Top 15 Big Data Tools in 2020

Today's market is flooded with an array of Big Data tools. They bring cost efficiency, better time management into the data analytical tasks. Here is the list of best big data tools with their key features and download links.

1) Hadoop:

The Apache Hadoop software library is a big data framework. It allows distributed processing of large data sets across clusters of computers. It is designed to scale up from single servers to thousands of machines.


  • Authentication improvements when using HTTP proxy server
  • Specification for Hadoop Compatible Filesystem effort
  • Support for POSIX-style filesystem extended attributes
  • It offers robust ecosystem that is well suited to meet the analytical needs of developer
  • It brings Flexibility In Data Processing
  • It allows for faster data Processing

2) HPCC:

HPCC is a big data tool developed by LexisNexis Risk Solution. It delivers on a single platform, a single architecture and a single programming language for data processing.


  • Highly efficient accomplish big data tasks with far less code.
  • Offers high redundancy and availability
  • It can be used both for complex data processing on a Thor cluster
  • Graphical IDE for simplifies development, testing and debugging
  • It automatically optimizes code for parallel processing
  • Provide enhance scalability and performance
  • ECL code compiles into optimized C++, and it can also extend using C++ libraries

3) Storm:

Storm is a free and open source big data computation system. It offers distributed real-time, fault-tolerant processing system. With real-time computation capabilities.


  • It benchmarked as processing one million 100 byte messages per second per node
  • It uses parallel calculations that run across a cluster of machines
  • It will automatically restart in case a node dies. The worker will be restarted on another node
  • Storm guarantees that each unit of data will be processed at least once or exactly once
  • Once deployed Storm is surely easiest tool for Bigdata analysis

4) Qubole:

Qubole Data is Autonomous Big data management platform. It is self-managed, self-optimizing tool which allows the data team to focus on business outcomes.


  • Single Platform for every use case
  • Open-source Engines, optimized for the Cloud
  • Comprehensive Security, Governance, and Compliance
  • Provides actionable Alerts, Insights, and Recommendations to optimize reliability, performance, and costs
  • Automatically enacts policies to avoid performing repetitive manual actions

5) Cassandra:

The Apache Cassandra database is widely used today to provide an effective management of large amounts of data.


  • Support for replicating across multiple data centers by providing lower latency for users
  • Data is automatically replicated to multiple nodes for fault-tolerance
  • It is most suitable for applications that can't afford to lose data, even when an entire data center is down
  • Cassandra offers support contracts and services are available from third parties

6) Statwing:

Statwing is an easy-to-use statistical tool. It was built by and for big data analysts. Its modern interface chooses statistical tests automatically.


  • Explore any data in seconds
  • Statwing helps to clean data, explore relationships, and create charts in minutes
  • It allows creating histograms, scatterplots, heatmaps, and bar charts that export to Excel or PowerPoint
  • It also translates results into plain English, so analysts unfamiliar with statistical analysis

7) CouchDB:

CouchDB stores data in JSON documents that can be accessed web or query using JavaScript. It offers distributed scaling with fault-tolerant storage. It allows accessing data by defining the Couch Replication Protocol.


  • CouchDB is a single-node database that works like any other database
  • It allows running a single logical database server on any number of servers
  • It makes use of the ubiquitous HTTP protocol and JSON data format
  • Easy replication of a database across multiple server instances
  • Easy interface for document insertion, updates, retrieval and deletion
  • JSON-based document format can be translatable across different languages

8) Pentaho:

Pentaho provides big data tools to extract, prepare and blend data. It offers visualizations and analytics that change the way to run any business. This Big data tool allows turning big data into big insights.


  • Data access and integration for effective data visualization
  • It empowers users to architect big data at the source and stream them for accurate analytics
  • Seamlessly switch or combine data processing with in-cluster execution to get maximum processing
  • Allow checking data with easy access to analytics, including charts, visualizations, and reporting
  • Supports wide spectrum of big data sources by offering unique capabilities

9) Flink:

Apache Flink is an open-source stream processing Big data tool. It is distributed, high-performing, always-available, and accurate data streaming applications.


  • Provides results that are accurate, even for out-of-order or late-arriving data
  • It is stateful and fault-tolerant and can recover from failures
  • It can perform at a large scale, running on thousands of nodes
  • Has good throughput and latency characteristics
  • This big data tool supports stream processing and windowing with event time semantics
  • It supports flexible windowing based on time, count, or sessions to data-driven windows
  • It supports a wide range of connectors to third-party systems for data sources and sinks

10) Cloudera:

Cloudera is the fastest, easiest and highly secure modern big data platform. It allows anyone to get any data across any environment within single, scalable platform.


  • High-performance analytics
  • It offers provision for multi-cloud
  • Deploy and manage Cloudera Enterprise across AWS, Microsoft Azure and Google Cloud Platform
  • Spin up and terminate clusters, and only pay for what is needed when need it
  • Developing and training data models
  • Reporting, exploring, and self-servicing business intelligence
  • Delivering real-time insights for monitoring and detection
  • Conducting accurate model scoring and serving

11) Openrefine:

Open Refine is a powerful big data tool. It helps to work with messy data, cleaning it and transforming it from one format into another. It also allows extending it with web services and external data.


  • OpenRefine tool help you explore large data sets with ease
  • It can be used to link and extend your dataset with various webservices
  • Import data in various formats
  • Explore datasets in a matter of seconds
  • Apply basic and advanced cell transformations
  • Allows to deal with cells that contain multiple values
  • Create instantaneous links between datasets
  • Use named-entity extraction on text fields to automatically identify topics
  • Perform advanced data operations with the help of Refine Expression Language

12) Rapidminer:

RapidMiner is an open source big data tool. It is used for data prep, machine learning, and model deployment. It offers a suite of products to build new data mining processes and setup predictive analysis.


  • Allow multiple data management methods
  • GUI or batch processing
  • Integrates with in-house databases
  • Interactive, shareable dashboards
  • Big Data predictive analytics
  • Remote analysis processing
  • Data filtering, merging, joining and aggregating
  • Build, train and validate predictive models
  • Store streaming data to numerous databases
  • Reports and triggered notifications

13) DataCleaner:

DataCleaner is a data quality analysis application and a solution platform. It has strong data profiling engine. It is extensible and thereby adds data cleansing, transformations, matching, and merging.


  • Interactive and explorative data profiling
  • Fuzzy duplicate record detection
  • Data transformation and standardization
  • Data validation and reporting
  • Use of reference data to cleanse data
  • Master the data ingestion pipeline in Hadoop data lake
  • Ensure that rules about the data are correct before user spends thier time on the processing
  • Find the outliers and other devilish details to either exclude or fix the incorrect data

14) Kaggle:

Kaggle is the world's largest big data community. It helps organizations and researchers to post their data & statistics. It is the best place to analyze data seamlessly.


  • The best place to discover and seamlessly analyze open data
  • Search box to find open datasets
  • Contribute to the open data movement and connect with other data enthusiasts

15) Hive:

Hive is an open source-software big data too. It allows programmers analyze large data sets on Hadoop. It helps with querying and managing large datasets real fast.


  • It Supports SQL like query language for interaction and Data modeling
  • It compiles language with two main tasks map, and reducer
  • It allows defining these tasks using Java or Python
  • Hive designed for managing and querying only structured data
  • Hive's SQL-inspired language separates the user from the complexity of Map Reduce programming
  • It offers Java Database Connectivity (JDBC) interface

Readmore: What is BIG DATA? Introduction, Types, Characteristics & Example