5 Highly Recommended Open Source Big Data Tools in 2018


5 Highly Recommended Open Source Big Data Tools in 2018

Today we are living in an era where companies have to focus on the collection and analysis of large data in order to make a dominant place in the competitive industry. It is not possible to rely on some basic elements but analyzing larger data has become imperative to get useful insights that help the businesses to move in the right direction, that is progression and growth!

In this blog, we are talking about 5 top open source tools which facilitate the experts in assessing big data. Data specialists and experts already have pretty much knowledge about Hadoop, the most popular tool for analyzing a large volume of data so, for now, we are going towards other useful tools.


  • Cassandra:

It is an open source distributed database system that stores and manages the data. To avoid any single point of failure, Apache Cassandra is designed to handle the sudden increase in demand by peer to peer symmetric nodes rather than named or master nodes.

One plus point is that the administrator has full control over the data and can determine which data will be replicated and how many copies will be produced. This tool has the capability to serve both the real-time operational data store and read-intensive databases.

Today Apache Cassandra is being used by some of the big business giants like Facebook, Netflix, Cisco, eBay and others. Its scalable architecture, support for big data, ACID support, high-speed data writes and seamless distribution has made it that popular.


  • HPCC:

HPCC is the acronym for High-Performance Computing Cluster. It is developed by LexisNexis Risk Solutions and is an open source platform for profiling, job scheduling, cleansing and automation.

It uses ECL as a scripting language which supports both parallel batch data processing and real-time query applications.

HPCC runs on commodity hardware that supports end-to-end big data workflow management. It is an extensible and highly optimized platform that also helps to build graphical execution plans.


  • R Programming Tool:

Data analysis is now reshaping how the businesses run and stay ahead of the competitors. So in the meantime, you cannot neglect new technologies as AI and machine learning for data analysis. But now the real topic of concern is,

Which is the best tool for data analysis?

Amongst many others, R programming tool is famous for its deployment in clustering, correlation, and data reduction. It has a public library CRAN (Comprehensive R Archive Network) which consist of more than 9000 algorithms and modules used for the purpose of statistical data analysis.


  • Apache Storm:

Why is it named as storm?

The fast processing of large volume of data has contributed to naming it as ‘storm’. It is a distributed real-time computation system that can process millions of records in a second. Some of its new business applications include data monetization, real-time customer service management, operational dashboards, cybersecurity analytics and threat detection.

It is simple to use and developers can write storm topologies by using any programming language. The features which make its prominent than others are fast speed, scalability, reliability, fault tolerance and easy operation.


  • Neo4j:

Developed by Neo Technologies, this is a graph database management system.

 For businesses, this tool is highly recommended as it has many valuable features. It can be utilized for developing real-time recommendation engines, track roles and groups help to detect fraud and enhance master data management.

Besides other advantages, it is highly responsive to managing large data. Its flexibility and scalability have made it a favourite tool for data experts.


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