10 Best Big Data Analytics Tools List
Here's my pick of the 10 best software from the 33 tools reviewed.
Our one-on-one guidance will help you find the perfect fit.
With so many different big data analytics tools available, figuring out which is right for you is tough. You know you want to efficiently leverage complex data to inform strategic decisions but need to figure out which tool is best. I've got you! In this post I'll help make your choice easy, sharing my personal experiences using dozens of different big data analytics software with various large datasets, with my picks of the best big data analytics tools.
What Are Big Data Analytics Tools?
Big data analytics tools are software that process, analyze, and extract meaningful insights from large and complex sets of data. These tools handle vast amounts of structured and unstructured data, utilizing advanced techniques like machine learning, predictive analytics, and data mining to reveal patterns, trends, and relationships.
The benefits and uses of big data analytics tools include enabling data-driven decision-making, enhancing business intelligence, and providing deep insights into customer behavior, market trends, and operational efficiencies. They empower organizations to anticipate future trends, identify new opportunities, and optimize processes. By leveraging big data analytics, businesses can gain a competitive advantage, innovate more effectively, manage risks better, and ultimately drive growth and success.
Overviews Of The 10 Best Big Data Analytics Tools
Here’s a brief description of each big data analytics platform on my list, showcasing what it does best, plus screenshots to showcase some of the features.
Supermetrics lets marketers consolidate data from different sources, store it in their favorite reporting tool, and transform it for reporting and analysis. It assists big data analytics tools by integrating data from over 150 platforms and making it analysis-ready for various reporting and analytics tools. It supports data storage solutions like data warehouses, enabling businesses to store and structure large, complex datasets.
Supermetrics offers a 14-day free trial and pricing starting at $29 (billed annually).
Pros and cons
Pros:
- Offers a wide range of integrations
- Customizable reports
- Offers automated data movement
- Provides scheduled data refreshes
Cons:
- Steep learning curve for advanced features
- Some scalability issues
- Limited data transformation
Zoho Analytics is a self-service BI and analytics software used by the likes of Hyundai, Ikea, HP, and Philips. Their freemium plan is a bit feature lite but you can add up to 2 users, input up to 10K rows/records, and access unlimited reports and dashboards. This is a pretty sturdy offering for free-to-use data analysis. Zoho Analytics comes with a library of pre-built visualizations divided by function (social media, finance, IT, sales) to help you get started.
Zoho Analytics costs from $24/month for 2 users and offers a free 15-day trial. They also have a free plan for 10K rows/records or less.
Pros and cons
Pros:
- Feature expansion through connection with Zoho’s other apps
- Generate reports right from SQL queries
- Excellent embedded AI feature (called ZIA)
- Building or customizing reports and dashboards is super easy
Cons:
- Dashboards seem a bit cramped and busy
- Cannot auto export data straight to Google Drive
- Hourly data sync not included in entry level plan
Tableau is a user-friendly, intuitive visual analytics platform with built-in best practices for data exploration and informational storytelling. Users can access their full suite of self-service prep and analytics tools with a minimal learning curve, leveraging drag-and-drop visualizations and easy point-and-click AI-driven statistical modeling. Most users should be able to assemble data to their liking without advanced programming or special commands.
Tableau costs from $70/user/month and offers a free 14-day trial.
Pros and cons
Pros:
- Easy to use with self-learning module available
- Offers a hearty variety of chart types (Sankey, Doughnut, Maps)
- Comes with robust mobile app for iOS and Android
- Good native integration with Salesforce CRM
Cons:
- Frequently requires saved database connections to be re-authenticated
- Limited room for columns when assembling worksheets
- Some data manipulation required in order to successfully match queries
Splunk is currently used by 91 of the Fortune 100 companies, including Intel, Comcast, and Coca-Cola. Splunk offers machine learning-centric visibility and detection of entity profiling and scoring, risk behavior detection, anomaly observation, and high fidelity behavior-based alerts. You can access a free cloud-based sandbox trial of Splunk UBA to check it out before committing. They offer dedicated solutions to DevOps, Security, IT, and big data.
Splunk costs from $2000/year for 1 GB/day and offers a free plan that allows you to index only 500 MB/day.
Pros and cons
Pros:
- Can set up detailed, specific alerts for various KPIs
- Search queries can be saved for repeat use or converted into apps
- Quick log queries across different types of infrastructure
- Flexible data and report sharing using URL links
Cons:
- Steep learning curve compared to others
- Query builder may be prohibitive for non-technical users
- Infrastructure maintenance requires more manpower than some competitors
GoodData is a big data analytics platform that provides users the tools, runtimes, and storage for data ingestion, preparation, transformation, and analytic queries. They boast 50+ connectors for data ingestion/synchronization and offer an Agile data warehousing system on higher tier plans. Their per-workspace pricing model lets unlimited users access sets of data models, metrics, calculations, and dashboards according to a flexible permissions system.
GoodData costs from $20/workspace/month and offers a free demo. They also have a free plan that includes 5 workspaces and up to 100 MB/workspace.
Pros and cons
Pros:
- Provides easy linking of disparate data sources for comparison
- Good for scheduling reports according to exact times and frequencies
- Excellent integration with Salesforce, Pardot, Zendesk
- Non-technical users can build dashboards and views easily
Cons:
- Some data model adjustments might require customer support
- Datasets of 100M+ rows may stall performance
- Coding knowledge required for inquiries and report building
Arcadia Data scored first place in the 2018 Big Data Analytics Market Study by Dresner Advisory Service report among 17 other BI vendors. Their in-data-lake BI architecture offers a drag-and-drop web-based interface, an in-cluster analytics engine that scales linearly for ease of management, and embedded analytics for Hadoop and Cloud. Telecom companies will enjoy their behavioral churn analysis, service cost controls, and impact of infrastructure reports.
Arcadia Enterprise offers customized pricing upon request. They also have Arcadia Instant, a freemium version of their tool whereby processing is done on your computer rather than on a server cluster.
Pros and cons
Pros:
- Handy scheduled mail reporting features
- Smooth, intuitive interface for data connections and dashboards
- Freemium tool is very accessible and great to test the software
Cons:
- A steep learning curve for IoT analytics and ingest functionality
- No mobile app available at this time
- Poor integration with Hortonworks Data Platform
IBM Cloud Pak for Data is a fully-integrated, cloud native, data and AI platform designed for sophisticated DataOps and business analytics solutions. IBM boasts a potential for a 25-65% reduction in extract, transform, load (ETL) requests by eliminating the complexities of data integration of different data types and structures using Cloud Pak for Data. You can customize your workflow using their flexible API and complimentary proprietary and third-party services.
IBM Cloud Pak for Data costs from $800/month and offers a 7-day free trial.
Pros and cons
Pros:
- Tailored solutions for lessening your ETL request load
- Good for optimizing storage and other maintenance of preexisting data
- Award-winning data security solutions
Cons:
- Their Db2 database has a bit of a clunky, old-fashioned feel
- May be cost prohibitive for smaller enterprises
- Could use more options to better migrate data from other cloud providers to IBM
Qlik Sense is an end-to-end data analytics platform with a unique associative analytics engine that lets users search and explore across all data in any direction with no pre-aggregated data or predefined queries to limit you. Purchasing departments will get the most use out of in-depth supplier and industry trend comparisons, easy currency filters for international partners, and low product or low spend reports.
Qlik Sense costs from $30/user/month and offers a free 30-day trial.
Pros and cons
Pros:
- Low learning curve for self-service
- Easy to reuse code or query logic for time saving
- Incorporated data modeling for “no-warehouse” options
- Thorough and quick search functionality
Cons:
- Low res monitors may struggle to clearly display
- Minimal non-interactive report creation options
- Not as customizable as others, like Tableau
With SAS Visual Analytics, users are able to easily import data from databases, Hadoop, Excel spreadsheets, and social media. They offer a huge variety of interactive visualizations, including bar and pie charts, heat maps, animated bubble charts, vector maps, numeric series, tree maps, network diagrams, correlation matrix, forecasting, decision trees, and more. Plus they have ease-of-use options like one-click filtering and automated content linking.
SAS Visual Analytics costs from $8000/year and offers a free 14-day trial.
Pros and cons
Pros:
- Quality BI dashboards can be accessed across many devices
- Works with tens of millions of records without lagging
- Well-suited to support high volume of simultaneous users
- Flexible drag-and-drop analytics elements
Cons:
- May be price prohibitive compared to others on this list
- Could use better HTML5 support
- Low number of connection options with third-party apps
Azure Data Lake Analytics is an on-demand analytics job service that prices per-job, ensuring that you only pay for the processing as you use it. This tool can process petabytes of data for business intelligence (BI) as well as sentiment analysis. You’re left with high-impact visualizations of your relational source data, such as Azure SQL Database and Azure Synapse Analytics.
Azure Data Lake Analytics costs from $1 per 1,000 runs or $0.25/DIU-hour and scales according to your use case.
Pros and cons
Pros:
- Only pay for consumed ADLUs
- Works well with power BI services for reporting
- Complimentary storage of relational database and NoSQL
- Simple solution for batch workloads
Cons:
- Lacks resources for end-user training
- May be confusing for users coming from a primarily MSBI background
- Lack of streaming option and event processing
The Best Big Data Analytics Tools Summary
Tools | Price | |
---|---|---|
Supermetrics | Pricing upon request | Website |
Zoho Analytics | From $24/user/month (billed annually) | Website |
Tableau | From $70/user/month (billed annually) | Website |
Splunk Enterprise | From $150/user/month (billed annually) | Website |
GoodData | From $1000/month | Website |
Arcadia Enterprise | No price details | Website |
IBM Cloud Pak for Data | From $95/user/month (billed annually) | Website |
Qlik Sense | From $30/user/month | Website |
SAS Visual Analytics | Pricing upon request. | Website |
Azure Data Lake Analytics | $1 per 1,000 runs or $0.25/DIU-hour | Website |
Compare Software Specs Side by Side
Use our comparison chart to review and evaluate software specs side-by-side.
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Other Big Data Analytics Tools
Here’s a few more that didn’t make the top list.
How Is Big Data Analyzed?
To put it simply: Big data is analyzed by collecting structured semi-structured and unstructured data from your data lakes and parsing out what's most relevant to your current informational need most likely using some form of data quality automation to do so.
Then, you leverage statistics and machine learning to parse through the data ecosystem and compile predictive analytics, user behavior analytics, and other metrics. This process might also include text analytics, natural language processing, predictive analytics, and so forth.
All of this works to create end reports that are readable and actionable for business users.
Big Data Analytics Tools Comparison Criteria
Here’s a summary of my evaluation criteria:
- User Interface (UI): Does the software convey large, complex data sets stemming from myriad sources in an easy to understand, intuitive, and efficient way? Can users reasonably find their way around the large-scope data technologies?
- Usability: Big data analysis comes in many shapes and does many things—does the big data software offer use case-specific tutorials, training resources, and tech support? Is the full functionality of the tool manageable for motivated data science experts?
- Integrations: Big data analytics tools must connect to an assortment of common and uncommon data stores—Hive, Oracle, Azure, Google Cloud, and social media. There are some must-haves; for example, easy connectors with Amazon Web Services (AWS).
- Value for $: Pricing of big data processing solutions must be scalable according to the amount of data, number of data warehouses, artificial intelligence capabilities, and other metrics. Are all costs fair, transparent, and flexible?
Big Data Analytics Tools Key Features
- Inclusive of a variety of programming models, like MapReduce, Message Passing, Directed Acyclic Graph, Workflow, SQL-like, and Bulk Synchronous Parallel
- Statistical algorithms and what-if analysis
- Flexible programming language accommodations (ex. SQL and NoSQL, Java, Python)
- A streamlined, interactive application programming interface software (APIS)
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Which Big Data Analytics Tools Have You Used?
What do you think about this list of business intelligence and big data analysis tools? What data management tools do you use for your business analytics on a day to day basis? Do you have a big data platform in mind that you would add to this list if you could? What big data visualization tools are your "must-haves" on-premise or in the cloud? Let us know in the comments section.
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