Even with a requirements checklist, weighing the various big data analytics solutions’ pros and cons can be daunting. Having internal conversations and asking questions to identify stakeholder expectations can help.

Use these questions as a starting point:

Big Data Analytics Key Questions To Ask

  • Are you using a big data analytics solution? What are the pain points?
  • What’s the company budget? Is the current software cost-effective?
  • What are your company's present and future goals? How does the analytics tool fit in with them?
  • Who are the end users, and what gaps do you hope this platform fills?
  • What databases should it integrate with, e.g., SQL Server, MySQL, MS Access, etc.?
  • Which deployment method works best for your company?
  • Is self-service a must-have feature, or do you want a system that performs the analysis and offers you recommendations for further action?
  • How important is scalability?
  • What databases should it integrate with, e.g., SQL Server, MySQL, MS-Access, etc.?
  • Will you need a mobile application?
  • Is technical expertise available to deploy and manage the software?

Questions to Ask Vendors

Use these questions as a starting point for conversations with vendors:

About the Software

  • Does the solution provide visualization, data management, reporting and collaboration?
  • Is it scalable? Can it integrate with add-ons and other applications?
  • Does it need customization before deployment?
  • Which mobile functionalities will you get?
  • Can you track KPIs? Is this feature available on mobile?
  • Is the software user-friendly?

About the Vendor

  • How often does the vendor issue updates?
  • Is training included in the purchase plan?
  • Which features cost extra?
  • Do they provide implementation support?
  • Do they offer phone, email and chat support? Is it free or paid?
  • How can you submit a support request? What is the average response time?

In Conclusion

Selecting a big data analytics tool is a task that needs careful thought and lots of research. Do your due diligence in evaluating business requirements and list them by holding in-depth discussions with stakeholders. It should tell you what you want in a big data analytics tool.

Asking the right questions internally and to potential vendors is an excellent way to fine-tune your requirements and compare software products. It will help you narrow down your product shortlist to present to your top-level management for the final decision.

Big Data Analytics Software Report

Expert recommendations and analysis on the top software

Get free access now

Additional Resources

About The Contributors

The following expert team members are responsible for creating, reviewing, and fact checking the accuracy of this content.

Ritinder
By Ritinder Kaur
Technical Content Writer
Ritinder Kaur is a Senior Technical Content Writer at SelectHub and has eight years of experience writing about B2B software and quality assurance. She has a Masters degree in English language and literature and writes about Business Intelligence and Data Science. Her articles on software testing have been published on Stickyminds.
See Full Bio
Sagardeep
Technical Research By Sagardeep Roy
Senior Analyst
Sagardeep is a Senior Research Analyst at SelectHub, specializing in diverse technical categories. His expertise spans Business Intelligence, Analytics, Big Data, ETL, Cybersecurity, artificial intelligence and machine learning, with additional proficiency in EHR and Medical Billing. Holding a Master of Technology in Data Science from Amity University, Noida, and a Bachelor of Technology in Computer Science from West Bengal University of Technology, his experience across technology, healthcare, and market research extends back to 2016. As a certified Data Science and Business Analytics professional, he approaches complex projects with a results-oriented mindset, prioritizing individual excellence and collaborative success.
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Manan
Technical Review By Manan Roy
Principal Analyst
Manan is a native of Tezpur, Assam (India), who currently lives in Kolkata, West Bengal (India). At SelectHub, he works on categories like CRM, HR, PPM, BI, and EHR. He has a Bachelor of Technology in CSE from The Gandhi Institute of Engineering and Technology, a Master of Technology from The Institute of Engineering and Management IT, and an MBA in Finance from St. Xavier's College. He's published two research papers, one in a conference and the other in a journal, during his Master of Technology.
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Hunter
Edited By Hunter Lowe
Content Editor
Hunter Lowe is a Content Editor, Writer and Market Analyst at SelectHub. His team covers categories that range from ERP and business intelligence to transportation and supply chain management. Hunter is an avid reader and Dungeons and Dragons addict who studied English and Creative Writing through college. In his free time, you'll likely find him devising new dungeons for his players to explore, checking out the latest video games, writing his next horror story or running around with his daughter.
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