Categories   >   Big Data Platforms
Last Reviewed: November 11th, 2024

Best Big Data Platforms Of 2024

What are Big Data Platforms?

Big Data Platforms are integrated systems designed to manage and analyze massive, complex datasets. They tackle the challenges of storing, processing, and extracting insights from data exceeding traditional capabilities in volume, variety, and velocity. These platforms offer significant value by enabling data-driven decision making, optimizing operations, and uncovering hidden trends. Key functionalities include data ingestion, storage, processing, analytics, and visualization. Emerging features focus on real-time processing, machine learning integration, and enhanced security. Industries like finance, healthcare, and retail benefit most, leveraging big data for fraud detection, personalized medicine, and targeted marketing. However, limitations include complex infrastructure, skilled workforce requirements, and potential privacy concerns. In essence, Big Data Platforms empower organizations to unlock the true potential of their data, driving innovation and gaining a competitive edge. Their ability to handle the ever-growing data deluge makes them a cornerstone of modern business intelligence.

What Are The Key Benefits of Big Data Platforms?

  • Enhanced decision-making
  • Improved operational efficiency
  • Deeper customer insights
  • Reduced costs and risks
  • Product and service innovation
  • Real-time fraud detection
  • Targeted marketing and sales
  • Personalized customer experiences
  • Optimized resource allocation
Read more

Overall

Based on the latest available data collected by SelectHub for 14 solutions, we determined the following solutions are the best Big Data Platforms overall:

Hadoop
Hadoop
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Why We Picked Hadoop

Hadoop has been making waves in the Big Data Analytics scene, and for good reason. Users rave about its ability to scale like a champ, handling massive datasets that would make other platforms sweat. Its flexibility is another major plus, allowing it to adapt to different data formats and processing needs without breaking a sweat. And let's not forget about reliability – Hadoop is built to keep on chugging even when things get rough. However, it's not all sunshine and rainbows. Some users find Hadoop's complexity a bit daunting, especially if they're new to the Big Data game. The learning curve can be steep, so be prepared to invest some time and effort to get the most out of it.

So, who's the ideal candidate for Hadoop? Companies dealing with mountains of data, that's who. If you're in industries like finance, healthcare, or retail, where data is king, Hadoop can be your secret weapon. It's perfect for tasks like analyzing customer behavior, detecting fraud, or predicting market trends. Just remember, Hadoop is a powerful tool, but it's not a magic wand. You'll need a skilled team to set it up and manage it effectively. But if you're willing to put in the work, Hadoop can help you unlock the true potential of your data.

Pros & Cons

  • Scalability: Hadoop can store and process massive datasets across clusters of commodity hardware, allowing businesses to scale their data infrastructure as needed without significant upfront investments.
  • Cost-Effectiveness: By leveraging open-source software and affordable hardware, Hadoop provides a cost-effective solution for managing large datasets compared to traditional enterprise data warehouse systems.
  • Flexibility: Hadoop's ability to handle various data formats, including structured, semi-structured, and unstructured data, makes it suitable for diverse data analytics tasks.
  • Resilience: Hadoop's distributed architecture ensures fault tolerance. Data is replicated across multiple nodes, preventing data loss in case of hardware failures.
  • Complexity: Hadoop can be challenging to set up and manage, especially for organizations without a dedicated team of experts. Its ecosystem involves numerous components, each requiring configuration and integration.
  • Security Concerns: Hadoop's native security features are limited, often necessitating additional tools and protocols to ensure data protection and compliance with regulations.
  • Performance Bottlenecks: While Hadoop excels at handling large datasets, it may not be the best choice for real-time or low-latency applications due to its batch-oriented architecture.
  • Cost Considerations: Implementing and maintaining a Hadoop infrastructure can be expensive, particularly for smaller organizations or those with limited IT budgets.

Key Features

  • Distributed Computing: Also known as the Hadoop Distributed File System (HDFS), this feature can easily spread computing tasks across multiple nodes, providing faster processing and data redundancy in the event that there’s a critical failure. Hadoop is the industry standard for big data analytics. 
  • Fault Tolerance: Data is replicated across nodes, so even in the event of one node failing, the data is left intact and retrievable. 
  • Scalability: The app is able to run on less robust hardware or scale up to industrial data processing servers with ease. 
  • Integration With Existing Systems: Because Hadoop is so central to so many big data analytics applications, it integrates easily into a number of commercial platforms like Google Analytics and Oracle Big Data SQL or with other Apache software like YARN and MapR. 
  • In-Memory Processing: Hadoop, in conjunction with Apache Spark, is able to quickly parse and process large quantities of data by storing it in-memory. 
  • Hadoop MapR: MapR is a component of Hadoop that combines a number of features like redundancy, POSIX compliance and more into a single, enterprise grade component that looks like a standard file server. 
Domo
Domo
Start Price
$15
Per User, Monthly
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked Domo

Domo has everything data teams could wish for — self-service ETL, advanced analytics and data science. Its rich set of connectors makes users happy as they praise its robust integration APIs. Its scripting language is similar to Power BI, and knowing SQL will shorten your team's learning curve. The vendor offers daily refreshes, currently capping them at 48.

On the flip side, the interface seemed a bit clunky to me. Dashboards don’t display in the edit mode by default, which was a tad annoying. The Getting Started documentation is dated and doesn’t match the new interface. I could find my way around with help from user forums.

While the vendor earns praise for releasing frequent updates, quite a few users say some much-needed features lack depth. According to our research, Domo offers only 64% of the required functionality out of the box, which is much less than what Power BI and Tableau provide. It also underperforms in data querying, scoring only 53 in our analysis.

Some reviews mention bugs and that performance can lag when handling anything complex than simple visualizations. The slowness could be due to the multitenant SaaS model that provides shared computing. As for the mobile app, it didn’t work in the offline mode for me. I should mention here that I had opted for the trial version. A proof-of-concept will help you check if the issue persists in the paid edition.

Domo’s pay-as-you-go model is great for estimating usage but be prepared to pay more for workload spikes. According to our research, about 89% of users who reviewed the price found Domo’s consumption model expensive. Small organizations working with a lean team might find it challenging to handle billing.

Here’s what’s great about subscribing to Domo. You can create as many reports and dashboards as required — there’s no limit or additional cost. Plus, Domo allows adding an unlimited number of users. Domo accepts external data models from OpenAI, Amazon Bedrock, Hugging Face, Databricks and Jupyter Workspaces.

Despite a competitive market, Domo is an excellent product for organizations seeking data visualization and strong integration. Its flexible pricing model and recent AI updates make it a strong challenger to leading data platforms.

Pros & Cons

  • Source Connectivity: About 86% of users citing data integration said they could connect to their preferred sources easily.
  • Ease of Use: Around 82% of users discussing the interface said options and tabs were straightforward and intuitive.
  • Data Visualization: About 74% of people who reviewed Domo for graphics appreciated the ease of creating and sharing data stories.
  • Functionality: Around 73% of users who mentioned features said Domo performed as expected.
  • Support Services: About 71% of reviews discussing assistance praised the support team for being helpful and responsive.
  • Speed: About 78% of users discussing speed said the platform lagged sometimes.
  • Cost: Around 89% of users discussing price termed the platform as expensive.

Key Features

  • Domo App Studio: Design custom apps for needs Domo might not address out of the box. Build your own functionality without coding knowledge. Create branded visualizations with your trademark colors, logos and fonts. ESPN enhances the fan experience by capturing and analyzing customer data using a Domo Studio app.
  • Analyzer: Save time spent cleaning data manually. Use a special editor to set up checks for data inputs. Keep tabs on which charts and dataflows use a specific dataset with the lineage option. You can choose the best chart to present your data and annotate it. Use the Beast Mode for complex math.
  • DomoGPT: Get answers to data queries using AI Chat (currently in beta). Convert text to SQL or calculations and understand your data using text summaries. Use Domo.AI in a safe, governed space.
  • Personalized Data Permissions: Create custom data views for your users and hide sensitive data. Your regional managers get exclusive views specific to their roles, while senior management can switch between full and filtered views.
  • Domo Mobile: View cards and text summaries on the mobile app. Cards fit within the small screen, giving a great user experience. Domo Buzz allows sharing files to WhatsApp, Gmail, QuickShare and Google Drive. You can even save a screenshot to your phone gallery.
  • Alerts: Stay informed about KPIs that matter to you. Set new alerts and share them with selected users or subscribe to existing ones. Choose where you want to receive the notifications — email, mobile app or SMS.
Cloudera
Cloudera
Start Price
$833
Per User, Annually
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked Cloudera

Is Cloudera the answer to your data management woes, or is it just a bunch of hot air?

User reviews from the past year paint a mixed picture of Cloudera. While some users praise its flexibility and ability to handle large datasets, others find it cumbersome and expensive. Cloudera's hybrid cloud approach, allowing users to deploy on-premises or in the cloud, is a major selling point for many. However, some users find the platform's complexity a barrier to entry, especially for those without extensive experience in data management. Cloudera's integration with other tools, such as Apache Hadoop, is a key differentiator, but some users report issues with compatibility and performance.

Cloudera is best suited for large enterprises with complex data needs and a dedicated team of data engineers. Its robust features and scalability make it a powerful tool for organizations that require a comprehensive data management solution. However, smaller businesses or those with limited technical resources may find Cloudera's complexity and cost prohibitive.

Pros & Cons

  • Scalability: Cloudera can handle massive datasets and complex queries, making it suitable for large-scale data analysis and reporting.
  • Security: Cloudera offers robust security features, including data encryption and access control, ensuring sensitive data is protected.
  • Performance: Cloudera's optimized architecture and distributed processing capabilities deliver fast query execution and efficient data processing.
  • Integration: Cloudera integrates seamlessly with various data sources and tools, enabling users to connect and analyze data from different systems.
  • Community Support: Cloudera has a large and active community, providing access to resources, support, and best practices.
  • Steep Learning Curve: New users often find Cloudera's interface and complex architecture challenging to navigate, requiring significant time and effort to master. This can be especially problematic for teams with limited technical expertise.
  • Costly Implementation: Cloudera's pricing model can be expensive, particularly for large deployments. The cost of hardware, software licenses, and ongoing support can be a significant barrier for some organizations.
  • Limited Scalability: While Cloudera offers scalability, some users have reported challenges scaling their deployments to meet rapidly growing data volumes. This can lead to performance bottlenecks and slow query execution times.
  • Complex Management: Managing a Cloudera cluster can be complex, requiring specialized skills and knowledge. This can be a burden for organizations with limited IT resources.

Key Features

  • Data Science Workbench: Through a unified workflow, collaboratively experiment with data, share research between teams and get straight to production without having to recode. Create and deploy custom machine learning models and reproduce them confidently and consistently.
  • Real-Time Streaming Analytics: With edge-to-enterprise governance, Cloudera DataFlow continuously ingests, prioritizes and analyzes data for actionable insights in real-time. Develop workflows to move data from on-premises to the cloud or vice-versa, and monitor edge applications and streaming sources.
  • Machine Learning: Enable enterprise data science in the cloud with self-service access to governed data. Deploys machine learning workspaces with adjustable auto-suspending resource consumption guardrails that can provide end-to-end machine learning tools in one cohesive environment.
  • Data Warehouse: Merges data from unstructured, structured and edge sources. The auto-scaling data warehouse returns queries almost instantly and has an optimized infrastructure that moves workloads across platforms to prepare vast amounts of data for analysis.
  • Operational Database: The operational database promises both high concurrency and low latency, processing large loads of data simultaneously without delay. It can extract real-time insights and enable scalable data-driven applications. 
  • Open-Source Platform: Access the Apache-based source code for the program and make adjustments, customizations and updates as desired. 
  • Data Security and Governance: Reduce risk by setting data security and governance policies. The Cloudera Shared Data Experience (SDX) then automatically enforces these protocols across the entire platform, ensuring sensitive information consistently remains secure without disruption to business processes.
  • Hybrid Deployment: Leverage the deployment flexibility and accessibility to work on data wherever it lives. Read and write directly to cloud or on-premises storage environments. With a hybrid cloud-based architecture, choose between a PaaS offering or opt for more control via IaaS, private cloud, multi-cloud or on-premises deployment.
Redshift
Redshift
Start Price
$0
Company Size
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Deployment
Cloud On-Premise
Platform
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Key Features

  • Massively Parallel Processing: Parallelize and distribute SQL (Structured Query Language) operations. Use local attached storage to maximize throughput between CPUs and drives.
  • End-To-End Encryption: Utilize SSL encryption to secure data in transit and AES-256 encryption for data at rest. Encrypt all data written to disk and backup files. Migrate data between encrypted and unencrypted clusters and make use of highly customizable and robust data security options. 
  • Cost-Effective: Enjoy on-demand pricing with no upfront costs, no commitments and no hidden charges. Pay only for the resources used. Use the pause and resume feature to suspend on-demand billing when the cluster isn’t going and pay only for backup storage.
  • Scalable: Change the number or type of nodes with a few clicks from the console or an API call. Share live data across clusters, improving the agility of the company. Use S3 as a highly available, secure and cost-effective data lake, storing unlimited data in open formats.
  • Advanced Query Accelerator: Enable the platform to run ten times faster by automatically boosting certain types of queries.
  • Currency Scaling: Support thousands of users and queries simultaneously, enabling superior performance. Process queries without delays and assign clusters to particular groups and workloads while controlling the number.
  • Data Sharing: Enable instant, granular and high-performance data access across clusters without moving or copying data. See information as it’s updated in the data warehouse via live access, at no additional cost. Provide a secure and organized collaboration within and across companies.
  • Concurrency Limits: Attain flexibility while maintaining concurrency limits similar to other data warehouses. Configure limits based on regions instead of applying a single limit to all users.
  • Fault Tolerance: Enhance the reliability of the data warehouse cluster — automatically re-replicate data and shift data to healthy nodes on the failure of drives, nodes and clusters.
  • Column-Oriented Databases: Allow quick access to large data amounts and accomplish massive data processing tasks in an instant.
  • Automatic Workload Management: Easily segregate a workload and assign a part of cluster resources to every workload.
Hortonworks
Hortonworks
Company Size
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Deployment
Cloud On-Premise
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Key Features

  • Multi-Workload Processing: The product is able to handle multiple workloads and other taxing processes such as detailed analysis and report generation — all in parallel processes. 
  • Real-Time Processing: Users can take advantage of processing in real time, without having to wait for their data to finish compiling. 
  • Batch Processing: Batch processing is the processing of large quantities of data in large batches, significantly cutting down the time it takes to process information. 
  • Data Governance: Controlling, managing and distributing data are essential to a modern analytics solution. The software provides a suite of management features for users to take advantage of.  
  • Dataflow: Dataflow is an all-in-one data crunching feature that streams data and insights in real-time. It delivers actionable intelligence and curated data as it’s being processed. 
Talend
Talend
Start Price
$4,800
Annually
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked Talend

Users praise Talend's visual drag-and-drop builder as a major draw, especially when compared to code-heavy platforms like Informatica. "Talend's UI feels much more intuitive and beginner-friendly," one reviewer shared, making it easier to onboard non-technical colleagues. However, its steeper learning curve compared to simpler tools like Snaplogic was also noted, demanding more training and initial investment.Talend's open-source roots and wide range of connectors were lauded, especially for cost-conscious businesses. One user compared it to IBM DataStage, noting "Talend's open-source version offers surprisingly robust functionality, making it a viable alternative for smaller budgets." However, limited native profiling compared to powerful tools like Alteryx was mentioned as a drawback, forcing integration with separate solutions and potentially raising costs.Users appreciated Talend's scalability and cloud-readiness, particularly when contrasted with legacy ETL offerings. "Talend scales seamlessly as our data needs grow," said one reviewer, making it a future-proof investment. However, some found enterprise features locked behind paid editions, like advanced lineage tracking and data quality monitoring, a disadvantage compared to competitors like Boomi that offer these capabilities across the board.Overall, Talend's balance of affordability, user-friendliness, and scalability shines through in user reviews. While it may require deeper initial investment in training and lack certain advanced features out-of-the-box, its flexibility and adaptability make it a strong contender for businesses navigating the evolving data landscape. As one user summed it up, "Talend might not be perfect, but it's a powerful tool that grows with you, and that's what matters most."

Pros & Cons

  • Simplified Lineage Tracking: Visually map data flows and transformations for enhanced auditability and compliance, ensuring your data journey is clear and transparent.
  • Centralized Policy Management: Define and enforce data quality, access, and security rules across the organization, fostering a consistent and controlled data environment.
  • Automated Data Catalog: Discover, document, and govern all data assets for improved data visibility and utilization, empowering your teams to find and leverage the right data.
  • Streamlined Data Masking & Anonymization: Protect sensitive data during development and testing while preserving data utility, safeguarding privacy and complying with regulations.
  • Scalable & Collaborative:** Manage data governance across diverse data sources and teams with ease, adapting to your evolving data landscape and fostering collaboration.
  • Limited Native Profiling: Lacks built-in tools for advanced data profiling and quality monitoring, requiring additional integrations or third-party tools, increasing complexity and potentially costs.
  • Steep Learning Curve: Complex UI and configuration can be challenging for beginners, especially non-technical users, requiring significant training and investment in onboarding and ongoing support.
  • Customization Challenges: Extensive customization options, while offering flexibility, can lead to complexity and maintenance overhead, particularly for large deployments or intricate data governance requirements.
  • Limited Community Support: While the Talend community is active, it may not be as extensive as other data governance solutions, potentially impacting troubleshooting and knowledge sharing, especially for niche issues or advanced configurations.
  • Enterprise Features in Paid Editions: Key data governance features like advanced lineage tracking, data masking, and data quality monitoring often require purchasing higher-tiered paid editions, increasing costs and potentially limiting accessibility for organizations with budget constraints.

Key Features

  • Pipeline Designer: Pull in data to create dashboards that power business insights. Build reusable pipelines to integrate data from any source, then transform it to upload to data warehouses. 
    • DIY Data Workflows: Design and preview data workflows directly in the web browser without IT help. Run pipelines directly where the data lives, with support for disparate filesystems, business apps, databases, data warehouses and messaging services. Automate data stewardship, preparation and cataloging into data pipelines. 
  • Data Inventory: Prepare, consume and share data from a single, centralized hub. Make informed decisions by putting unique, accurate data at the center of business. 
    • View data trustworthiness at a glance with the Trust Score. Aggregates multiple metrics into a single, easy-to-understand score, scaling from 0 to 5. 
    • Flags data silos across sources and resolves the gap with reusable and shareable data assets. 
    • Make data work for you by sharing assets between the data preparation and designer modules. 
  • Talend Open Studio: Handles big data by leveraging Hadoop and its databases, or HBase, HCatalog, HDFS and Hive. Connects to Oracle, Teradata, Microsoft SQL Server, Marketo, Salesforce, Netsuite, SAP, Sugar CRM, Microsoft Dynamics, SMTP, FTP/SFTP, LDAP and more. Provides an Eclipse-based integrated environment (IDE) for system development. 
  • Talend Sandbox: The vendor provides a sandbox environment for data warehouse optimization, clickstream analytics, social media sentiment analysis and Apache weblog analytics. 
SAP HANA
SAP HANA
Start Price
$972
Per Capacity Unit, Usage-Based
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Why We Picked SAP HANA

SAP HANA is a multi-model database and analytics platform that combines real-time transactional data with predictive analytics and machine learning capabilities to drive business decisions quicker. Most of the users who mentioned analytics said that, with its Online Analytical Processing(OLAP) and Online Transactional Processing(OLTP) capabilities, the tool analyzes data faster with predictive modeling and machine learning. Many users who reviewed data processing said that the tool has a lean data model due to its in-memory architecture and columnar storage capabilities, and, paired with its compression algorithm, can perform calculations on-the-fly on huge volumes of data. In reference to data integration, many users said that the platform connects seamlessly with both SAP and non-SAP systems, such as mapping tools like ArcGIS, to migrate data to a consolidated repository, though quite a few users said that integration with media files and Google APIs is tedious. Most of the users who reviewed support said that they are responsive, and online user communities and documentation help in resolving issues, whereas some users said that the support reps had limited knowledge. A majority of the users who reviewed its speed said that the platform has a fast runtime, though some users said that it requires high-performing hardware infrastructure to do so and that memory management might be tricky with large datasets.
The software does have its limitations though. Being in-memory, the tool is RAM-intensive, which can add to the cost of ownership, though some users said that data compression reduces the database size and saves on hardware cost. A majority of the users who reviewed its functionality said that it needs to be more mature in terms of flexibility and agility, though some users said that with easy updates and maintenance, it is a robust solution and increases efficiency and productivity.
In summary, SAP HANA serves as a single source of truth for analysis of large volumes of data and uncovering consumer insights through planning, forecasting and drill-down reporting. However, it seems more suited for large organizations with complex data types and analytics workflows because of its costly pricing plans.

Pros & Cons

  • Data Analysis: Around 92% of users who reviewed data analysis said that the tool analyzes and displays insights and trend forecasts of transactional data in real time to enable timely decision-making.
  • Data Processing: Approximately 91% of the users who discussed data processing said that the tool can query large amounts of data due to its in-memory architecture and data compression algorithm.
  • Data Integration: Around 87% of the users said that the solution migrates data efficiently from a wide range of SAP and non-SAP systems.
  • Support: Approximately 87% of the users who discussed support said that they are responsive, and online user communities and knowledge bases assist in faster resolution of issues.
  • Speed and Performance: Citing the tool’s fast runtime, around 76% of the users said that they can perform on-the-fly calculations at very high speeds.
  • Pricing: Approximately 80% of the users who mentioned pricing said that the solution’s in-memory architecture demands large amounts of RAM, which adds to the cost.
  • Functionality: According to around 53% of the users who reviewed functionality, the solution needs to be more flexible and agile to perform complex calculations on large datasets.

Key Features

  • Data Integration: Captures any type of data — structured or unstructured — from database transactions, applications and streaming data sources. Ingests part of a data set or the complete data set into its native architecture for ready access. 
  • Capture and Replay: Record complex database transactions and then replay them on another device. Test on a non-production system while using production transactions, on a hosted instance, or in the cloud. 
  • Graph Data Processing: Combines its built-in graph engine with the in-memory relational database. Makes graph processing of relational tables easy to learn and use. 
  • SAP HANA Cockpit: Configure and manage HANA instances and applications through a single console interface. Easily schedule all backup jobs and monitor the system for immediate visibility of potential blockers. Integrate with other applications for workload management and security. 
  • Flexible Querying: Choose from a variety of semantics structures to query data in the database memory through a flexible algorithm. 
  • In-memory Architecture: Analyze insights in real time to monitor business KPIs and generate forecast trends. Access data quicker than with conventional databases via its in-memory database. 
  • Data Compression: Compresses data by up to 11 times and stores it in columnar structures for high-performance read and write operations. Saves storage space and provides faster data access for search and complex calculations. 
  • Parallel Processing: Performs basic calculations, such as joins, scans and aggregations in parallel, leveraging column-based data structures. Processes data quicker for distributed systems. 
  • Real-time Analysis: Queries transactional data directly as it is added in real-time. Leverages its inbuilt data processing algorithm to read and write data to a column storage table at high speeds. Acquire total visibility over information while it is being analyzed and make on-the-spot, incisive decisions. 
  • Role-based Permissions: Maintain data integrity across the organization — assign data access based on each team member’s role. 
Vertica
Vertica
Start Price
$3.19
Per Hour, Usage-Based
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Why We Picked Vertica

Vertica Analytics is a big data relational database that provides batch as well as streaming analytics to enterprises. Citing a robust, distributed architecture with massively parallel processing (MPP), all users who review data processing say that it performs extremely fast computing with I/O optimization, and columnar storage makes it ideal for reporting. Approximately 72% of the users who review performance say that it is a reliable tool with high availability and virtually no downtime, with K-safety protocol in place for efficient fault tolerance. Citing its feature set, around 56% of the users say that they are satisfied with its elastic scalability, rich analytical functions and excellent clustering technology.
On the flip side, almost 50% of the users who mention technical and community support say that it is inadequate and possibly contributes to the platform’s steep learning curve. All users who review its cost say that the solution is expensive, with restrictive data storage limits.
In summary, Vertica is a big data and analytics platform that provides streaming analytics with lightning-fast query speeds, machine learning and forecast capabilities.

Pros & Cons

  • Data Processing: All users who mention computing say that the tool’s columnar storage and parallel processing enable faster querying.
  • Performance: Almost 72% of the users who review performance say the platform is robust and reliable with high availability.
  • Functionality: Around 56% of the users who review functionality say that it is feature-rich and performs as expected.
  • Cost: All users who mention cost say that data storage limits can be restrictive and the tool is expensive.
  • Community Support: Citing lack of technical community support, approximately 50% of the users say that it makes adoption difficult.

Key Features

  • Streaming Analytics: Connects to Apache Kafka for IoT data analysis in real time. Analyzes and manages large volumes of data from IoT devices such as machine and sensor data for buildings, vehicles, medical systems, smart devices and wearables. 
  • Machine Learning: Get automated insights and deliverables through machine learning modules that automatically digest and parse large data portions. ML modules are built into its core — no need to pay for them or install them separately. 
  • Software Only: Work with a robust software interface with dedicated IT resources. All data warehousing, storage and processing infrastructure is hosted offsite. 
  • Fast SQL Databases: Store and retrieve data through highly scalable and speedy SQL databases. 
  • Massively Parallel Processing: Get increased speed and scalability at larger scales by running two processes side-by-side through massively parallel processing. 
  • Columnar Storage: Read only the most important sets of data first through columnar storage that greatly speeds up data retrieval. 
AgilOne
AgilOne
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
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Key Features

  • Machine Learning: Machine learning modules can take customer data and automatically parse it, clean it, analyze it, and report on it. It takes a multitude of defined events — subscriptions, purchases, engagement — and automatically learns from them in order to optimize service. 
  • Self-Service: Marketers and data scientists alike can easily operate this software, and utilize different facets of it. Marketers can utilize dashboards, reports and templates, whereas data scientists can use AgilOne Lightning IQ to directly interact with data to perform sophisticated operations. 
  • Built-In Calculations: Users can save time by utilizing AgilOne’s built-in functions and calculations. Calculations like lifetime value, acquisition cost, retention value and more give decisionmakers deeper insights into their customers. 
  • Integrations: It offers robust integrations with alternative data programs such as ZenDesk, Salesforce, Oracle Service Cloud, SAP, Google and more. These diverse integrations allow for maximum digestion of information. 
  • Enhanced Communication and Marketing: Combining outbound marketing tools and big data enhancements, it is able to greatly enhance SMS, email, push notifications and even direct mail with rich, personalized customer data. 
  • Enriched CRMs: Data can be exported and integrated into most CRM systems, enriching customer profiles. 
Actian
Actian
Start Price
$500
Monthly
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked Actian

Actian, a Big Data Storage Solutions software, has garnered mixed reviews in the past year. Users praise its scalability, flexibility, and cost-effectiveness. They appreciate its ability to handle large datasets and its compatibility with various data sources. However, some users have reported performance issues, particularly with complex queries, and have expressed concerns about its documentation and technical support.

Compared to competitors like Cloudera and Hortonworks, Actian is seen as a more affordable and user-friendly option. Its intuitive interface and pre-built templates make it accessible to users with varying technical expertise. However, Cloudera and Hortonworks offer more comprehensive features and support for advanced analytics, making them better suited for large-scale, data-intensive applications.

Actian is an ideal choice for organizations looking for a cost-effective and easy-to-use Big Data Storage solution. Its scalability and flexibility make it suitable for businesses of all sizes. However, organizations with complex data analysis requirements or those seeking advanced analytics capabilities may need to consider more feature-rich alternatives.

Pros & Cons

  • Scalability and Performance: Actian's distributed architecture enables users to easily scale their data storage and processing capabilities to meet growing business demands, delivering high performance and low latency for complex analytics and data-intensive workloads.
  • Flexibility and Extensibility: Actian provides a flexible and extensible platform that allows users to integrate with a wide range of data sources, tools, and applications, making it easy to customize and adapt to specific business needs.
  • Security and Compliance: Actian offers robust security features such as encryption, access control, and audit trails, ensuring the protection of sensitive data and compliance with industry regulations.
  • Cost-Effectiveness: Actian's subscription-based pricing model and optimized resource utilization help businesses reduce their IT costs while still accessing powerful data storage and analytics capabilities.
  • Technical Support: Actian provides comprehensive technical support to assist users with installation, configuration, and ongoing maintenance, ensuring a smooth and efficient experience.
  • Difficult to Use Interface: Many users have complained that the interface for Actian is not intuitive and can be difficult to navigate, especially for those who are not familiar with Big Data Storage Solutions.
  • Limited Scalability: Actian has been criticized for its limited scalability, which can be a major issue for businesses that need to handle large amounts of data.
  • Lack of Support: Users have also reported that Actian's customer support is lacking, which can be a major inconvenience when troubleshooting issues.
  • High Cost: Actian is often more expensive than other Big Data Storage Solutions, which can be a deterrent for businesses that are on a budget.
  • Frequent Bugs: Users have also complained about frequent bugs and glitches in Actian, which can lead to data loss or corruption.

Key Features

  • Data Warehouse: Avalanche, its data warehouse service, deploys on-premises and in the cloud, including AWS, Azure and Google Cloud, enabling self-paced migration of enterprise applications and data. 
    • Scalability: Scales seamlessly across data volumes, query complexity and concurrent users. Continues to perform analytical queries while the database is updated without a drop in performance, allowing up to 64 concurrent users out-of-the-box. 
    • Columnar Database: Scales out to multiple nodes and petabytes of data through massively parallel processing (MPP). Its underlying database engine, Vector processes hundreds of tuples of data by leveraging SIMD support in x86 CPUs. Speeds up performance and reduces data footprint by compressing data up to 4-6 times. 
    • Edge Computing: Query data distributed across multiple sources in one go with federated queries and get results in the query source itself to lower costs and reduce time to insight. 
  • Integrations: Connect to any data source across on-premise, in the cloud, and hybrid environments through its UniversalConnect technology. Rapidly connects two applications and expedites mapping and data transformations through PointConnect. 
  • Zen Embedded Database: Provides reliable data across enterprise applications, onsite as well as remote, including IoT applications. Move data seamlessly between any operating system, other versions or PostgreSQL database products — with no ETL overhead. Enables edge computing by deploying with minimal effort across Windows, Linux, Mac OS, iOS and Android devices. 
    • NoSQL Object Database: In addition to SQL access for reporting, query and transactions, it offers NoSQL access for data-intensive application performance and local analytics support. 
    • Architecture: The Zen database family is built on a single, modular architecture that scales seamlessly from single-user client to enterprise-grade servers. Add-on packages for auditing, replications and multi-instance synchronization further support various office networks, including remote workers and IoT and mobile devices. 
  • Operational Analytics: Provides robust online transaction processing through a high-performing analytics engine coupled with a stable enterprise RDBMS. Integrates with Ingres to provide an end-to-end solution for designing and implementing integrations. 
  • DataCloud Backup: Automatically transfer backup data resulting from Ingres/Actian X database checkpoints and journals to the cloud. Scales according to backup requirements and handles increasing workloads through load balancing and automatic provisioning of servers. 

COMPARE THE BEST Big Data Platforms

Select up to 2 Products from the list below to compare

 
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SelectHub’s Analyst Rating is based on data from our 400+ point analysis of Big Data Platforms, 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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Platform
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Hadoop
Undisclosed
No
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Hadoop
Domo
$15
Per User, Monthly
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Domo
Cloudera
$833
Per User, Annually
Yes
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Mac Windows Linux Chromebook Android
Cloudera
Redshift
$0
No
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Cloud On-Premise
Mac Windows Linux Chromebook Android
Redshift
Hortonworks
Still gathering data
No
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Cloud On-Premise
Mac Windows Linux Chromebook Android
Hortonworks
Talend
$4,800
Annually
Yes
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Talend
SAP HANA
$972
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Yes
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SAP HANA
Vertica
$3.19
Per Hour, Usage-Based
Yes
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Cloud On-Premise
Mac Windows Linux Chromebook Android
Vertica
AgilOne
Still gathering data
No
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Cloud On-Premise
Mac Windows Linux Chromebook Android
AgilOne
Actian
$500
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Yes
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Actian

All Big Data Platforms (14 found)

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Hadoop

by Apache Software Foundation
Hadoop
Apache Hadoop is an open source framework for dealing with large quantities of data. It’s considered a landmark group of products in the business intelligence and data analytics space, and is comprised of several different components. It functions on basic analytics principles like distributed computing, large data processing, machine learning and more. Hadoop is part of a growing family of free, open source software (FOSS) projects from the Apache Foundation, and works well in conjunction with other third-party products.
User Sentiment User satisfaction level icon: great
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$100 - $500
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Domo

by Domo
Domo
Domo is a cloud-based analytics platform that integrates end-to-end data management into one solution. Being SaaS, it’s available from anywhere with an internet connection. The vendor offers the best of both worlds — self-serve ease of use and data science.Domo has a friendly interface aimed at senior management who are hard-pressed to make tough decisions daily. A breadcrumb trail at the top of the workspace will help you navigate between folders. A performant, scalable warehouse supports fast queries with in-memory data.Domo Buzz is an instant messaging option like Slack with file sharing and is available on the mobile app also. Annotation options allowed me to add comments to my chart and mark data points of interest. If you want something more than what it offers, you can build your own apps within Domo. It’s our analysts’ pick and a user favorite in its category for these and more features.Domo Everywhere is the embedded version, though it doesn’t offer as many options to design views as some other platforms, such as Dundas BI.You can use Domo dashboards and reports for several critical tasks. Decide where to reduce spending and identify the factors that affect your business. Forecast demand for your services and products. Predict how unexpected events can impact the economy and your business and do much more.There’s a 30-day free trial after which you can upgrade to the Standard or Enterprise pricing model. Or opt for the Business Critical edition to get a private AWS link that promises watertight security and reduces latency.Some users mention performance limitations, which could be caused by shared cloud resources. The vendor offers a consumption model — pay for what you use and add unlimited users at a flat fee of $750.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 - $100
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Cloudera

by Cloudera, Inc.
Cloudera
Cloudera is a multi-environment analytics platform powered by integrated open source technologies that help users glean actionable business insights from their data, wherever it lives. With an enterprise data cloud, it puts data management at analysts’ fingertips, with the scalability and elasticity to manage any workload. It offers users transparency into the whole data lifecycle and the flexibility of customization through its open architecture. It is available on an annual subscription basis with three offerings: CDP Data Center, Enterprise Data Hub and HDP Enterprise Plus. Each edition offers different components and pricing varies based on computing power, storage space and number of nodes. The company merged with Hortonworks in 2019 to provide a comprehensive, end-to-end hybrid and multi-cloud offering.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$500 - $1,000
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Redshift

by Amazon Web Services, Inc.
Redshift
Amazon Redshift is a cloud-based data warehouse service that enables enterprise-level querying for reporting and analytics. It supports an unlimited number of concurrent queries and users through its high-performing Advanced Query Accelerator (AQUA). Scalable as needed, it retrieves information faster through massive parallel processing, columnar storage, compression and replication. Data analysts and developers leverage its machine learning attributes to create, train and deploy Amazon Sagemaker models.Enterprises can glean actionable insights through its integration with the AWS ecosystem. Data sharing is enabled across all its clusters, eliminating the need to copy or move data. It offers data security through SSL and AES-256 encryption with granular access controls to ensure that users have access to only the information that they need.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 or less
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Hortonworks

by Hortonworks Inc.
Hortonworks
Hortonworks Data Platform is an open-source data analysis and collection product from Hortonworks. It is designed to meet the needs of small, medium and large enterprises that are trying to take advantage of big data. The company was acquired by Cloudera in 2019 for $5.2 billion. HDP has a number of features that help it process large enterprise-level volumes, including multi-workload processing, batch processing, real-time processing, governance and more.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
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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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SAP HANA

by SAP, Inc.
SAP HANA
SAP HANA is the in-memory database for SAP’s Business Technology platform with strong data processing and analytics capabilities that reduce data redundancy and data footprint, while optimizing hardware and IT operational needs to support business in real time. Available on-premise, in the cloud and as a hybrid solution, it performs advanced analytics on live transactional data to display actionable information. With an in-memory architecture and lean data model that helps businesses access data at the speed of thought, it serves as a single source of all relevant data. It integrates with a multitude of systems and databases, including geo-spatial mapping tools, to give businesses the insights to make KPI-focused decisions.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$500 - $1,000
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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
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$10 or less
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AgilOne

by AgilOne
AgilOne
AgilOne is a data exploration and analysis product that is built from the ground up to handle vast quantities of data from a variety of sources. Utilizing machine learning, self-service BI, built-in calculations and more, it’s able to parse, prepare and analyze massive amounts of information. It’s ideal for small to medium-sized businesses.
Cost Breakdown
$100 - $500
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Actian

by Actian Corporation
Actian
Actian is a cloud data management platform that enables data integration with fully managed warehousing, transformation and analytics for enterprises. It integrates with various cloud-based and on-premise technologies and services. With massively parallel processing and data compression on the back end, its embedded analytical engine works in tandem with a robust RDBMS, complementing its computing with built-in user-defined functions to process online transactions at scale.Part of the Cloud Security Alliance, the vendor consistently updates their best practices to ensure the security of its cloud services. It offers a 30-day free trial. Pricing is available on request.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
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1010data

by 1010data, Inc.
1010data
1010data is a market intelligence and enterprise analytics solution that helps track consumer insights and market trends. In addition to vendor-critical insights, it provides brand performance metrics to buy-side entities. Seamlessly embeddable, it can also function as a standalone private-label option. Data scientists and statisticians leverage its integration with R to view and query data tables.It enables analytics development through its QuickApps framework. By tracking consumer spending trends and brand performance, it enables businesses to better position their products in the marketplace.
User Sentiment User satisfaction level icon: good
Cost Breakdown
$1,000 or more
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TrendMiner

by TrendMiner
TrendMiner
TrendMiner is an advanced analytics software designed to optimize manufacturing processes through data-driven insights. It excels in real-time process monitoring, root cause analysis, and predictive maintenance, making it particularly suitable for industries such as chemicals, pharmaceuticals, and oil & gas. The software's standout features include its intuitive search capabilities, pattern recognition, and self-service analytics, which empower users to make informed decisions without needing extensive data science expertise. TrendMiner's unique benefits include its ability to quickly identify process inefficiencies and predict equipment failures, thereby reducing downtime and improving operational efficiency. Users appreciate its user-friendly interface and the speed at which it can analyze large datasets. Compared to similar products, TrendMiner is often praised for its ease of use and robust functionality. Pricing details for TrendMiner are not readily available, so it is recommended to contact SelectHub for a customized quote based on specific needs.
Cost Breakdown
$100 - $500
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Microsoft Machine Learning Server

by Microsoft Corporation
Microsoft Machine Learning Server
Microsoft Machine Learning Server is an AI-enabled enterprise intelligence solution with big data capabilities. With full R and Python support, it produces predictive and retrospective analytics, with the ability to score structured and unstructured data. It is fully machine learning enabled, with options to train or use prebuilt trained models. It integrates with major open-source resources and other Microsoft entities like Azure. Its web service publishing and operationalization features streamline putting insights to work.
Cost Breakdown
$100 - $500
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Pivotal Big Data Suite

by Pivotal Software, Inc.
Pivotal Big Data Suite
The Pivotal Big Data Suite is an integrated solution that enables big data management and analytics for enterprises. It includes Greenplum, a business-ready data warehouse, GemFire, an in-memory data grid and Postgres which helps deploy clusters of the PostgreSQL database. With a data architecture built for batch as well as streaming analytics, it can be deployed on-premise and in the cloud, and as part of Pivotal Cloud foundry.Since 2019, Pivotal has been owned by VMWare. Besides an annual subscription, the vendor offers pricing by core for its computing resources. It is compatible with open data platform (ODP) versions of Hadoop and works with Apache Solr to provide text analytics.
Cost Breakdown
$100 - $500
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Buyer's Guide

Big Data Platforms Are All About Comprehensive Insights

By Ritinder Kaur, Market Research Analyst at SelectHub

Big Data Platform BG Intro

Big data platforms changed the software development landscape and challenged existing paradigms. Vendors scrambled to assemble applications capable of managing large, complex volumes and generating analytics insights.

Your systems may need a boost if you’re constantly playing catch-up with your digital assets. Our buyer’s guide will help you pick a best-fit big data solution to address your BI and analytics needs. Read on for desirable features, trends and handy selection tips.

Executive Summary

  • Big data platforms help businesses achieve their business intelligence and analytics goals while managing large volumes of information.
  • Determining your must-have features and devising a software selection strategy is an unmissable step of your purchase process.
  • Data interoperability across systems, scalability and robust integration with fast-processing systems are critical big data software features.
  • Centralized analytics is a thing of the past, with domain-driven data products and federated analytics changing how we do data pipelines.
  • Prepare a list of questions to ask vendors before making a purchasing decision.
What This Guide Covers:

What Are Big Data Platforms?

A big data platform is a software application to manage huge data volumes and types. They make up for conventional platforms unequipped to handle new data varieties and volumes.

Big data is a term for large datasets that are impossible to capture, manage or process through conventional databases. These include multimedia, sensors, IoT (Internet of Things) and streaming data.

Related Categories

Volume, variety and velocity characterize big data.

What else is new? Big data analytics platforms can differ mutually in how they manage, store and process information, especially in how they integrate with other applications and BI systems.

  • One tool might process data at the source to save power, while another brings it into a data warehouse.
  • Processing enormous workloads can be overwhelming for conventional systems. It spawned the launch of distributed computing platforms like Apache Hadoop and its next-gen version, Spark.
  • Hadoop accelerates processing tasks by distributing workloads among its clusters of servers that run them in parallel.
  • Every data platform offers some semblance of user autonomy. As a discerning buyer, match your users’ skills and needs to the platform’s self-service capabilities to find a good match.

Architecture

What does a big data platform’s architecture look like? Before a standard design was approved, industry software leaders — Oracle, SAP, Microsoft and IBM — proposed homegrown designs for big data systems.

These architectures were similar in design with the following components.

National Big Data Reference Architecture (NBDRA)

To drive standard services globally, the European Commission proposed that a common architecture was a must for big data systems. Siloed software serves no one, binding consumers with specific vendors and blocking collaboration and innovation.

The National Institute of Standards and Technology (NIST) Big Data Public Working Group (NBD-PWG) sought to develop a comprehensive list of big data requirements for use cases in nine primary software domains.

  • Government operations
  • Healthcare and life sciences
  • Commercial
  • Deep learning and social media
  • Research
  • Astronomy and physics
  • Defense
  • Earth, environmental and polar science
  • Energy

They proposed the Big Data Reference Architecture (NBDRA). It’s a common standards roadmap consisting of data definitions, taxonomies, reference architectures, and security and privacy needs.

The NBDRA is a living document, evolving with new developments in big data. Its components include the following.

  • A data provider
  • A big data system with collection, preparation, analytics, visualization, security and privacy compliance
  • The big data framework provider with various technologies linked in a specific hierarchy
  • Data-consuming applications or users
  • A system orchestrator that organizes the data application activities into an operational system
  • The systems and data lifecycle management layers.

Deployment Methods

Though many enterprises still prefer to keep sensitive data on proprietary systems, cloud solutions are popular. Private clouds provide the security and discretion of on-premise systems with fast computing speeds.

On-Premise

Many enterprises with legacy on-premise systems find it easier to maintain the status quo and prefer patching on a big data solution to their existing infrastructure.

Companies that set up on-premise solutions from scratch have their reasons, like wanting greater control over the infrastructure and data and possibly a strong IT team to back them up for maintenance and fixes.

But scaling to take on big data workloads can be tedious and resource-intensive.

Cloud-Based

Since scalability is at the core of big data analytics, cloud deployment is the go-to method for most big data needs. Besides software-as-a-service, big data providers offer cloud infrastructure and managed services to prepare and store your data.

These platforms offer:

  • Extensive scalability.
  • Zero infrastructure and maintenance costs. You only pay recurring subscription fees.
  • 24x7 data accessibility from anywhere.
  • Painless implementation and low cost of entry.

A downside is that you must rely on a third party for data security. However, many cloud software vendors provide robust encryption and regular penetration testing with annual audits, which should ease security concerns.

Hybrid

If you don't want to rely entirely on cloud providers for data governance and protection, best-of-breed hybrid cloud deployment might be the way to go. It’s a patchwork of multiple private and public cloud and on-premise environments.

And it’s the best of both worlds. You get the cloud's efficiency, flexibility and affordability while keeping data secure.

But hybrid adoption can be complex, needing careful planning, workload assessment and continuous optimization.

Benefits

When your digital assets are as multifaceted as big data, a platform that has your back can make all the difference. It assists operations and informs customer interactions, including outreach, support and sales.

When processes work smoothly, focusing on what matters becomes easy. You can see what’s working and do more of it. Besides, you can identify and remove blockers, turning them into opportunities.

Big Data Platform Benefits

  • Maximize ROI: Accurate, timely data drives intelligent decision-making and empowers you to take advantage of opportunities. You can optimize returns by improving process efficiency and employee productivity.
  • Stay Competitive: Market intelligence tells you when to diversify by launching new products. Uncovering unexplored customer segments can be rewarding, like Coca-Cola benefits from offering corn-free soft drinks during Passover.
  • Sell Better: Customer analytics is the most significant advantage of big data mining. It supports personalized marketing via email outreach, discounts and loyalty programs.
  • Anticipate Risk: Cut your losses by ditching products and campaigns that are money sinks. Invest wisely — decide when to sell/buy by tracking stock markets. Approve/deny loans to individuals/companies based on risk assessment.
  • Streamline Resource Allocation: Plan for the next financial period by comparing the actual and estimated budgets. Allocate funds for expenses, manage the cash flow across departments, and support project planning and hiring decisions.
  • Manage Inventory: Optimize stock levels with information on market demand. Keep carrying costs in check with intelligent route planning using location data and traffic updates. Big data can include addresses, zip codes and area names.

Implementation Goals

Assess internally why your company stakeholders want to implement a big data solution. What are their objectives? What do they hope to achieve?

Matching your requirements with features of shortlisted software can help you select a suitable solution.

Goal 1

Stay Competitive
  • You want to sell better than the competition.
  • Sticking to long-term objectives is business-critical.
  • You wish to be ready for the future.
  • You hope to explore innovation options.

Goal 2

Improve Operations
  • You want to boost revenue.
  • Improving efficiency is a key focus area.
  • You need insights 24/7.

Goal 3

Boost Customer Satisfaction
  • You want accurate client insights.
  • You want to acquire new customers.
  • You wish to keep existing buyers happy.
  • You hope to reduce customer churn.

Goal 4

Manage Big Data
  • You wish to monetize your organizational information.
  • You want end-to-end data management in one tool.
  • You hope to scale the solution as data grows without impacting performance.

Goal 5

Maintain Data Quality
  • You want teams to have confidence in their data.
  • Client trust is vital for you.
  • You want watertight security for sensitive data.

Basic Features & Functionality

Self-service, interactivity and customization options allow you to view, explore and discover metrics according to your role and requirements.

Which other features should be on your must-have list?

Interoperability

It allows systems to combine and use data freely, regardless of location and type.

Your solution must connect to your preferred data sources and applications — vendor-provided SDKs (software development kits) help you do that.

The big data tool must have the means to combine standardized data, which is where connectors play a role.
Data Storage Integration

Big data platforms may or may not have storage, so they rely on external systems or additional modules.

Hadoop Distributed File System (HDFS) supports Hadoop and Spark clusters. Ask potential vendors if your preferred systems have data storage integration.
High-Volume Processing

Highly performant systems process data and scale fast, distributing workloads onto separate servers. Many such systems use machine learning to automate workflows.

A trial run should tell you if the solution suffers from performance lags and latency issues.
Data Preparation

Not every big data tool will have data prep capabilities — Hadoop doesn’t. But it has Hive, Apache Pig and MapReduce in its corner.

Apache Spark enables data preprocessing before you aggregate it for reporting and analysis.

Big data tools with data prep, cleansing and enrichment include Alteryx, Datameer and SAS Data Loader for Hadoop.

Hadoop works seamlessly with Qlik Sense, Tableau and Alteryx Designer Cloud for generating visual insights.
Data Blending

It’s the process of combining datasets from various sources for comprehensive insights.

Spark has data blending built in, while Hadoop works with KNIME Analytics, Pentaho Data Integration and Tableau.
Data Modeling

Building data models involves creating a concept map of how various datasets relate mutually.

It helps organize data in repositories and assists in analysis and trendspotting. Entity-relationship diagram, data flow and dimensional modeling are relationship-mapping techniques.
Scalability

Vertical and horizontal scalability — adding more power, storage and servers — keeps you worry-free when workloads increase.

If your big data analytics platform doesn’t match your scalability requirements, leaning on a platform like Hadoop might be the next best thing.

Additionally, techniques like data caching and query optimization keep your platform performant. Task processing in parallel accelerates big data analysis.

Automatic failover and fault-tolerant processing are essential for 24/7 data availability.

Advanced Features & Functionality

They might not be business-critical today, but investing in advanced features today can save you from inflated costs later.

In-Memory Computing

It’s a power-saving feature that allows storing data in the RAM rather than querying sources repeatedly, which is also draining for the database.

Apache Spark stores data in memory and marks data points while computing instead of reloading from the disk again.
Stream Processing

It’s the process of computing data as the system produces or receives it. The entire process of cleansing, aggregating, transforming and analyzing happens in real time.

It’s an essential technology for analyzing sensor data, social media feeds, live videos and clickstreams.

Spark has built-in streaming, while Hadoop relies on Apache Storm and Flink. Kafka is another streaming analytics platform.
Integration With Programming Languages

Support for coding languages makes a big data tool a better choice for building custom apps.

Many big data platforms have out-of-the-box Python, R and Javascript libraries. PySpark is a Python-Spark library.
Machine Learning

Artificial intelligence (AI) and machine learning are efficient model-building assistants with automation and sophisticated algorithms.

If your big data platform doesn’t have machine learning built-in, can it integrate with a solution that does? Hadoop relies on Apache Mahout for building and deploying ML models.

Spark has a model-building ML library, MLib, with handy algorithms and tools.

Current & Upcoming Trends

Big Data Platform Trends

Edge Computing

Big data redefined computing to extend to edge devices. They include machines that collect real-world information — smartphones, home automation bots, sensors, medical scanners and industrial systems.

Amidst the deluge of big data information, businesses struggle with processing costs. Conveying data to warehouses and databases consumes time and power, while processing it at the source is cost-effective and faster.

Edge devices are optimized to incorporate low-bandwidth touchpoints and can operate without cloud infrastructure.

Manufacturing and telecom companies are leading in adopting edge technologies, with monitoring, response and site trustworthiness being their primary focus areas.

Artificial Intelligence

The hype about AI-ML and natural language processing is quite loud presently. They’re excellent equalizers, enabling anyone with basic computer skills to interact with data and gain helpful information.

Model training makes these technologies fast and near-accurate and allows open-ended exploration and data analysis. Benefits of AI-ML include outlier detection, pattern recognition and forecasting.

What-if scenarios and time series analysis enable glancing into the future and predicting what’s likely to happen with a fair degree of accuracy. Prescriptive analytics has raised the bar — the software suggests how to proceed to get the best possible outcome.

Despite these innovations, AI-related data security concerns are rising, and there aren’t enough guardrails yet. We can't decide whether we love or hate it.

Data Standardization

Standardized data facilitates software systems working together and using information interchangeably. Common definitions, terminologies and references driving big data, cloud, IoT and AI systems make for a globally connected digital system.

Interoperability is the property of digital resources to be accessible to the involved parties via standard definitions and formats. It instills confidence in users, provides open market access and encourages innovation.

The Big Data Working Group mandates that organizations maintain data consistency by adopting NBDRA-established standards. It significantly impacts how vendors develop software and innovate.

One such technology is the semantic metadata model — an index with references and dataset attributes that supports cross-platform integration and ubiquitous data sharing.

It’s why the data mesh is the talk of the town.

Data Mesh

Zhamak Dehghani, principal technology consultant at ThoughtWorks and creator of the “data mesh,” describes it as a decentralized approach to monolithic architectures.

Data lakes are centralized and generic, and a centralized analytics team can be a bottleneck, especially when scaling to meet additional workloads.

Domain-driven design is a necessity to accelerate data pipelines, she says.

A data mesh splits the architecture into “microservices-like” data products for storage, transformation and modeling. Each data product belongs to a separate team and serves a specific purpose in the analytics pipeline.

All components conform to quality and interoperability standards, with the respective teams owning data quality.

Data mesh adoption is still in its infancy. As Dehghani says, the technology exists. What enterprises are yet to achieve is a mindset shift.

Software Comparison Strategy

It can be overwhelming to make an informed choice, with many big data platforms having similar features. Your business is unique, so what works for others might not be the best fit.

How can you proceed?

Start by determining your business needs. Analyze where your current big data analytics solution falls short and how a new solution might help fill the gaps.

Check if the solution is performant while querying data.

  • Is live source connectivity available, or is replication the only option?
  • Check how live querying affects performance in tools that promise real-time insights. Querying data at source is resource-intensive, and most databases don’t stay open to live query code as it drains their power.
  • The more queries there are, the greater the power consumption. Check with the vendor or test run the product to assess how performance varies with fluctuating workloads.

Here are some other considerations when evaluating big data analytics platforms.

  • Integration with Hadoop is a primary requirement as its distributed file system tips the scales. Additionally, during your integration journey, you’ll encounter systems that work with Hadoop. Why not factor it in at the onset?
  • Hybrid cloud solutions are worth considering, with many vendors offering tailor-made configurations.
  • Archiving data consumes less storage than performing calculations, so assess your needs before settling on a storage capacity.
  • Consider including in-memory analytics as it’s power-saving.

Vendor research is crucial and online reviews, industry guides and peer recommendations are excellent resources. Free trials are worth your time to check product usability and functionality.

Get started today with our big data platform comparison report to gain feature-by-feature insight into top big data software leaders.

Cost & Pricing Considerations

Flexible, tier-based pricing makes cloud solutions accessible, but consider user seats, computing speed and storage when calculating the total cost.

Pay-as-you-go models charge by usage, costing less per storage unit as consumption increases.

24/7 email and phone support is likely to cost extra. Additionally, it’s better to factor in the cost of data migration, deployment, customization and add-ons at the onset.

The Most Popular Big Data Platforms

Jumpstart your software search with our research team’s list of the top five big data platforms.

Oracle Analytics Cloud

What It Does

Oracle Analytics Cloud is a big data platform with extensive source integration via over 45 data connectors. OAC highlights dataset correlations across visualizations on the same dashboard with a brushing feature.

Oracle Analytics Cloud

Intuitive dashboards address the last mile of analytics in Oracle Analytics. Source

The system delivers meaningful insights on demand by processing natural language. Additionally, it generates best-fit visualizations using machine-learning recommendations.

Product Overview
User Sentiment Score 84%
Analyst Rating 96
Company Size S | M | L
Starting Price $16/user/month Source

IBM Watson Analytics

It’s a data science solution that with natural language processing. The vendor evolved it from a question-answering (QA) computing system to a prescriptive system. It relies on Hadoop for data processing.

IBM Watson Health has over 100 techniques for analyzing language, recognizing sources, finding evidence and scoring it to rank possible hypotheses and present them to the user.

A 30-day free trial is available.

IBM Watson Analytics

Predict churn rate in IBM Watson Analytics.

Product Overview
User Sentiment Score 84%
Analyst Rating 95
Company Size S | M | L
Starting Price $140/month Source

SAP HANA

It’s the vendor’s in-memory database and development platform with robust data processing and real-time analytics. Processing data in memory helps avoid time-intensive aggregations. SAP HANA workflows run seamlessly on Azure.

It provides database services to SAP Analytics Cloud, SAP Data Warehouse Cloud, SAP Business Applications and third-party platforms. Besides data preprocessing, columnar storage, data aging, dynamic tiering and data recovery are available.

SAP HANA

Analyze cash flow across periods, accounts and liquidity items with SAP HANA. Source

Product Overview
User Sentiment Score 86%
Analyst Rating 92
Company Size S | M | L
Starting Price Available on request

BIRT

It’s an open-source reporting and BI solution on Eclipse IDE for Java and Java EE rich-client and web programs. A visual designer addresses enterprise reporting needs with a built-in charting engine and runtime component.

BIRT supports reusable queries, crosstabs and combining data from multiple sources within a report.

BIRT

BIRT Report Designer has a rich chart library out of the box.

Product Overview
User Sentiment Score 80%
Analyst Rating 90
Company Size S | M | L
Starting Price $30/month Source

Qlik Sense

It’s a big data analytics and app-building platform with pre-built analytics components and the option to design custom modules. Its sources include files, websites, applications and big data systems like Hadoop, AWS and Azure.

Qlik Attunity keeps data synced across sources, databases, warehouses and data lakes. Qlik Catalog promotes client trust in record reliability with lineage tracking. You can build custom apps using the Qlik partner network and its open APIs.

The vendor offers annual subscriptions to the Business and Enterprise editions. A 30-day trial is available.

Qlik Sense

Get answers fast from Qlik’s Insight Advisor. Source

Product Overview
User Sentiment Score 85%
Analyst Rating 90
Company Size S | M | L
Starting Price $30/user/month Source

Refer to our Jumpstart platform to compare your shortlisted products feature-by-feature.

Questions To Ask Yourself

Use these questions as a starting point for internal conversations:

  • What are our current key pain points?
  • Which are the must-have big data platform features to address them?
  • Which operational domains (HR, inventory, project planning) should integrate with the new system?
  • Are we ready for a data mesh architecture?

Big Data Platform Key Questions To Ask

 

 

 

Questions To Ask Vendors

Learn more about vendors and their offerings by adding these questions to your list.

About the Software

  • Is the software customizable?
  • What security features does it offer?
  • How does the solution integrate with other systems?
  • Is it compatible with my tech stack?

About the Vendor

  • What business sizes do you work with?
  • What are the support and training options?
  • How long does onboarding take?
  • What’s the average support response time?

Next Steps

Expecting your legacy systems to scale with your big data needs is like playing with a losing hand —you’ve got the wrong cards. To win, choose a data platform that handles your big data end-to-end and supports advanced analytics with machine learning.

Define your business needs with our free requirements checklist and get started on your software search. All the best!

Product Comparisons

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.
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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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