Top Open Source Big Data Processing Frameworks 2018

Over the past half a decade data generation reached an extreme level. With the technology evolution sources of data generation increased massively. We are now surrounded with data. in every micro moment mammoth amount of data is being generated all over the world. These data are huge & equally important. Processing, storing & distributing this mammoth data needs a powerful technology & architecture. Here Big Data comes into play. With Big Data frameworks massive amount of structured or unstructured data can be processed, stored & distributed real time to different nodes rapidly with ultimate speed. Big Data offers processing & analyzing this huge data for further usage as statistics & analytics.  Here we will discuss about Top open Source Big Data processing Frameworks 2018.

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1. Apache Hadoop

Hadoop is currently leading in big data industry and for its security, scalability & robustness. This distributed data processing & storage framework has become the most popular architecture among the developers & enterprises. It’s an open source framework form Apache which is mainly famous for its powerful data processing for very large data & easy implementation.

It can process, store & distribute both structured & unstructured data. Also it transforms big chunk of data into schema & nodes for local machines. Even if you have mammoth data, Hadoop makes it easy & simple for processing it & make it available for data analytics at any point of time.

Major components of Hadoop are:

• Hadoop Common: These common utilities used for supporting other Hadoop modules.
• HDFS (Hadoop Distributed File System): It’s a distributed file system which provides the access & interaction to application data.
• Hadoop YARN: This is a framework which manages Cluster Resource & Job Schedule.
• Hadoop MapReduce: It’s a framework and one of the most important modules of Hadoop which uses logic & algorithm to process huge datasets.

2. Apache Spark

Spark is another open source framework used for Big Data processing & creating hybrid framework. It’s mainly a batch & stream processing framework. This framework supports Java, Scala, Python & R also popular for its ease of use.

It’s one of the leading big data frameworks for its powerful ability to provide cluster computing & machine learning rather than processing huge data sets. Most importantly it can run on a single machine & can act as an individual, standalone framework with the help of cluster manager & distributed storage system. For its scalability this open source big data framework is ideal for any enterprise.

Spark framework operates on a read only ‘Resilient Distributed Dataset (RDD)’ data structure which offers restricted distributed shared memory to machines. Moreover Spark also can access HDFS, HBase, Cassandra data sources.

Spark can perform tasks like programming, task dispatch, job schedule, handling basic I/O operations etc.

Major modules of Spark Core:

• SparkSQL: it used to manipulate DataFrames.
• Spark Streaming: For batch & stream analytics.
• SparkMLlib: It’s mainly a machine learning library which simplifies the machine learning task.
• GraphX: This is a distributed Graph Processing framework.

3.  MongoDB

This open source Big Data tool is also in the leading position & very popular among developers for its scalability, ease of use & deployment, security, compatibility, flexibility, robustness, indexing, expressive query language, huge data processing & storage capabilities with real time data analytics. MongoDB is also a cross platform framework with NoSQL database program.

This framework is also highly used for making customized applications. MongoDB is basically a Database as a Service which is also available on Amazon AWS, Microsoft Azure & Google Cloud.

Four major products of MongoDB:

• MongoDB Professional
• Mongo DB Stitch
• MongoDB Atlas
• Cloud Manager

4.  Apache Cassandra

Apache Crassandra is also an open source Big Data framework from Apache which is popular among developers & companies. Cassandra also uses NoSQL DBMS & capable of processing  & managing extremely large data sets on various server.

This is a scalable & powerful data processing system which is higly compatible with cloud servers & delivers great performance. Many big enterprises prefer this Big Data framework for its flexibility, durability, decentralization, massive community support, fault tolerance, ease of use & maintenance.

5.  Lumify

Lumify is also an open source Big Data framework which is relatively newer. But its gaining fast popularity among developers & mid enterprises as an alternative to Hadoop. This framework is capable of processing & sorting huge data with different sizes, types, formats & sources rapidly.

Moreover its USP is its web-based data analytics & visualization module. This features makes it more cutting edge & smart. With its web-based interface you can explore & present data relationships between huge data sets via 2D and 3D graphs, advance searches, dynamic histograms, interactive geospatial views and moreover it offers a real-time collaborative shared workspaces with team, clients & stakeholders. It’s also available on AWS Cloud.

6.  Apache Storm

Apache Storm is an open source big data framework. This can be used with Hadoop as well as an individual framework. It’s a real time distributed computation & machine learning system. This framework is mostly known as processing & distributing real time data rapidly. Its very simple & easy to configure & use and can be configure & implemented almost with any programming language.

Enterprises highly prefer Storm for real-time data analytics, online machine learning, uninterrupted computation speed, performance & scalability.

7.  HPCC Systems Big Data

This is an open source platform for Big Data framework. It’s an excellent tool for data warehousing. It’s used for programming, data manipulation, data transformation & data querying. Moreover HPCC is a great alternative to Hadoop. Many enterprises prefer HPCC for its superior performance, speed, agility, scalability, compatibility & fast data handling.

It also has features like robust programming IDE, fault tolerance, built-in distributed file system etc.

8.  Samza

Samza is also an open-source asynchronous Big Data framework mainly known for distributed stream processing & almost real-time distribution. Basically this framework works with the collaboration of other frameworks. For messaging it uses Apache Kafka and for processing, security, data management, data handling, resource management & fault tolerance it uses Hadoop.

9.  Flink

Flink is another open source hybrid framework for batch & stream processing. For processing it uses low-latency engine written in Java and Scala. It also has a pipeline system for executing programs & tasks. Its runtime engine performs native execution of iterative algorithms.

Flink is one of the most fault-tolerant frameworks. This framework supports programming languages like Java, Scala, Python, and SQL.

Major components of Flink:

• Streams
• Operators
• Sources
• Sinks

One point to remember before using this framework that, it doesn’t have its own storage system, therefore you need to use this tool in collaboration with other framework & Flink is compatible with almost any Big Data framework.

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