Categories   >   Data Warehouse Tools
Last Reviewed: November 11th, 2024

Best Data Warehouse Tools Of 2024

What are Data Warehouse Tools?

Data Warehouse tools are the data janitors and analysts rolled into one. They clean up scattered information from databases, spreadsheets, and other sources, storing it in a central location. This eliminates data silos, those isolated pockets of information that hinder clear insights. By integrating this data, businesses can finally see the big picture. These tools then analyze the information, generating reports that answer key questions. Imagine a retailer wondering which products fly off shelves together. Data Warehouse tools can show them these patterns, enabling data-driven decisions about promotions and inventory. While some technical expertise might be needed, these tools are getting easier to use. Features like drag-and-drop dashboards and visual storytelling put insights at the fingertips of a wider audience. Retail, finance, and healthcare especially leverage this power. However, upfront costs and keeping the data clean can be hurdles. But for businesses seeking to transform data chaos into actionable knowledge, Data Warehouse tools are a powerful weapon.

What Are The Key Benefits of Data Warehouse Tools?

  • Unified Data View
  • Improved Data Quality
  • Self-Service Analytics
  • Faster Reporting
  • Data-Driven Decisions
  • Actionable Insights
  • Reduced Costs (Long-Term)
  • Enhanced Collaboration
  • Predictive Analytics
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Overall

Based on the latest available data collected by SelectHub for 51 solutions, we determined the following solutions are the best Data Warehouse Tools 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. 
Tableau Big Data
Tableau Big Data
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Key Features

  • Big Data Integrations: Connects seamlessly to all kinds of data infrastructures and sizes of data sets, including Big Data. Its ecosystem integrates with Hadoop distributions, NoSQL databases and Spark sources with ease. 
  • One Data Interface: Consolidate and democratize data in one place. Invite all members of an organization to explore data, asking more questions and finding more answers.
  • VizQL: Interact with data in real time with a proprietary technology that allows for limitless exploration that’s more accessible to all skill levels than traditional coding.
  • Data Catalog: Take advantage of data governance with a glossary of data sources and standard data definitions, as well as metadata management and standardization of data procedures. Understand data in context and easily find the right data for the job within vast data sets.

Pricing

License/Subscription Cost
  • Based on the number of users
  • Based on an annual subscription model – per user, per month (billed annually)
  • Subscription costs allow users to access Tableau over a set timeframe for either an on-premise or cloud-based/SaaS model
  • Subscription licensing model requires a one-time license fee
  • Provides enterprise subscriptions/enterprise licensing based on the type of SMB and the enterprising plan selected with both the on-premise and cloud-based/SaaS model
Maintenance Cost
  • Cost is included in the price of subscription license. This feature includes ongoing updates, access to Tableau’s ongoing product upgrades and support services at no additional charge
Installation/Implementation Cost
  • Included in the upfront subscription cost
  • No hidden costs
Customization Cost
  • The custom version cannot be requested. Instead, users can do limited customizations on their own, such as:
  • User can change the server name that appears in the browser tab, tooltips and messages
  • User can change the logos that appear in different server page contexts
  • User can control the language used for the server user interface and the locale used for views
  • Custom fonts can be installed for different clients
  • Administrators and project leaders can also add images for projects in thumbnail view
Data Migration Cost/Change Management/Upfront Switching Cost
  • Data Migration is possible in Tableau Servers and can be done with the following tools: Tabcmd Script, REST API, TabMigrate and Enterprise Deployment Tool by InterWorks
  • There is a limit to how much data can be stored within Tableau Online and the storage cost will increase if that threshold is crossed
Training Cost
  • The price will vary depending on the type of training opted for. Options include live online training, classroom training, eLearning and certification programs.
  • Tableau provides free training videos, whitepapers and product demos for streamlining the implementation process.

Two types of training can be obtained:

  • In-person training takes two days, and classroom sessions are available in various global locations
  • Virtual classes are held four times a month and have a duration of 4 to 5 days
  • The cost of training is $1,400 per person for each type of training
  •  Web Authoring training costs $700 per person

eLearning is a year-long subscription that gives users access to two courses:

  • Desktop I: Fundamentals and Desktop II: Intermediate
  • The cost of Tableau Certification varies depending on the type of course that a user takes
  • Tableau Desktop 10: Qualified Associate ($250/exam fee), Certified Professional ($600/exam fee) and Delta Exam ($125/exam fee)
  • Tableau Server 10: Qualified Associate ($250/exam fee), Certified Professional ($800/exam fee) and Delta Exam ($125/exam fee)
Recurring/Renewal Costs
  • Renewal cost is equivalent to the fees paid annually, based on the number of users
  • Regular support services are built into the subscription price. However, professional services, such as on-site consulting, are add-on expenses
Cloudera
Cloudera
Start Price
$833
Per User, Annually
Company Size
Small Medium Large
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.
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.
Hortonworks
Hortonworks
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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. 
BIRT
BIRT
Start Price
$0
Open-Source
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked BIRT

Reviews for BIRT paint a picture of a user-friendly report designer with a loyal following, particularly among those new to report building. Its drag-and-drop interface and pre-built templates are praised for streamlining report creation compared to coding-heavy solutions. This is a major advantage for businesses that need reports up and running quickly, without tasking developers.

BIRT shines in its ability to connect to various data sources, eliminating the need for complex data extraction steps. This is a big win for teams working with data scattered across spreadsheets, databases, and even flat files. While BIRT offers strong core functionality, some reviewers mention a steeper learning curve for advanced features. For instance, users who need to wrangle massive datasets or create highly customized data visualizations might find BIRT limiting compared to more feature-rich (and often pricier) competitors. Similarly, BIRT's mobile capabilities are seen as less robust than some competing products. This can be a dealbreaker for teams that prioritize mobile reporting for field staff or remote workers who need real-time access to reports.

In conclusion, BIRT is a solid choice for businesses seeking a cost-effective and user-friendly solution for generating basic to moderately complex reports. Its strength lies in its ease of use, data source flexibility, and affordability. However, those working with exceptionally large datasets or requiring a highly customized mobile reporting experience might be better served by exploring feature-rich alternatives.

Pros & Cons

  • Drag-and-Drop Report Design: BIRT boasts a user-friendly interface that lets you visually design reports using drag-and-drop functionality. No coding required! This streamlines report creation for business users who might not be familiar with programming languages.
  • Flexibility and Customization: BIRT empowers you to tailor reports to your specific needs. From basic tables to complex charts and graphs, BIRT offers a wide range of data visualization options. This ensures your reports effectively communicate insights to a variety of audiences.
  • Integration with Various Data Sources: BIRT seamlessly connects to diverse data sources like databases, spreadsheets, and even flat files. This eliminates the hassle of data extraction and manipulation, allowing you to focus on crafting impactful reports.
  • Scheduling and Automation: BIRT allows you to schedule reports to run automatically at specific intervals. This ensures you always have access to fresh data, saving valuable time and keeping everyone on the same page.
  • Steeper Learning Curve for Advanced Features: While BIRT excels in drag-and-drop simplicity for basic reports, users venturing into complex functionalities like data scripting or advanced data manipulation might find the learning curve steeper. This can be a hurdle for teams without in-house BIRT expertise.
  • Potential Performance Issues with Large Datasets: Some users report performance limitations with exceptionally large datasets. BIRT might not be the ideal choice if you consistently work with massive amounts of data that require real-time report generation.
  • Limited Mobile Capabilities: While BIRT reports can be accessed on mobile devices, the user experience might not be optimal for all report formats. This can be a drawback for teams that heavily rely on mobile reporting for on-the-go decision-making.

Key Features

  • Data Explorer: Build connections to data sources and view them together with business assets and data cubes.
    • Access and blend various datatypes from a range of sources, including POJOs, JDO datastores, SQL Databases, JFire scripting objects, XML and web services. 
    • Pull in more data than before by extending the available sources with Eclipse’s Open Data Access framework. 
    • Accesses Hadoop data using Hive Query Language. Ingests data from distributed databases like Cassandra through APIs. 
    • Pulls data from LDAP, report documents and Java objects. In addition to Salesforce, it can ingest information from LinkedIn, Facebook and GitHub. 
  • Report Designer: Supports a wide range of reports, layouts and formatting, with reuse of designs and elements. Create report layouts, connect to data sources and produce XML-based report designs. 
    • Navigator: Create a new Report Design Project or a new BIRT file within a project through the navigator. View all the projects in the workspace and create multiple file types such as a design file, template file, library file or report document. 
  • Sample Report Viewer: View reports in a sample web application before publishing them online. Generate and export them in HTML, PDF, CSV formats. Print locally or on the server and rerun them when needed with new parameters. 
  • BIRT Report Engine: Query data sources and merge the query results into the report layouts created by the Report Designer. Produce the report output in a wide range of formats that include Microsoft Office, HTML, PDF, postscript and open document formats. This feature is also available in the BIRT Web Viewer. 
  • Data Visualizations: Create data visualizations with task-specific editors, builders and wizards and integrate into web systems. 
    • Charts: Choose from a vast library of pie charts, Gantt charts, pyramid charts, scattergrams and many more, with multiple subtypes, such as a bar chart with options of side-by-side, stacked and percent stacked. Create charts in 2D, 2D with depth and 3D formats. 
    • Crosstabs: Present data in two dimensions (sales or hits) with the cross-tabulation or matrix feature. 
    • Palette: Drag and drop elements from the palette into the visualization layout. Add rich text to the report, including HTML formatting integrated with dynamic data. Aggregate business metrics with more than 30 built-in SQL operators. 
  • Customization: Make report data easy to understand with support for internationalization, including bidirectional text. Easily replace static report elements — report labels, table headers and chart titles — with localized text. 
  • Lists: Present data methodically through simple data lists by grouping related data together. 
Zoomdata
Zoomdata
Company Size
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Deployment
Cloud On-Premise
Platform
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Why We Picked Zoomdata

How can Zoomdata help your business intelligence "zoom" to new heights? Zoomdata (now known as Logi Composer) is a business intelligence tool that garnered a devoted following for its ability to handle large, complex datasets, making data visualization and analysis accessible to a wide range of users. Users rave about its intuitive interface and real-time streaming data analysis capabilities, allowing them to "pause, rewind, and replay" data streams, a feature described by one user as a "game-changer" for identifying patterns and anomalies. This sets it apart from traditional BI tools that often lag in real-time data processing.

However, Zoomdata is not without its drawbacks. Some users have pointed out the absence of built-in predictive analytics features, limiting its ability to forecast future trends and perform more in-depth statistical modeling. For instance, one user noted that while Zoomdata excels at presenting "what is happening," it lacks the robust forecasting tools found in competitors like Tableau, making it less suitable for businesses heavily reliant on predictive modeling.

Overall, Zoomdata (Logi Composer) is best suited for organizations seeking a user-friendly BI tool for real-time data visualization and exploration, particularly those dealing with large, streaming datasets. Its intuitive design and powerful data sharpening capabilities make it ideal for users of all technical levels, empowering them to make data-driven decisions quickly and efficiently. However, businesses requiring advanced predictive analytics or statistical modeling features may need to consider alternative solutions or integrations to supplement Zoomdata's capabilities.

Pros & Cons

  • Fast Visual Analysis: Zoomdata is known for its ability to quickly process and visually analyze large datasets, making it ideal for handling big data.
  • Easy-to-Use Interface: Zoomdata offers a user-friendly platform that allows users with varying technical skills to explore data, identify trends, and generate visualizations without relying on data analysts.
  • Seamless Integration: Zoomdata can be easily integrated with other platforms, such as ClickFox, to enhance customer journey analysis by providing interactive dashboards and actionable insights.
  • Lack of Mobile App: Zoomdata lacks a dedicated mobile application, which can be a drawback for users who need to access data and dashboards on the go.
  • No Version Control: The platform lacks version control features, making it difficult to track changes to dashboards and analyses over time, potentially leading to confusion and difficulty in reverting to previous versions.

Key Features

  • Data Connectors: Users have access to a suite of pre-designed connectors that draw data directly from their selected data source. 
  • Drag-and-Drop Attributes: The simple drag-and-drop interface lets users drag, pinch, zoom, swipe and drop to create dashboards. 
  • Data Exploration: The system allows users to create interactive visualizations, customize dashboards and perform self-service analysis to discover data insights. 
  • Data DVR: By unifying historical data analysis with real-time data into a single interface, the dashboards operate like a video, allowing users to pause, rewind, fast forward and replay data streams. 
  • Microservices: Taking advantage of small, coupled programs that work in tandem with one another, Zoomdata is able to scale with a user’s business needs. Microservices are written to be able to be deployed and restart on-the-fly in the event of an outage. 
Alteryx
Alteryx
Start Price
$99
Per User, Monthly
Company Size
Small Medium Large
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. 
Spotfire
Spotfire
Start Price
$0.99
Monthly, Quote-based
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.
BigQuery
BigQuery
Start Price
$6.25
Per TiB, Usage-Based
Company Size
Small Medium Large
Deployment
Cloud On-Premise
Platform
Mac Windows Linux Chromebook Android
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Why We Picked BigQuery

Bigquery is a scalable big data warehouse solution. It enables users to pull correlated data streams using SQL like queries. Queries are executed fast regardless of the size of the datasets. It manages the dynamic distribution of workloads across computational clusters. The easy-to-navigate UI is robust and allows the user to create and execute machine learning models seamlessly. Users liked that it can connect to a variety of data analytics and visualization tools. However, users complained that query optimization is an additional hassle they have to deal with, as the solution is expensive and poorly constructed queries can quickly accumulate charges. It can be overwhelming for the non-technical user, and SQL coding knowledge is required to leverage its data analysis capabilities. Data visualization features are lacking and in need of improvement.

Pros & Cons

  • Performance: The system can execute queries on massive amounts of data with agility, as specified by about 89% of users who mentioned performance.
  • Functionality: About 68% of users who reviewed functionality talked about its robust inbuilt features.
  • Ease of Use: The UI is simple and easy to navigate, according to about 72% of users who talked about user-friendliness.
  • Integration: Approximately 75% of reviewers who talked about integration said that it connects to numerous other tools seamlessly.
  • Scalability: All users who reviewed scalability said that the platform scales to thousands of servers.
  • Cost: Approximately 76% of users who mentioned cost complained that it’s expensive, and charges can rack up quickly if queries aren’t properly constructed.
  • Learning Curve: About 82% of users mentioned that the software has a steep learning curve.
  • Resources: About 89% of users who spoke about resources said that documentation and video tutorials are lacking and need improvement.
  • Visualization: Data visualization capabilities aren’t up to the mark, according to all users who talked about visualization.

Key Features

  • Machine Learning: Comes with machine learning modules that can perform mass-segmentation and recommendations in seconds. These modules can be built and trained within minutes without ingesting data for training. 
  • Cloud Hosted: Handles all the hardware provisioning, warehousing and hardware management from the cloud. 
  • Real-Time Analytics: Large volumes of business data are quickly analyzed and presented to the user to ensure that insights and data discrepancies can be immediately uncovered. 
  • Automated Backups: Data is automatically stored and backed up multiple times a day. Data histories can be easily restored to prevent loss and major changes. 
  • Big Data Ecosystem Integrations: Integrate with other big data products such as Hadoop, Spark and Beam. Data can be directly written from the system into these products. 
  • Data Governance: Features such as access management, filter views, encryption and more are included in the software. The product is compliant with data regulations such as the GDPR. 

COMPARE THE BEST Data Warehouse Tools

Select up to 2 Products from the list below to compare

 
Product
Score
i
SelectHub’s Analyst Rating is based on data from our 400+ point analysis of Data Warehouse 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
Hadoop
Undisclosed
No
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Hadoop
Tableau Big Data
Still gathering data
No
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Tableau Big Data
Cloudera
$833
Per User, Annually
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Cloudera
Domo
$15
Per User, Monthly
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Domo
Hortonworks
Still gathering data
No
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Hortonworks
BIRT
$0
Open-Source
No
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Cloud On-Premise
Mac Windows Linux Chromebook Android
BIRT
Zoomdata
Undisclosed
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Zoomdata
Alteryx
$99
Per User, Monthly
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Alteryx
Spotfire
$0.99
Monthly, Quote-based
Yes
Small Medium Large
Cloud On-Premise
Mac Windows Linux Chromebook Android
Spotfire
BigQuery
$6.25
Per TiB, Usage-Based
Yes
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Cloud On-Premise
Mac Windows Linux Chromebook Android
BigQuery

All Data Warehouse Tools (51 found)

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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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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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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
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$10 or less
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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
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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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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
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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
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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
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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
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SAS Viya

by SAS Institute
SAS Viya
SAS Viya is a cloud-based in-memory analytics engine that provides data visualization, reporting and analytics to businesses for actionable data insights. Powered by AI, it brings together visual analytics, visual statistics and data science for enterprises to achieve end-to-end self-service analytics. It uses a standardized code base with support for programming in R, Python, SAS, Java and Lua. Deployable in the cloud, on-premises and hybrid environments, it integrates with a wide range of business applications through an agile, scalable architecture. The vendor offers an introductory 30-day free trial. Pros Comprehensive features Powerful analytics capabilities User-friendly interface Scalable architecture Strong support from SAS Cons Steep learning curve Limited customization optionsHigh cost of ownership Potential vendor lock-in Resource-intensive
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
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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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Pentaho Data Integration

by Pentaho Corporation
Pentaho Data Integration
Pentaho Data Integration helps organizations access, prepare and analyze all of their data from one place. It connects quickly and easily to any data source, whether it's structured or unstructured. It uses an extract, transform and load (ETL) approach to data management.Organizations can govern their data, ensure data quality and accelerate time to insight. Available on-premise and in the cloud, it’s easy to use and offers a wide range of features.
User Sentiment User satisfaction level icon: good
Cost Breakdown
$500 - $1,000
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Apache Arrow

by Apache Software Foundation
Apache Arrow
Apache Arrow is an open-source software development framework for building high-performing data analytics applications that process large, complex data sets. With an in-memory columnar format for data storage, it provides a query execution language that helps define the framework for analytics libraries. Language-agnostic due to easy translation, it acts as an interface between a wide range of programming languages and technologies.With a modern CPU architecture based on the single instruction, multiple data (SIMD) taxonomy, it processes data at lightning speeds. Its zero-copy design makes moving data round faster and more efficient.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$100 - $500
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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
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TARGIT

by Targit
TARGIT
Targit is built from the ground up for reporting and business metrics, getting actionable insight from trillions of rows of data in seconds. It surfaces complex events and provides key information to make business decisions. It merges internal and external data and presents it in customizable dashboards and storyboards. Businesses can figure out how to grow in target markets by accurately analyzing data. Deployment options vary from stand-alone to embedded, with web browser compatibility built-in.
User Sentiment User satisfaction level icon: great
Cost Breakdown
$1,000 or more
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Arcadia Data

by Arcadia Data
Arcadia Data
Arcadia Data is a cloud-native visual analytics platform that provides data visualization and analysis to enterprises. Its in-data-lake architecture enables accelerated insights in real time through integrations with Confluent Cloud, Google BigQuery, Amazon Redshift and other cloud data warehouses. Developers leverage its intuitive interface and no-code approach to build analytics applications and embed them into websites. Its ability to process batch and streaming data in a single, unified interface empowers enterprises to harness real-time data insights for enhanced decision-making.Owned by Cloudera since 2019, it serves small and medium-sized businesses in the U.S. and the U.K. In addition to its free version, Arcadia Instant, the vendor offers an Enterprise version.
User Sentiment User satisfaction level icon: excellent
Cost Breakdown
$100 - $500
Company Size
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WebFOCUS

by Information Builders Inc
WebFOCUS
WebFOCUS is a comprehensive data management and analytics platform that enables organizations to access, transform, visualize, and distribute data across multiple platforms. It's particularly well-suited for enterprises with large, complex datasets and a need for robust reporting and analytics capabilities. Key benefits include its ability to unify disparate data sources, create interactive dashboards and visualizations, and automate data-driven workflows. Popular features include its drag-and-drop report builder, self-service data exploration tools, and integration with various business intelligence applications. User experiences generally praise its ease of use, scalability, and ability to handle diverse data types. Pricing is typically based on the number of users, data sources, and required features, with options for both on-premise and cloud-based deployments. Pros Easy to use interface Handles large datasets Diverse data source integration Customizable reports and dashboards Scalable for enterprise needs Cons Occasional performance issues Limited out-of-the-box visualizations Upgrades can be complex UI may feel outdated to some Learning curve for advanced features
User Sentiment User satisfaction level icon: great
Cost Breakdown
$10 - $100
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Buyer's Guide

Data Warehouse Software Is All About Lightning-Fast Insights 

By Ritinder Kaur, Market Analyst at SelectHub

Data Warehouse Software BG Intro

In a competitive landscape, businesses need information at the speed of thought for faster decision-making. Data warehouse tools are BI software that provides business insights by storing this information in a centrally accessible repository. They eliminate silos with automated integration and structuring of all asset types, including semi-structured and unstructured data.

Data warehousing is a critical component of business intelligence. It empowers businesses to collect information from the various silos where it’s housed, speeding up turnaround during business decisions.

Executive Summary

  • Data warehouses provide easy access to original and new information, unlike transactional databases that only retain the latest data.
  • They provide pertinent information at your fingertips, empowering your employees toward greater efficiency, increased buyer perception and quality delivery.
  • It’s a good idea to shortlist your warehousing requirements before choosing a software solution.
  • Multi-source connectivity, integrations with existing applications, automation, data quality management, scalability and advanced analytics are some features to look for in a warehouse solution.
  • Decentralized warehouses, DWaaS, event-driven architecture and composable systems are some trending warehouse attributes.
  • Ask yourself and software vendors in-depth questions about the software and your expectations before buying.

What This Guide Covers

What is Data Warehouse Software?

Data warehouse tools store assets from multiple sources and reformat them for business reporting and analysis. These tools include repositories and software solutions that wrangle assets to get them analysis-ready, like data mining, ETL and statistical modeling tools.

By definition, a data warehouse is a subject-focused, integrated, time-variant and non-volatile asset repository. Unlike a transactional database, it stores information “after the fact,” that is, it’s not in production mode anymore, and the system can access it without impacting data integrity. Additionally, you can copy this data multiple times for easier access and improved performance.

A relational database regularly updates entities with the latest values; it doesn't retain historical values. On the other hand, in a warehouse, you’ll find completed events as well as their original transactions. Data warehouses are static snapshots of events, and there can be multiple views of the same data at different times.

Data Warehouse Software Categories

Building a warehouse from scratch is time-consuming and resource-intensive. It can be tricky for your organization to stay up-to-date with industry-standard security and governance regulations. A readymade warehouse solution’s feature set includes built-in compliance and security.

Deployment Methods

Based on your enterprise’s requirements, you can opt for on-premise or cloud deployment or a combination of the two. Though cloud-based software solutions are popular, what works for one company might not work for another. Here’s a quick look at the pros and cons of the various deployment options.

On-Premise

Data warehouses were initially built on local servers that gave companies ready access, greater control and better latency. The infrastructure cost is a one-time investment though managed maintenance overheads can add up.

On the flip side, it has a high cost of entry – self-deploying might be a pain with added hardware and IT costs, and you might lose out on periodic vendor updates. An on-premise tool isn’t elastic and scalable, so if you’re looking to grow the business, an on-premise solution might not be enough.

Cloud-Based

In contrast, SaaS warehouse solutions store, manage and process assets in public and private clouds. The vendor is responsible for implementation, updates and fixes, and services are available 24/7. Cloud solutions have a low cost of entry with monthly and annual subscriptions. Many vendors offer cost-effective pay-as-you-go pricing – pay only for what you use.

On the flip side, companies often shy away from adopting cloud solutions because of security concerns. Even the most secure repositories are vulnerable to hacking, and breaches can be debilitating. According to the ITRC’s data breach analysis for 2021 (gated report), the number of reported breaches till September 30th of the same year exceeded incidents in 2020 by 17%.

Internet availability is an external factor you can’t control, and any lag or connectivity issues can impact performance. Capacity pricing can be inflexible, especially if you’re a small or medium-sized business, and the cost of additional features and add-ons can add to the overhead. Since the vendor hosts your assets, you are dependent on server availability for access to your data.

There is a third option. Hybrid warehousing solutions offer the best of both worlds – the scalability and availability of cloud software with an on-premise platform's control, security and autonomy. If you’re looking to scale quickly but want to save on maintenance, hybrid might be the way to go.

Benefits

A warehouse solution is a single source of truth for all your information. Let’s look at how it can help you monetize your investment.

Benefits of Data Warehouse Tools

Gain Historical Insights

Warehouse solutions tell you how your business is doing because you can view previous information and existing metrics side-by-side. It’s helpful when you need the sales metrics for a specific product over the past five or 10 years. You can generate reports or pull this information into a dashboard for comparative analysis with current figures.

Warehouse solutions help with risk management and fraud prevention in banks, insurance companies and loan lending entities. They can analyze individual payment histories to vet applicants before approving loans and credit. In healthcare, physicians can prescribe treatment plans faster with historical patient data and medical research at their fingertips.

Empower Your Employees

Behind every self-service BI and analytics tool is a reliable asset repository like a relational database or warehouse. You can wrangle, parse and transform assets of any structure and type to store them in an analysis-ready format. With these assets, you can perform trends analysis and predictions through models and visualizations.

Warehouse solutions perform automated integration and asset processing to free you up for effort-intensive tasks. You can focus on building modeling workflows and analyzing critical business metrics. Faster processes mean quicker turnarounds and more impactful real-time decisions.

Understand Your Buyers

Warehousing solutions support customer analytics in consumer goods and services enterprises. You can aim for higher revenue by aligning your offerings with buyer preferences and including insightful customer metrics like buying trends and past purchases. Plan for the future; learn which products are in demand and why.

Get to know how seasonality and socio-economic trends affect your target demographic’s buying decisions. Highlight successful products and emulate their sales strategies; identify poor performers to allocate resources in potential improvement areas.

Boost Efficiency

Warehouses provide priceless business performance insights and boost efficiency by streamlining internal operations. Warehouse tools let you retain large volumes of historical and existing employee and department-wide information. As with business metrics, you can set internal KPIs and monitor through modeling, visualizations and analytical reports.

Keeping in touch with your operational metrics, you can better manage your supply chain and inventory management workflows with a clear view of in-demand and out-of-stock products. You can optimize inventory by replenishing stock before it depletes and downscaling demand if sales are slow.

Build Customer Trust

If your buyers are satisfied with your products, they’re more likely to form a lasting association with your brand. A loyal customer base translates into consistent returns; add to that the business from new leads, and you can move significantly ahead in the market. Warehouse tools drive revenue by storing accurate market trends and customer preferences.

Having actionable insights at hand helps you provide better proactive customer service. Enterprise-grade information security and governance builds customers’ trust in you and establishes your reputation as a reliable services provider.

Implementation Goals

When buying warehousing software, ask yourself and your stakeholders what you hope to achieve by deploying the tool. We discuss some common implementation goals here that might fit yours.

Goal 1

Move Ahead of the Competition
  • You want to capture the market and establish a reputation by ensuring your offerings are in sync with market demand.
  • You are looking to make more confident business decisions based on key performance metrics.

Goal 2

Get All Data in One Place
  • The solution should draw data from all possible sources, including conventional databases, text files, images, audio and video files, the cloud and streaming and web apps
  • It should store all this information in an accessible format for quicker insights.
  • You don’t want to have to do it manually.
  • No data gets left behind.

Goal 3

Gain Historical Performance insights
  • You are interested in tracking your business performance over time.
  • Your stakeholders want to analyze what worked and what didn’t.
  • Performance insights would help you push business strategies or disprove those that don’t perform to stakeholders with hardcore data.

Goal 4

Boost Operational Efficiency
  • Your company wants better internal workflows.
  • It would help to have improved interdepartmental collaboration. Teams should work in sync as often as needed without impacting processes and transactions.
  • You want business as usual during transition phases like offboarding, onboarding, training and adopting new technologies and tools.

Goal 5

Plan For the Future
  • The warehouse solution should provide hardcore data to back up your future plans.
  • It can give you a clear picture of upcoming opportunities and potential risks.
  • You want to prepare for unforeseen challenges with robust mitigation and disaster recovery plans.

Basic Features & Functionality

Preparing a requirements list will help you determine which warehouse solution best fits your company’s needs. While software vendors often advertise the newest, shiniest product features, identifying your basic functional requirements sets a sound foundation for your software search.

Source Connectivity

Ingests structured, semi-structured and unstructured assets with spatial and visual information and real-time feeds. Connects to relational databases out of the box for live, accurate updates and faster time-to-insight. Simplifies ad hoc tasks by letting you upload assets via the web interface.
Data Management

Draws data either through ETL or built-in connectors. Massively parallel processing ensures improved response times with enhanced throughput. The solution should have a pre-configured columnar format, partitioning and large joins to provision databases.
Data Querying

Handles multiple concurrent queries without provisioning new clusters unless necessary. Successfully abstracts how assets are laid out in the warehouse, freeing you up for other tasks. The software should support joins, stored procedures and workflow triggers with periodic automatic refreshes.
Analytics Integrations

Integrates natively with existing business systems and analytics software. Collaborate, build and deploy predictive models by integrating with ML modules.
Automation

Automate insight discovery – create the warehouse instance, load your assets directly from transactional databases and run queries within minutes. Create data-driven applications with automated materialized views, workload capture/replay, storage management and more.
Data Security

An efficient warehouse solution keeps customer assets exclusive by isolating the tenant stack and tenant app data. It ensures end-to-end security across systems, networks, applications, databases and other platforms. The tool should support encryption, row and column level security, auditing, user risk scoring, asset masking and redaction of sensitive information.
Data Quality Management

Incorporate unique assets and create data marts conforming to industry-level standards. Clean assets at every stage before loading in batches and in real time. Generates mappings for asset correction based on business logic. Reduces redundancies with indexing and normalization.

Advanced Features & Functionality

While every vendor doesn’t offer them, advanced features are worth considering if your company has specific needs. Advanced analytics and scalability greatly enhance and deepen the level of insights that a warehouse tool can deliver. Take a look and see if these are features that your organization should include on your requirements list.

Distributed Computing

The warehouse solution distributes computing tasks across multiple nodes, providing faster processing and redundancy if there’s a critical failure. It replicates assets across nodes; if one node fails, assets stay intact and retrievable.
Scalability

The solution’s computing and storage resources should scale with your business, shutting off when idle, so you only pay for what you use. It lets you configure cluster limits for auto-concurrency to kick in when the number of nodes increases. Connects to newer sources through well-defined interfaces and lets you personalize your assets.
Advanced Analytics

An advanced warehouse solution should have prebuilt ML algorithms and support R and Python, popular data scientist programming languages. No-code AI analytics with automatic machine learning identifies the best prediction algorithm for each workload. Automated feature selection uses specific data that results in the best possible outcome.

Automated model tuning identifies the parameters for the best performance.
OLAP

You should be able to query multidimensional assets and analyze them from different perspectives in the warehouse tool. OLAP pre-aggregates and pre-calculates assets for faster analysis.
Environment Monitoring

Monitor the warehouse environment for metrics and alarms. Ensure zero downtime and consistent, continuous system availability through self-patching and self-tuning. Collects and analyzes files and diagnostics consistently for discrepancies.

Check how frequently the vendor releases updates and fixes.

Current & Upcoming Trends

We discuss some warehouse software trends here that might help you fine-tune your requirements. You may not need all these attributes in your warehouse tool instantly, but it’s a good idea to factor them in if planning to scale.

Data Warehouse Tools Trends

Decentralized Warehouses

Physical warehouses provide the advantage of having your previous and existing values in one place. Also, they help avoid speed and performance issues that can happen when querying sources directly. But, setting up can be costly in terms of hardware, software and ETL flows.

Decentralization, or virtualization, lets one computer host several virtual computing instances on demand, providing more in-memory storage and CPU cores. Businesses get more control over their desktop configurations, and employees can work on commodity hardware with little risk. It allows federated queries that applications, reporting tools, message-oriented middleware and other components can use.

Businesses can scale to new projects faster with reduced time-to-insight, doing away with infrastructure costs for multiple computers. Virtualization is likely to be in demand for the foreseeable future.

Data Warehouse-as-a-Service

Aside from traditional databases, IoT and big data sources add to the vast amount of information businesses generate daily. The cloud is an attractive option for businesses owing to 24/7 availability, no infrastructure overheads and low maintenance. Cloud-based warehouses like Amazon Redshift, Google BigQuery and Panoply can retain vast, complex datasets without the cost and complexity of conventional repositories.

The DWaaS market size was valued at $3.53 billion in 2020 and is expected to increase to $17.55 billion by 2028, growing at a CAGR of 22.08% (gated report). Growing consumer demand will spur companies to stay ahead of their rivals by capturing a significant chunk of the market. Supply chain management, asset management, customer analytics, risk and threat management will form the majority of the DWaaS consumers.

Data Warehouse Software Stats

Event-Driven Architecture

Real-time insights are critical in how companies conduct business, and event-driven architecture seems to be a significant part of the future digital landscape. Open-source, cloud-agnostic platforms like Apache Kafka and Apache Pulsar draw in large volumes of streaming and messaging data, called events. They act as triggers for publish-subscribe communication across microservices, faster than making REST calls to APIs.

Though it’s in its nascent stage yet, EDA will continue to be a priority, with streaming information increasing exponentially every day.

Composable Systems

Businesses often opt for all-in-one solutions that are cost-effective, giving up the option of acquiring the best-of-breed individual tools. A pre-assembled software stack is convenient and cost-effective, but the applications are all tied rigidly together. On the other hand, composable systems' dissociable components make building new applications more straightforward.

Developers can innovate freely, reuse code flexibly and enhance software by integrating components into their existing tech stack. Creating automated workflows is a breeze with the option to connect desired products via event streams. Vendors are designing cloud-native applications with composable architecture at the back-end, and this trend is likely to continue.

Augmented Analytics

With an eye on capturing the market, ISVs offer self-service AI, which is changing business analytics. Heavyweight workflows like query process optimization, workload-based aggregate caches and metadata-based rewrites happen behind the scenes. At the front end, drag-and-drop actions and NLP help you drill down to uncover hidden patterns that impact key metrics.

Enterprises are welcoming data democratization and pushing for organization-wide information literacy. Employees feel a sense of ownership, seeing their assets in action. Access permissions and authentication protocols ensure employees access only relevant insights and collaborate with others in a secure, audited environment.

ML-driven autonomous databases enable business modeling, spatial and graph analysis and low-code app development. AI-backed self-service insights are an essential aspect of warehousing, and this trend is likely to continue in the future.

Software Comparison Strategy

While searching for a warehouse tool, creating a requirements checklist can help shortlist your desired features. You can use our readymade requirements template as a starter. Or conduct in-depth research by comparing vendors and big data tools with our BI software comparison report.

Cost & Pricing Considerations

When evaluating warehousing software, it’s essential to keep your company’s budget in mind. Pricing depends on the deployment strategy, the number and type of user licenses required and the purchased modules and add-ons. You can also consider support options and packages if those are necessary.

Before buying a warehousing solution, carefully evaluate its cost on the vendor’s website or reach out to the vendor. You can also get our free pricing guide that will help you determine which top vendors align with your budget

Most Popular Data Warehouse Software

Navigating the inundated market of warehouse solutions can be overwhelming. Check out our top picks, curated by the SelectHub analyst team.

Hadoop

The Apache Hadoop library supports highly complex asset volumes' distributed processing at a low cost. It’s a robust framework based on simple programming models. Yet, it’s scalable from single servers to thousands of machines, each with local storage and processing.

It is highly available; it detects and handles failures at the application layer without relying on hardware.

Hadoop

Separate resource and node management daemons in Apache Hadoop YARN.

Tableau Big Data

Tableau integrates seamlessly with Hadoop distributions, Spark and NoSQL databases to structure data for exploration and analysis. Its Prep Builder tool blends information from various sources into an extensive list of database servers. With machine learning and NLP, you can analyze data on a user-friendly UI with intuitive drag-and-drop actions — no need for prior technical knowledge.

Tableau Big Data

A visual representation of the top customers ranked by estimated lifetime value sourced from Google Sheets.

Board

Board BI draws business information from relational databases and various applications to cloud-based data stores. The solution is robust and flexible, letting you tailor it to your needs with its no-code toolkit. Combining CRM and BI platforms, Board enables customer analytics and enterprise performance management.

Board

A Sales Intelligence and Planning Dashboard.

Domo

Domo’s data warehouse is available to administrators on the user interface from the toolbar. You can view a 3D dataset representation by connector, along with the data flowing in and out on a customizable carousel. With admin controls, you can uncover dataset relationships, watch them update in real time and instantly find out which sources need attention.

Search for a specific dataset or connector and click on a particular object to view its details. Additionally, dataset usage and system health details are available.

Domo

A 3D view of data sources on a rotating palette on Domo’s admin console.

Cloudera

The product is recognized as a core provider of warehousing capabilities in Nucleus Research’s latest technology value matrix. The vendor provides automatically configured warehouses and marts that you can customize. Workload isolation prevents noisy neighbors from causing resource overload, optimizing performance.

The solution is auto-scaling, and you can configure it to shut down after staying idle for a certain period. Security comes pre-configured, so you don’t have to set it up for individual catalogs and virtual instances.

Cloudera

Concurrency autoscaling for standard BI-type queries in the Hive virtual warehouse based on CDW.

 

 

Questions to Ask Yourself

Selecting a warehouse solution is a unique process for each organization, and what works best for one business may be wrong for another. There are countless vendors on the market, each offering unique platforms. Fortunately, SelectHub is here to help.

Ask yourself these questions as a starting point to properly evaluate warehouse tools in the context of your organization and its needs.

Data Warehouse Tools Questions to Ask

  • What are your data management, storage and processing challenges? What do you hope to achieve with the new solution?
  • What are your company’s current and future priorities?
  • What’s your budget? Are you overpaying for your current solution?
  • Which deployment model will your stakeholders prefer? Is going serverless an option?
  • What devices will the software run on across your company?
  • Who will use the software? What are their technical skill levels?
  • How much customization do you expect the software will need before installation?
  • What IT resources are available if you need to self-deploy the system?
  • How will the new solution integrate with your current or future technologies?
  • What does it offer in terms of self-service data management?

Questions to Ask Vendors

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

About the Software

  • How does the system connect to new and existing sources?
  • Does the vendor provide visual source management through a central console?
  • Will it integrate well with your existing analytics applications?
  • How often does the vendor offer updates and enhancements?
  • Which features will cost extra?
  • What level of customization is available?
  • Is it scalable and extensible?
  • What kind of security and permissions management features are there?
  • How does the system allocate storage and computing resources?
  • Are NLP and machine learning available for autonomous data management?

About the Vendor

Vendor comparison based on requirements is a crucial component of the software selection process. Having a conversation with potential vendors can reveal whether or not they satisfy your needs. These conversations may include discussing features and deployment options and questions about how the data warehouse software and the vendor can benefit your business.

  • What are the available pricing tiers, and what does each one offer?
  • What kind of customer support does the vendor provide? Does it come at an extra charge?
  • Is the vendor known for data warehouse solutions?
  • What additional modules or features are available, and are they necessary?
  • What kind of support or training will the company provide during and after implementation?
  • How do they ensure successful user adoption?
  • Are training options, documentation and learning resources available? Are they free and easy to use?
  • How closely does the vendor consider user requests and feedback for feature improvement when releasing updates?
  • Do they offer a free trial or personalized product demo?
  • Does the vendor have an SLA that lays out the metrics for measuring successful product delivery?

In Conclusion

Choosing the right data warehouse solution is a task that requires careful research and consideration. This buyer’s guide is meant to serve as a starting point for IT professionals tasked with making this decision.

Product Comparisons

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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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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