Many moons back, as the world stepped into the ambit of globalisation, streamlining business workflow seemed to be the most urgent need of the hour. Therefore, the market today is swarming with a wide range of Big Data Tools in order to bring in cost efficiency and improved time management into data analytical services. Big Data Analytics being one of the most crucial part of any business workflow these days, the below mentioned popular open source Big Data Solutions are being used at each stage of data processing.

Apache Hadoop

A popular big data framework is the Apache Hadoop software library. It helps in the distributed processing of large data sets across clusters of computers. Its design is such that single servers can be scaled up to thousands of machines.

Benefits and features:

  • Improved authentication when using HTTP proxy server.

  • Hadoop Compatible File system effort specification.

  • Extended support attributes for POSIX- style file system.

  • It provides a strong ecosystem that is suitable for analytical needs of the developer.

  • Flexible data processing.

  • Faster data processing.

Apache Spark

An alternative and in many ways, the successor of Apache Hadoop is Apache Spark. Apache Spark addresses all the shortcomings of Apache Hadoop; for instance, it can process both batch data and real time data with speeds of more than 100 times faster than MapReduce. Apache Spark allows an in- memory data processing option which is much faster than the disk processing used by MapReduce. Apache Spark works with HDFS, OpenStack, and Apache Cassandra, in addition to cloud and on-prem. This makes it a versatile tool in big data operations of any business nowadays.

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Apache Storm

Apache Storm provides a real time framework, for data stream processing and supports all programming languages. The scheduler balances the workload between the various nodes depending upon the topology configuration and is compatible with Hadoop HDFS. 

Benefits and features:

  • Vast horizontal scalability

  • Great horizontal scalability

  • Built-in troubleshoot

  • Auto-restart during crashes

  • Works with Direct Acyclic Graph(DAG) topology

  • Output files are in JSON format

Apache Cassandra

One of the pillars behind Facebook’s huge success is Apache Cassandra. Apache Cassandra can process the massive structured data set’s distribution of nodes across the globe. It has the capability to work under enormous workloads due to its sturdy architecture, and has proven non-failure record that no other NoSQL or relational DB can boast of.

Benefits and features:

  • Great liner scalability

  • Use of simple query language  makes operation simple

  • Constant replication across nodes

  • Simple adding and removal of nodes from a running cluster

  • High fault tolerance

  • Built-in high-availability

MongoDB

An open source NoSQL database having rich features such as a cross platform compatibility with several programming languages is MongoDB. IT Svit uses MongoDB in various cloud computing and monitoring purposes as well as developing a module for automated mONGOdb BACKUPS USING Terraform. 

Benefits and features:

  • From text and integer to strings, arrays, dates and Boolean, MongoDB can store any type of data.

  • Vast flexibility of configuration with cloud native deployment.

  • Data partitioning across several nodes and data centres.

  • Dynamic schemes enable data processing on the go thereby considerable cost savings.

R Programming Environment

For enabling wide scale statistical analysis and data visualization, R Programming is generally used along with JuPyteR stack (Julia, Python, and R). JuPyteR Notebooks is one of the four most accepted Big Data visualization tools. It allows composing all types of analytical model amongst more than 9000 CRAN (Comprehensive R Archive Network) algorithms and modules, running it in a suitable environment, adjusting it on the go, and inferring the analysis results at the same time.

Benefits and features:

  • Runs inside the SQL server.

  • Compatible with both Windows and Linux servers.

  • Supports Apache Hadoop and Spark

  • Highly portable.

  • Can easily scale from a single test machine to massive Hadoop data lakes.

Neo4j

An open source graph database having interconnected node relationship of data and following a key value pattern in storing data is Neo4j. Not long back, IT Svit has built a robust AWS infrastructure using Neo4j. Its database performs fine under heavy workload of network data and graph related requests. 

Benefits and features:

  • For ACID transactions, it has a built in support.

  • Cypher graph query language.

  • Good demand and scalability.

  • Due to the absence of schemas, it is highly flexible.

  • Assimilation with other databases.

Apache SAMOA

Another Apache family tool used for Big Data processing is Apache SAMOA. It specializes in building distributed streaming algorithms for successful Big Data mining. It must be used on top of other Apache products like Apache Storm, since it is built with pluggable architecture.

Benefits and features:

  • Clustering

  • Classification

  • Normalization Programming primitives for developing custom algorithms

  • Regression

Big Data industry and data science  have evolved swiftly and has significantly progressed off late. With numerous Big Data projects and tools launched in 2018, this sure is one of the newest IT trends of 2019, along with IoT, blockchain, AI, and  ML.

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