In the world of Information Technology, ‘data’ is everything. Moreover, every day, this data continues to grow exponentially. In the past, discussions would be on kilobytes and megabytes, but today the talk is about terabytes of data. Data has no value until it is transformed into knowledge and information that can help management make decisions. Top big data software is readily available as open source on the market for this purpose. This software facilitates the storage, analysis, reporting, and other uses of data. Read more to know about the best 10 open source big data tools:

Exactly how is big data analyzed?

Big data analysis involves gathering structured, semi-structured, and unstructured data from your data warehouse. And helping to determine what is most pertinent to your present informational requirement. This process is automated to ensure data quality. After that, you apply machine learning and statistics. Mainly it is to analyze the data environment and create metrics such as user behavior analytics and predictive analytics. Additionally, this procedure may involve text analytics, natural language processing, predictive analytics, and other techniques. All of these factors combine to produce final reports that business users find readable and actionable.


Cassandra is an open-source and free database management tool. It is a NoSQL database management system. It is primarily intended to hold and handle enormous amounts of data dispersed across numerous servers. Due to their scalability and user requirements, databases are primarily used by many businesses and individuals around the world. Since there is no single point of failure in its architecture, it performs effectively even under large workloads.

Key features

  • For fault tolerance, the data is automatically replicated into multiple nodes.
  • It is one of the best big data tools and is most suitable for applications that can’t afford to lose data, even when an entire data center is down.  
  • It supports replicating across multiple data centers by lowering latency for users. Cassandra offers support agreements, and third parties can provide services.


MongoDB is a document-oriented NoSQL database that was created in JavaScript and C, C++. It is an open-source program that is free to use and supports many different operating systems, including Linux, Solaris, FreeBSD, Windows Vista (and later versions), and OS X (10.7 and later versions). Its key features include aggregation, replication, ad hoc searches, the use of the BSON format, indexing, and sharding. Its feature includes server-side javascript execution, capped collections, load balancing, the MongoDB management service (MMS), and file storage are some of its key features.

Key features

  • The majority of other languages that are used with MySQL are fairly compatible with it.
  • Due to its frequent updates, it provides tighter security measures.
  • Improved storage engines are offered.
  • Ii is more effective and has improved performance.


It is an open-source NoSQL document database that mainly works by storing and collecting data in JSON-based formats to store data. It stores HTTP as an API and JavaScript as a querying language. Unlike another relational dataset, this tool used a scheme-free data model to simply record management across web browsers, computing devices, and mobile phones. It works well with a mobile application and modern web. It allows the capacity of distribution of data more effectively using incremental replication.

Key features

  • It is a single-node database that works like any other database.
  • Across multiple server instances, you can replicate data easily.
  • It is one of the big data processing tools that will allow running a single logical database server on multiple numbers of servers.
  • Easy interface for updates, document insertion, deletion, and retrieval.
  • It makes use of JSON data format and the ubiquitous HTTP protocol.
  • JSON-based document format can be translated into various languages.


HPCC is abbreviated as High-Performance Computing Cluster. This is the complete big data solution for a highly scalable supercomputing platform. HPCC is also referred to as Data Analytics Supercomputer (DAS). This tool is written in a data-centric C++ programing language known as ECL (Enterprise Control Language). It is based on Thor architecture that supports pipeline parallelism, supports data parallelism, and system parallelism.  

Key features

  • Helps in parallel data software.
  • Follows on commodity hardware and runs on commodity hardware.
  • Comes with binary packages supported for Linux distributions and support end-to-end big data workflow management.
  • Implicitly extensible and highly optimized parallel engine.
  • Maintains data encapsulation and codes and helps to build graphical execution plans.
  • It compiles into native machine code and C++


Apache storm is a distributed stream processing, cross-platform, fault-tolerant real-time computational framework. It’s written in Java and Clojure. Its architecture is based on bolts and customized spouts to describe manipulations and sources of information in order to permit batch, distributed processing of unbounded data streams.

Key features

  • It has big data tools and technologies that use parallel calculations that run across a cluster of machines.
  • Storm guarantees that each unit of data will be processed exactly once or at least once.
  • It will automatically restart in case of any node dies. The worker will be restarted to some other node. Once deployed, this tool is surely the easiest tool for big data analysis.


SAMOA stands for Scalable Advanced Massive Online Analysis (SAMOA) and is used for mining big data streams with a special emphasis on machine learning. It also supports Write Once Run Anywhere (WORA) architecture which allows seamless integration of various distributed stream processing engines into a framework. It allows the development of new machine learning algorithms while avoiding and complexity of dealing.

Key feature

  • Write Once Run Anywhere architecture.
  • True real-time streaming.
  • Fast and scalable, along with simple and fun to use.


OpenRefine is one of the powerful tools which is widely used for data cleansing and transforming it into many formats. It works very smoothly with a large database. It is used with external data and extended web services. OpenRefine always keeps the data privately in your system and also allows you to share it among other team members.

Key features

  • You can store data in various types of formats.
  • It handles cells of the table with multiple data values, and it performs cell transformation.
  • Within a matter of seconds, you can explore multiple data values.
  • You can also extend your datasets to various web services.

8. Dataddo

Dataddo is a cloud-based ETL and no coding platform that puts flexibility at first. It has a wide range of connectors and the ability to choose your own attributes and metrics. The tool makes creating stable data pipelines fast and simple. You don’t want to add elements to your architecture that you are not using anyway. Because Dataddo seamlessly plugs into your existing data stack. Dataddo’s quick set-up and intuitive interface let you focus on integrating your data. It reduces wasting time on learning how to use another platform.

Key features

  • It’s more friendly for non-technical users with a simple interface.
  • In users existing data stack, you can use flexible plugs.
  • You can create data pipelines within minutes of account creation.
  • Within 10 days from request the new connectors can be added.
  • Customizable metrics and attributes when creating solutions.
  • No-maintenance charges for API managed by the Dataddo team.
  • Central management system to track all the data pipelines simultaneously.


Big data technologies are available in Pentaho to extract, prepare, and combine data. It provides analytics and visualizations that transform how any business is run. With the help of this big data tool, large data can provide huge insights.

Key Features

  • Data integration and access for efficient data visualization
  • It is a big data tool that allows users to create large data at the source and stream it for precise analytics.
  • To achieve the most processing, seamlessly switch between or combine data processing with in-cluster execution.
  • Make analytics, including charts, visualizations, and reports, easily accessible for data checking.
  • Supports a variety of big data sources by providing special abilities.


Datawrapper is an open-source data visualization tool that enables users to quickly create straightforward, accurate, and embeddable charts. The majority of its clients are newsrooms located all around the world. The Times, Fortune, Mother Jones, Bloomberg, Twitter, and other brands are just a few of them. Device-friendly is a pro. All types of devices, including mobile, tablet, and desktop, operate really well.

Key features

  • Completely receptive and fast
  • Interactive and consolidates all the charts in one location.
  • Excellent export and customization possibilities.
  • Necessitates no code knowledge.
  • It has a very simple user-friendly interface.

Bottom Line

The use of big data analytics is becoming more commonplace throughout the world. It is incorporated into a variety of sectors, including government, healthcare, and the financial services industry. The mainframe for implementing big data is made up of open-source big data tools. Before choosing any database administration tool, it’s important to have a solid understanding of the many open-source solutions available.