Open Source Big Data Tools for 2020

Now, when we talk about big data tools, multiple aspects come into the picture concerning it. For example how large the data sets are, what type of analysis we are going to do on the data sets, what is the expected output etc. Hence, broadly speaking we can categorize big data open source tools list in following categories: based on data stores, as development platforms, as development tools, integration tools, for analytics and reporting tools.

Why There are So Many Open Source Big Data Tools in the Market?
No doubt, Hadoop is the one reason and its domination in the big data world as an open source big data platform. Hence, most of the active groups or organizations develop tools which are open source to increase the adoption possibility in the industry. Moreover, an open source tool is easy to download and use, free of any licensing overhead.

If we closely look into big data open source tools list, it can be bewildering. As organizations are rapidly developing new solutions to achieve the competitive advantage in the big data market, it is useful to concentrate on open source big data tools which are driving the big data industry.
Open Source Big Data Tools for 2020

Open Source Big Data Tools for 2020

Based on the popularity and usability we have listed the following ten open source tools as the best open source big data tools in 2020.

1. Hadoop

Apache Hadoop is the most prominent and used tool in big data industry with its enormous capability of large-scale processing data. This is 100% open source framework and runs on commodity hardware in an existing data center. Furthermore, it can run on a cloud infrastructure.

2. Apache Spark

Apache Spark is the next hype in the industry among the big data tools. The key point of this open source big data tool is it fills the gaps of Apache Hadoop concerning data processing. Interestingly, Spark can handle both batch data and real-time data. As Spark does in-memory data processing, it processes data much faster than traditional disk processing. This is indeed a plus point for data analysts handling certain types of data to achieve the faster outcome.

3. Apache Storm

Apache Storm is a distributed real-time framework for reliably processing the unbounded data stream. The framework supports any programming language..

4. Cassandra

Apache Cassandra is a distributed type database to manage a large set of data across the servers. This is one of the best big data tools that mainly processes structured data sets. It provides highly available service with no single point of failure. Additionally, it has certain capabilities which no other relational database and any NoSQL database can provide.

5. RapidMiner

RapidMiner is a software platform for data science activities and provides an integrated environment for:
  • Preparing data
  • Machine learning
  • Text mining
  • Predictive analytics
  • Deep learning
  • Application development
  • Prototyping
This is one of the useful big data tools that support different steps of machine learning, such as:
  • Data preparation
  • Visualization
  • Predictive analytics
  • Model validation
  • Optimization
  • Statistical modeling
  • Evaluation
  • Deployment
RapidMiner follows a client/server model where the server could be located on-premise, or in a cloud infrastructure. It is written in Java and provides a GUI to design and execute workflows. It can provide 99% of an advanced analytical solution.

6. MongoDB

MongoDB is an open source NoSQL database which is cross-platform compatible with many built-in features. It is ideal for the business that needs fast and real-time data for instant decisions. It is ideal for the users who want data-driven experiences. It runs on MEAN software stack, NET applications and, Java platform.

7. R Programming Tool

This is one of the widely used open source big data tools in big data industry for statistical analysis of data. The most positive part of this big data tool is – although used for statistical analysis, as a user you don’t have to be a statistical expert. R has its own public library CRAN (Comprehensive R Archive Network) which consists of more than 9000 modules and algorithms for statistical analysis of data.

8. Neo4j

Hadoop may not be a wise choice for all big data related problems. For example, when you need to deal with large volume of network data or graph related issue like social networking or demographic pattern, a graph database may be a perfect choice.

9. Apache SAMOA

Apache SAMOA is among well known big data tools used for distributed streaming algorithms for big data mining.

10. HPCC

High-Performance Computing Cluster (HPCC) is another among best big data tools. It is the competitor of Hadoop in big data market. It is one of the open source big data tools under the Apache 2.0 license.