Data is everywhere, from the moment you wake up in the morning to the moment you go to bed at night. This article will show you the top ten open-source big data tools which do this job well. These tools are useful in identifying patterns and managing large data sets.

The amount of data that can be obtained from IoT and mobile technology has increased dramatically. However, it is equally important to extract insights from it. Particularly if your organization wants to get the attention of your customers.

So, how do organizations harness big data, the quintillion bytes of data?

If you’re interested in joining the big data industry, These open source big data tools will help you to be more productive.

1. Hadoop

Even if this is your first introduction to Hadoop, it’s likely that you have read many times about the topic. Because it can send data to multiple servers, Hadoop is a popular tool for large-scale data analysis. Hadoop can also be used on a cloud infrastructure.

This open-source software framework can be used when data volumes exceed the available memory. This large data tool is ideal for data exploration and filtration, sampling, summarization, and summarization. It is made up of four parts.

  • Hadoop Distributed File System: This file system, also known as HDFS or distributed file system, is compatible with a very large bandwidth.
  • MapReduce This refers to a programming method for big data processing.
  • YARN All Hadoop’s infrastructure resources are scheduled and managed using this platform.
  • Libraries: They allow other modules to interact with Hadoop efficiently.

Also read: Top 11 Data Preparation Tools And Software

2. Apache Spark

Apache Spark is the next big thing in big data tools. Apache Spark is an open-source big data tool that fills in the Hadoop gaps when it comes to data processing. This big data tool is preferred over other types of programs for data analysis because it can store large computations in memory. It also has the ability to run complex algorithms, which is essential for handling large data sets.

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Apache Spark can handle real-time and batch data and is flexible enough to be used with OpenStack Swift, Apache Cassandra, and HDFS. Often used as an alternative to MapReduce, Spark is 100 times faster than Hadoop MapReduce.

3. Cassandra

Apache Cassandra is a big data tool that processes structured data sets and is among the most popular. Apache Software Foundation created it in 2008. is the most widely-used open-source big data tool to scale. This big data tool is more important for big data applications because it has been proven to be tolerant of cloud infrastructure and commodity hardware.

It offers features that are unmatched by any other NoSQL or relational database. These include simple operations, cloud availability points, and performance as well as continuous availability of data sources. Apache Cassandra has been used by huges such as Twitter and Cisco.

For more information about Cassandra, please visit the ” Cassandra tutorial” for key techniques.

4. MongoDB

MongoDB is a great alternative to traditional databases. Businesses that require fast, real-time information to make quick decisions are well-suited for a document-oriented database. It uses documents and collections to replace rows and columns, which is what sets it apart from traditional databases.

It can store data in documents and is flexible, so companies can easily adapt it. It can store any type of data, including integers, strings, Booleans, and arrays as well as objects. MongoDB is simple to use and supports multiple platforms and technologies.


High-Performance Computing Cluster (or HPCC) is a competitor to Hadoop in big data markets. It is an open-source tool for big data under the Apache 2.0 License. It was developed by LexisNexis risk solution and released to the public in 2011. It uses a single platform and architecture for data processing, as well as a single programming language.

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HPCC is the best big data tool if you need to do big data tasks quickly and with little code. It optimizes code for parallel processing and offers enhanced performance. Its uniqueness lies within its lightweight core architecture that provides near-real-time results without the need for large-scale development teams.

6. Apache Storm

It’s a free, open-source big data computation system. It’s one of the most powerful big data tools, offering a distributed, real-time, fault-tolerant, and reliable processing system. It has the ability to process one million 100-byte messages per second by nodes.

This tool also uses big data technologies and parallel calculations that can be run across multiple machines. It is a robust, open-source, flexible, and reliable choice for medium and large-scale companies. It ensures data processing, even if messages are lost or nodes die.

7. Apache SAMOA

Scalable Advanced Massive Online Analysis is an open-source platform for mining large data streams, with a particular emphasis on machine learning. It supports Write Once Run Anywhere (WORA) architecture that allows for seamless integration of multiple stream processing engines in the framework. This allows for the creation of machine-learning algorithms and avoids the complexity of dealing with distributed stream processing engines such as Apache Storm, Flink, or Samza.

Also read: Top 6 Open Source Data Modeling Tools and Software

8. Atlas.ti

This big data analysis tool allows you to access all platforms from one location. This tool can be used for qualitative and mixed data analysis in business, academia, and user experience research. This tool can export data from all data sources. This tool allows you to work with your data seamlessly and allows you to rename a Code in Margin Area. It can also help you manage projects that contain countless documents or coded data.

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9. Stats iQ

Stats iQ is a statistical tool by Qualtrics that is easy to use. It was designed by and for big data analysts. The interface is intuitive and automatically selects statistical tests. Statwing is a powerful data tool that allows you to quickly analyze any data. You can also quickly create charts, find relationships and tidy up data.

It allows you to create bar charts, scatterplots, and histograms which can be exported into Excel or PowerPoint. It can be used by analysts who don’t know much about statistical analysis to translate their findings into plain English.

10. CouchDB

CouchDB stores information in JSON documents. These can be accessed online using JavaScript. It allows for distributed scaling and fault-tolerant storage. It allows data access by creating the Couch Replication Protocol. One of the massive data processing tools allows one logical database server to be run on multiple servers.

It uses the ubiquitous HTTP protocol and JSON data format. It allows for simple database replication across multiple server instances. Also, it provides an interface that allows you to add, update, retrieve, delete, and retrieve documents.


These are the top 10 open-source big data tools that you need to have hands-on experience with if you are interested in data science. This domain is very popular and many professionals are now looking to improve their skills and be more successful in their careers.