Jeff Weiner, CEO, LinkedIn says, “Data really powers everything that we do.”
Data is the new currency for businesses.
The volume, variety, velocity and veracity at which we create data has led to the development of Big Data Analytics.
Big Data helps pick granular-level information from large and complex datasets made up of structured and unstructured information.
Its ability to accommodate both structured and unstructured data is what makes it ideal for real-time analytics.
Big Data is widely used by enterprises like Amazon, Facebook, Walmart, Ford, Domino’s, among many others.
Depending on the need of the business and the type of data involved, the choice of big data tools will also differ.
9 Best Open-Source Big Data Tools
Now let’s explore some open-source big data tools that will help you develop a real-time data analytics platform that is the best fit for your business requirements.
Apache Hadoop is one of the most popular open-source platforms for distributed storage and distributed processing of Big Data.
It boasts of high-end scalability using inexpensive commodity hardware with considerably lower failure rate.
Apache Hadoop’s MapReduce programming model-based cluster computing enables processing terabytes of data in minutes and petabytes of data in hours.
The processing is faster since the tools for data processing are often located on the same servers where the data is located.
Lumify enables data Integration, Analytics, and Visualization to derive meaningful intelligence.
It is popular for its user-friendly web-based interface that any level of user can use to discover data correlations using various analytic features like full-text faceted, interactive geospatial views, graph visualizations, dynamic histograms, etc. all of which can be collaborated in real-time.
Lumify also allows integrating your own choice of analytic tools through a Bring Your Own Analytics (BYOA) infrastructure.
Apache Storm is real-time data processing system written in Clojure programming language.
It comes with extended capabilities for machine learning and advanced debugging features.
As its name indicates, Apache Storm possesses extremely fast processing speeds pegging at millions of transactions for a data cluster of a nominal size.
Further, Apache Storm is also flexible to be used with any programming language.
Apache Cassandra is an open-source distributed NoSQL DBMS with high reliability, linear scalability, and fault-tolerance.
It replicates ever node in the cluster thus eliminating the possibility of network bottlenecks that crop up during real-time analytics.
Some big data enterprises like Netflix, eBay, and even Apple trust Cassandra for real-time analytics.
Apache Spark is a sub-project of Hadoop and is used extensively where large volume data storage and processing is required.
It has in-memory cluster computing which helps the faster processing of data and applications.
Spark works fine with a diverse range of languages including commonly used Java, Python, Scala and the likes.
In addition to supporting map and reduce for advanced analytics, Apache Spark also supports Streaming data, SQL queries, Graph algorithms and even Machine Learning.
This blog post explores how you can create a scalable, reliable and fault-tolerant data pipeline capable of fetching event-based data and streaming those events to Apache Spark, all of which will be done near real-time.
Apache Kafka is yet another open-source stream processing software platform from Apache wrote using Scala and Java languages.
This blog post explores why Kafka used for developing real-time streaming data analytics.
What makes Apache Kafka different from other open-source tools is that it has built-in partitioning, distributed commit log and a guarantee for zero data loss and zero downtime.
Even with TB of data running real-time Apache Kafka can run smoothly without any issues.
Flexibility is what makes Elastic Search a perfect ally for building a real-time analytics platform.
It allows users to take data from any source, in any form, and in any volume to conduct detailed analysis to derive insights.
It is horizontally scalable and allows users to make the best of fast search and detailed analytics.
Apache Samza is one of the top-level projects of Apache Software Foundation.
Samza’s highlight is that it uses a simple callback-based “process message” API similar to MapReduce for message queueing.
It comes out of the box with YARN and Apache Kafka and has a pluggable API that helps integrate the platform with other messaging platforms.
The Project R is an open-source free software environment for statistical computing.
It has several inbuilt libraries for running statistical calculations using linear and nonlinear modeling.
The R project also has a thriving contributing community who provide packages for extending its data mining capabilities.
The Project R is widely used by data miners, analysts, and statisticians who want to arrive at meaningful conclusions from large data volumes.
Summing It Up
The sheer volume of data involved makes Big Data difficult to manage.
These open-source tools help remove that difficulty and derive real-time insights that can propel the business forward.
Not all of these Big Data tools have the same level of capabilities.
Understand each one and its capabilities before going the best one that will best suit your predictive analytic needs.
Need Help in Developing Your Data Analytics Platform?
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