OPEN SOURCE BIG DATA TOOLS-The amount of data in today’s digital world has exploded to unheard levels, with nearly 2.5 quintillion bytes of data churned daily. With advances in the Internet of Things and mobile technology, harnessing insights from data has become a gold mine for organisations. So how do organisations harness the big data that is coming from different sources, here is our pick for the Top 10 Open Source Big Data Tools for 2019.
The Apache Hadoop software library is a framework allowing the distributed processing of large datasets across clusters of computers. The Apache Hadoop is designed to scale up from single servers to thousands of machines, with each offering local storage facilities. Hadoop framework allows users to write and test distributed systems efficiently and it automatic distributes the data and work across the machines.
Another big advantage of Hadoop is it is open source, and compatible with all the platforms.
The next on the list is Apache Spark, which is flexible to work with HDFS and the other data stores. Apache Spark integrates with OpenStack Swift and Apache Cassandra. Spark in addition can also run on a single local system to make the development and testing work easier. Spark assists to run an application in Hadoop cluster, which is up to 100 times faster in memory, and 10 times faster when it is running on disk. Spark provides built-in APIs in Python, Java or Scala, which enables users to write applications in different languages.
The Apache Cassandra database is the best open source big data tool when you need scalability and high availability. Cassandra, scores on its linear scalability and proven fault-tolerance on commodity hardware and cloud infrastructure. Cassandra is highly scalable and allows to add more hardware to accommodate more data and users as per requirement. In addition, Cassandra accommodates all possible data formats like unstructured, structured and semi-structured supporting properties like Atomicity, Consistency, Isolation, and Durability (ACID)
Apache Storm is a free distributed real-time computation system, which makes real-time processing of humongous streams of data easy for real-time processing. Apache Storm is easy to integrate with any programming language, with many use cases demonstrating real-time analytics, online machine learning, continuous computation, distributed RPC. The storm is fast: a benchmark clocked at over a million tuples processed per second per node. Apache Storm is scalable and offers an easy to set up and operate mechanism. Apache Storm uses parallel calculations that run across a cluster of machines
RapidMiner is an open source software platform for data science activities, providing an integrated environment for data preparation, machine learning, text mining, visualization, predictive analysis, application development, prototyping, model validation, statistical modelling, evaluation, deployment, etc. RapidMiner offers a suite of products to develop a new data mining process. Rapid big data tool has an ability to integrate with in-house databases