Big Data has gained popularity in recent years in terms of its exponential growth in its storage capacity, privacy, data analysis, visualization, updating, transfers, an information security.

Over the time there have been many Big Data techniques which are used to store data and perform tasks at a faster pace with increased processing speed in data analysis.

A week before, I was reading about some of the common Big Data frameworks that have been the best till date and have had served the purpose of data privacy and storage past a long period of time. to my surprise beyond the common list were a few frameworks which were new to me and i realized that the time has come to study about them some more.

So in this blog post, I will be discussing few of the best Big Data frameworks which are new in the world of data management but have been performing their tasks in the best of the their capabilities.

Read On!

1. FLINK–  Apache Flink is an open source framework  that is accurate, fault tolerant and a large scale performer. It is a low-latency streaming engine that is written in Java, Scala, Python and SQL. It is by far one of the most streamlined data framework which supports event time processing and and state management.
   Components of FLINK includes streams(unbounded data sets), operators(create other streams), sources(entry points that enter into the system), and sinks(places where streams flow out of the Flink system). It serves one of the best additional features of low latency, high performance and controlled cyclic dependency graphs on run-time basis. The only problem with this data framework is its comparatively high cost due to excessive RAM consumption.

2. SAMZA– Apache Samza is a distributed stream processing framework and is written in Java and Scala.It handles enduring streams which implies that if any transformations are made while creating new streams, the initial streams will be unaffected. Samza uses Apache Kalfa for messaging and Apache Hadoop YARN to provide fault-tolerance and resource management. The best part about Samza is that it is constructed in such a manner that it handles large amounts of state perfectly. samza.apache.org provides following provisions:-

   • Managed State.

   • Durability

   • Scalability

   • Pluggable

3. STORM– Apache Storm is a stream processing framework that focuses on low latency and is the best possible solution for extensively large data handlers and quick delivers of near real-time processing. It serves the one for all solution dealing in real time analytics, machine learning, continuous computation and has been benchmarked for processing one million tuples per second per node. It provided features similar to that of Flink and Samza i.e. reliability, fault-tolerant, low latency, Storm is simple and can be used with any programming language with a lot of fun!

4. Hadoop–  Apache Hadoop is an open source, scalable and fault-tolerant framework written in Java. Hadoop is one of the top frameworks which is in use today. It provides batch processing and processes large volumes of data effectively and efficiently at an ease. Modern versions of Hadoop include several components of data including; HDFS (Hadoop Distributed File System), which is a distributed file system that stores and replicates across the cluster nodes. It ensures that data remains available in spite of unwanted host failures. Hadoop YARN (Yet Another Resource Negotiator), coordinates and manages the underlying resources and scheduling jobs that need to be run. Map Reduce is Hadoop’s native branch processing engine.

5. Spark– Apache Spark is a fast cluster computing system. It is 100 times faster that Big Data Hadoop and 10 times faster than accessing data from the disk. It can be integrated with Hadoop and can process existing Hadoop HDFS data. Apache Spark focuses on speeding up batch processing workloads by offering full in-memory computation and processing optimization.

Out of the 3 new bees, STORM, SAMZA and FLINK; Storm is the most popular, Flink is comparatively s new comer and is gaining its existence with time and Samza is somewhere in the middle. All the 3 have proven in some or the other way in handling Big Data effectively yet they have not gained as much popularity in the data handling market as that of Apache Hadoop and Apache Spark. but with time these data handlers have proven to offer speed of processing and analyzing the data with effective and efficient methods.