10 Best Big Data Analytics Tools List
Here's my pick of the 10 best software from the 34 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.
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
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
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
Observable is designed for developers and data teams aiming to create and deploy interactive data visualizations and applications. It combines open-source flexibility with deployment capabilities, allowing users to efficiently prototype, build, and share data products. Observable stands out for its unique feature of embedded analytics, which lets you integrate interactive data visualizations right into your applications. This is especially important for big data analytics because it means you can share insights and data stories in a way that’s easy for others to understand and interact with.
Observable offers a free plan as well as paid plans with access to free trials and demos.
Pros and cons
Pros:
- Helps create readable data products
- Supports advanced JavaScript features
- Supports intricate visualizations
Cons:
- Potential performance problems when working with large datasets
- Sharing data analysis with the public can challenging
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
Azure Databricks is a data analytics tool optimized for Microsoft’s Azure cloud services solution. It provides three development environments for data-intensive apps, namely Databricks SQL, Databricks Machine Learning, and Databricks Data Science & Engineering.The platform supports languages including Python, Java, R, Scala, and SQL, plus data science frameworks and libraries including TensorFlow, scikit-learn, and PyTorch.Databricks offers pay-as-you-go pricing based on your computer usage, or prepaid packages starting from $23,500 per year. Pros:Highly versatile,More powerful than comparable AWS and Google tools, Supports multiple languages, Cons:Expensive for smaller teams/projects, Dashboards and visualizations could be better
Qrvey is an embedded analytics platform used for SaaS data, analytics, and automation technologies. You can deploy it right into your pre-existing AWS account in order to visualize your entire data pipeline. Their start-ups package includes specialized support for pre-launch or early-launch companies, like quick installation and launch, serverless analytics scalability, no-code embedded widgets, up to 10 GB data, and a lower entry subscription price point.
Qrvey costs from $2500/year and offers both a free demo and a free trial.
Pros and cons
Pros:
- Unlimited users and API calls for ever plan tier
- Ability to embed a chart into your own web app without iFrames
- Good out-of-the-box workflows/automation tool
Cons:
- More chart types would be welcomed
- Few online resources available for self-help
- Unlimited data limited to highest tier subscription plan
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
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
The Best Big Data Analytics Tools Summary
Tools | Price | |
---|---|---|
Zoho Analytics | From $24/user/month (billed annually) | Website |
Supermetrics | Pricing upon request | Website |
Tableau | From $70/user/month (billed annually) | Website |
Splunk Enterprise | From $150/user/month (billed annually) | Website |
Observable | From $22/editor/month (billed annually) | Website |
GoodData | From $1000/month | Website |
Azure Databricks | $23,500/year | Website |
Qrvey | From $2500/year | Website |
Qlik Sense | From $30/user/month | Website |
Arcadia Enterprise | No details | 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.
- Azure Data Lake Analytics
Pay-per-job big data solution
- SAS Visual Analytics
Big data analytics tool with smart visualizations
- IBM Cloud Pak for Data
For reducing ETL requests
- DNIF Security Information & Event Management (SIEM)
Event log management
- Semrush
Big data analytics for ease of use + accessibility
- Talend
Data integration with governance
- Cloudera
Industrialized enterprise AI
- Sisense
API-first cloud technology
- iceDQ
For dataops testing and monitoring
- Bizintel360
For analytics without programming knowledge
- Qubole
For openness and data workload flexibility
- Azure Databricks
For Microsoft Suite users
- Altamira Lumify
For link analysis
- CloudMoyo
For CIOs and CTOs
- DNIF Big Data Analytics
Event log management
- Exasol
For retail data analytics
- MATLAB
Iterative analysis and design processes
- Hortonworks
Open source framework for distributed storage
- Accelerite ShareInsights
Collaborative rapid insight prototyping
- Omniscope EVO
For Chrome browser users
- Deep.BI
For e-commerce and banking
- Jethro
For 1000+ concurrent users
- Plotly
To productionize Python analytics
- Apache Spark
Open-source big data analytics tool (with Apache Hadoop)
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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