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
MongoDB is a NoSQL, document-oriented database written in C, C++, and JavaScript. It is free to use and is an open source tool that supports multiple operating systems including Windows Vista (and the latest versions), OS X (10.7 and the latest versions), Linux, Solaris, and FreeBSD. Its main features include Aggregation, Sharding, Indexing, Replication, Server-side execution of javascript, Schemaless, Adhoc-queries, Uses BSON format, Capped collection, MongoDB management service (MMS), load balancing and file storage. MongoDB is easy to learn and provides support for multiple technologies and platforms.
Cloudera is the fastest, easiest and highly secure modern big data platform. It allows anyone to get any data across any environment within a single, scalable platform. Cloudera offers high-performance analytics offering a provision for multi-cloud. Users can spin up and terminate clusters, and only pay for what is needed when they need it. In addition, users can deploy and manage Cloudera Enterprise across AWS, Microsoft Azure and Google Cloud Platforms.
Hive is an open-source-software big data too. It allows programmers to analyse large data sets on Hadoop. It helps with querying and managing large datasets real fast. Hive supports SQL like query language for interaction and Data modelling compiling language with two main tasks map and reducer. Hive allows defining these tasks using Java or Python and offers Java Database Connectivity (JDBC) interface.
KNIME is the acronym for Konstanz Information Miner, an open source tool that is used for Enterprise reporting, CRM, data mining, data analytics, integration, research, text mining, and business intelligence. KNIME supports Linux, OS X, and Windows operating systems, integrating very well with other technologies and languages. With KNIME, users can Automate a lot of manual work, in an organized workflow environment.
Tableau is an open source data visualization platform for analysis and visual presentation of big data. Tableau works closely with the leaders in this space to support any platform that our customers choose. It lets you find that value in your company's data and existing investments in those technologies so that your company gets the most out of its data. From manufacturing to marketing, finance to aviation– Tableau helps businesses see and understand Big Data.
Join our WhatsApp Channel to get the latest news, exclusives and videos on WhatsApp
_____________
Disclaimer: Analytics Insight does not provide financial advice or guidance. Also note that the cryptocurrencies mentioned/listed on the website could potentially be scams, i.e. designed to induce you to invest financial resources that may be lost forever and not be recoverable once investments are made. You are responsible for conducting your own research (DYOR) before making any investments. Read more here.