Want to cut right to the case? The best big data tool right now is Zoho Analytics.
Big data tools are necessary to compute, capture, manage, and process large and complex data sets. Traditional data software does not have the ability to manage complex data, so big data tools are becoming increasingly popular. Big data tools are able to provide complex data analysis, data integration, and data visualization.
If your business is in need of a big data tool to compute and process complex data, there are many options available based on your needs and capabilities. Here are 14 of the best big data tools available. In order to help you make a knowledgeable decision, the pricing and pros and cons are included with each description.
Top 14 Best Big Data Tools
- Zoho Analytics – Best Big Data Tool Overall
- Apache Cassandra – Best Free Big Data Tool
- OpenRefine – Best Big Data Tool for Cleaning Data
- Plotly – Best Big Data Tool for Data Presentation
- Rapidminer – Best Big Data Tool for Large Enterprises
- Tableau – Best Big Data Tool for Data Visualization
- Apache Hadoop – Best Big Data Tool for Parallel Processing
- Apache Flink – Best Big Data Tool for Real-Time Data
- Cloudera – Best Big Data Tool for Cloud-Based Users
- Apache Storm – Best Big Data Tool for Unbound Streams of Data
- SkyTree – Best Big Data Tool for Mid-Sized Companies
- Apache Spark – Best Big Data Tool for Cluster Computing
- MongoDB – Best Big Data Tool for Data Storage
- Hevo – Best Big Data Tool for Automation
1. Zoho Analytics – Best Big Data Tool Overall
Pricing:
- Basic – $24/month (2 users)
- Standard – $48/month (5 users)
- Premium – $115/month (15 users)
- Enterprise – $455/month (50 users)
Zoho Analytics is used by over 13,000 companies, including hp, Hyundai, Ikea, and Peta. Zoho offers the ability to work remotely and use mobile apps with ease. Pricing is based on users and rows, so it is perfect for small businesses looking for a tool to manage their data. With Zoho Analytics, you are able to engage in interactive reports, customize your view within the drag and drop interface, collaborate with other users, and generate automated insights.
| Pros | Cons |
| Affordable compared to competitors | Limited reporting features |
| Intuitive | Lengthily set-up |
| Zoho apps | |
| Google Workspace and Office 365 integration | |
| Mobile apps | |
| Easy to use | |
| Detailed reports | |
| Free trial |
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2. Apache Cassandra – Best Free Big Data Tool
Pricing: Free
Apache Cassandra was initially released in 2008 and is trusted by Apple, BestBuy, eBay, and IBM. Cassandra is user-friendly, free, and can quickly sift through large amounts of data with ease. It also has the ability to restore deleted data and backup data, so you can guarantee that no data will be lost. For a free tool, Apache Cassandra is excellent.
| Pros | Cons |
| Free to download and use | Difficult scalability |
| Quick start installation guide | |
| Data replication | |
| Fast | |
| Multiple program languages | |
| Handles massive amounts of data easily | |
| Data restore and backup |
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3. OpenRefine – Best Big Data Tool for Cleaning Data
Pricing: Free
OpenRefine, founded in 2010, is used and trusted by the University of California. It is free to download and use and has the ability to handle and explore large data sets. It also can clean data, which is an ability that most competitors lack.
| Pros | Cons |
| Free to use and download | Not as user friendly compared to competitors |
| Able to clean data | |
| Handles Large data sets | |
| Data importing formats | |
| Explore large data sets easily and quickly | |
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4. Plotly – Best Big Data Tool for Data Presentation
Pricing:
- Free version
- Dash Enterprise VPC – Contact for pricing
- Dast Enterprise On-Premises – Contact for Pricing
Plotly, founded in 2013, is the choice big data tool used by Citi Bank, Tesla, and Shell. Ploty runs on Python framework that allows you to plot statistical data easily. It is also an extremely visually appealing data platform that allows users to create eye-catching graphs, charts, and reports without needing coding.
| Pros | Cons |
| User friendly | Limited API integration |
| Unlimited dash app users | No mobile apps |
| Cloud based | |
| Advanced analytics | |
| Visually appealing interface | |
| Seamless integration | |
| Multiple users | |
| Data backup |
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5. RapidMiner – Best Big Data Tool for Large Enterprises
Pricing:
- Free edition with limited features (limited to 10,000 rows)
- For pricing, contact for a quote
Founded in 2007, RapidMiner is now used by over 40,000 organizations, including Fidelity, TD Ameritrade, and Land Rover. With seamless integration and predictive analytics, this big data tool will be able to do what you need it to do. However, because the price point is based on rows, it is best for large enterprises.
| Pros | Cons |
| API integration | Interface is lacking in some features |
| Seamlessly integrated | More expensive than competitors |
| Machine Learning | No mobile options |
| Predictive Analytics | Lots of Jargon within the program |
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6. Tableau – Best Big Data Tool for Data Visualization
Pricing:
- Tableau-hosted with Tableau Online
- Creator – $70/month per license
- Explorer – $42/month per license
- Viewer – $15/month per license
- Self-hosted with Tableau Server
- Creator – $70/month per license
- Explorer – $35/month per license
- Viewer – $12/month per license
Tableau, founded in 2003, has over 25,000 companies using their tool, including Verizon, JP Morgan Chase, and Nissan. Tableau can process large amounts of data and conduct a rapid data analysis. Tableau’s engaging and advanced data visualization and graphics are what make it stand out among competitors.
| Pros | Cons |
| Free trial | You have to pay for eLearning |
| Real-time collaboration and analysis | Not user-friendly (requires IT knowledge) |
| Rapid data analysis | More expensive than competitors |
| Can blend multiple data points | No scheduling |
| Excellent integration | Limited custom formatting |
| Can handle a large amount of data | |
| Mobile use |
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7. Apache Hadoop – Best Big Data Tool for Parallel Processing
Pricing: Free to download
Apache Hadoop was first released in 2006 and operates specifically for the Hadoop Distributed File System. It is able to do parallel processing, sending large data sets across computers clusters. While it is limited in some functions, it is one of the only tools to offer parallel processing.
| Pros | Cons |
| Processes large data sets | Lacking real-time processing |
| Detects issues | Not user friendly compared to competitors |
| Java native | Complex integrations |
| Parallel processing capabilities | Slower than other tools |
| No in-memory computing |
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8. Apache Flink – Best Big Data Tool for Real Time Data
Pricing: Free to download
Apache Flink was first released in 2011 and is an open-source data analytic tool. It is highly accurate and has both ML and AI capabilities. Flink’s real-time indicators and alerts outperform competing tools, and it can perform at a large scale accurately.
| Pros | Cons |
| Highly accurate | Lacking API support compared to competitors |
| Layered APIs | Lacking support |
| In-memory computing | Uses raw bytes |
| Real-time analytics | |
| ML capabilities | |
| Large scale performance |
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9. Cloudera – Best Big Data Tool for Cloud-Based Users
Pricing: Contact for Quote – per-node based pricing
Cloudera is the only hybrid cloud-based platform that is built on Hadoop. Cloudera was founded in 2008 and is the big data tool of choice for Mastercard, NYSE, and ADP. With Cloudera, you can easily manage and deploy data across AWS, Google Cloud, and Microsoft Azure.
| Pros | Cons |
| Very secure | More expensive than competitors |
| Real-time insights | No free trial |
| Comprehensive distribution | |
| Easy to use | |
| Excellent documentation |
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10. Apache Storm – Best Big Data Tool for Unbounded Streams of Data
Pricing: Free to download
Apache Storm was first released in 2014 and is now acquired by Twitter. Storm is an open-source tool that is built on REST API. It is fault-tolerant, handling node failures exceptionally well. Apache Storm is also one of the only big data tools that can work with unbound data streams.
| Pros | Cons |
| Free | Not user friendly compared to competitors |
| Fast processing | |
| Real-time processing | |
| Parallel calculations | |
| Massive scalability | |
| Low latency |
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11. SkyTree – Best Big Data Tool for Mid-Sized Companies
Pricing: Contact for pricing
Created in 2010, SkyTree is a professional data software that has both machine learning and artificial intelligence capabilities. It is perfect for mid-sized businesses that desire fraud protection within their data tool abilities. With SkyTree, you only pay for what you need and have access to 24/7 support.
| Pros | Cons |
| Highly scalable | Not user-friendly compared to competitors |
| Fraud detection | No free trial |
| ML mechanisms | |
| Customizable data sets | |
| 24/7 support | |
| Great data visualization | |
| Predictive modeling |
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12. Apache Spark – Best Big Data Tool for Cluster Computing
Pricing: Free to download
Apache Spark, initially released in 2014, is built on Akka. Spark can cache and process data in real-time, compute in-memory calculations and do batch processing. While Spark may not be the most popular dashboard software available, it is best for cluster computing, cluster management, and large data-set processing.
| Pros | Cons |
| Batch processing | Not as secure as others |
| Open-source | Lackin maturity in some features |
| Flexibility | Not user friendly |
| Highly compatible | Poor graphics |
| In-memory calculations | Lacking support |
| Real-time processing | Lacking automation |
| Batch processing | |
| Machine learning | |
| SQL analytics |
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13. MongoDB – Best Big Data Tool for Data Storage
Pricing:
- MongoDB Atlas (Cloud database)
- Dedicated – from $57/month based on stage and RAM needs
- Serverless – from $0.30/million reads
- Shared – from $0/month
- MongoDB Enterprise Advanced (on-promises option)
- Contact sales
MongoDB, founded in 2007, is trusted by many companies, including Bosch, Toyota, eBay, MetLife. MongoDB is a well-optimized NoSQL document database that has high-volume data storage, flexible development, and excellent scalability.
| Pros | Cons |
| Free to use plan is excellent | Limited analytics |
| Easy to learn | High memory usage |
| Fast | Document size limited to 16 MB |
| Secure | |
| Flexible development | |
| Unified query API | |
| Full text search | |
| Excellent scalability | |
| Idiomatic language support |
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14. Hevo – Best Big Data Tool for Automation
Pricing:
- Free – $0/ month (includes 1 million events)
- Starter – Starting at $149/month (for 10 million events) to $999/month (for 300 million events)
- Business – Call for custom pricing
Hevo, founded in 2016, is trusted by MuleSoft, Wheely, Scratchpay, and Slice. Hevo is a fully automated big data tool that can integrate with over 100 data source platforms. It is easy to use, doesn’t require code, and, due to its automation, users can be hands off for most of the processes.
| Pros | Cons |
| 14-day free trial | Plans based on events |
| Unlimited users for every plan | Alerts could be improved |
| No-code | |
| Over 100 data source integrations | |
| Fully automated | |
| Easy to use | |
| Live monitoring and support |
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Frequently Asked Questions
What is big data?
Big data refers to large, complex, and hard to manage amounts of data. As time goes on, the amount of data that companies have access to or need to analyze increases. Data sizes are getting bigger (volume), data is generated faster (velocity), and data comes in different structures (variety). Big data can be structured, semi-structured, or unstructured.
How much data is big data?
Big data is any amount of data over one terabyte.
What is a big data tool and what does it do?
Big data tools are able to mine, compute, manage, and process large and complex data sets. It is getting more difficult for traditional data analyzing tools to manage the complexity of data as it grows. Big data tools can convert big data into data analytics, data integrations, and data visualizations so that your company can easily use and understand the data.
Do I have to know coding to use big data tools?
While it is not necessary to know how to code for some big data tools, you most likely will need to know some coding in order to make use of every function.
Do I need to hire a data analyst to use big data tools?
Some companies may choose to hire a data analyst, especially large enterprises. Smaller companies that don’t process as much big data may not need a data analyst.
How will big data tools benefit my business?
There are many benefits of using big data tools for your business. Big data tools can give a business insight into their consumer’s behaviors, conduct in-depth market analysis, and track social media analytics. It can also track consumer personal and financial information. IT, finance, and insurance companies can use big data tools to alert them to questionable actions or fraud claims. Every piece of information that big data tools can produce for you, you can use to propel growth.