Inception:
Big Data world is moving fast. The emergence of new technologies promises to manage and analyze large volumes of data faster and in a more scalable way. This happens with cheaper implementation and maintenance costs. Among the recent tools and technologies, Apache Spark which is the open source distributed computing platform, is among the most notable, as it offers added value in contrary to its predecessors.
It is a powerful open-source processing engine for Hadoop. It is cluster computing framework which was initially developed in AMPLab at UC Berkeley in 2009 and it became open sourced in 2010. Post-2010, Apache Spark became one of the largest open source communities in Big Data. It cherishes 200 contributors from 50+ organizations. Later on, Spark moved to Apache. Therefore, Apache Spark is basically a parallel data processing framework which can work along with Apache Hadoop. This fosters the easy and fast development of mobile applications. This also allows streaming, interactive analysis, and batch on all data of big data applications.
Enterprise Adoption:
Since Apache Spark got released, it has observed rapid adoption by enterprises and to a wide range of industries. Internet powerhouses including Yahoo, Netflix, and eBay have deployed Spark at a large scale therefore collectively processing multiple petabytes of data on over more than 8,000 nodes. Spark has quickly become the largest open source community in big data with more than 1000 contributors from 250+ organizations.
Harness Hadoop and Spark for user-friendly BI. #BigData #DataScience #ApacheSpark #Hadoop #Analytics #BIhttps://t.co/SlGjSNoQNC pic.twitter.com/Eln34lS1dM
— Dr. Ganapathi Pulipaka 🇺🇸 (@gp_pulipaka) March 2, 2017
There are various advantages of Apache Spark which makes it an attractive big data framework. Let us talk through the benefits Apache Spark in this blog:
1- Lightning Fast Processing
With BigData processing speed always matters. There is always a requirement of fast processing of huge data. Spark allow applications in Hadoop clusters to run up to 100x faster than Hadoop MapReduce in memory and 10 times faster even when running on disk. Spark uses the concept of a Resilient Distributed Dataset, which allows transparently storing data on memory and persist it to the disc only when it is needed. It reduces the most of the disc read and writes time which itself is a time-consuming factor of data processing.
2- Spark has a large and active community
The most amazing aspect of an open source solution is how active its community is. The community of developers improves the features of the platform and help programmers to implement solutions or solve problems. With each passing year, the community of Spark is getting increasingly active. In Sept 2013 there were over 113,000 lines of code which increased to 296,000 posts one year. Wherein in 2015, the volume of lines of code touched to 620,300. If we talk about a number of programmers since June 2012, there were just 4 contributors which turned into 128 after three years. July 2015 data showed that 137 joined the project.
3. Unified platform for data management
Apache Spark is considered as the platform of platforms and its ‘all-in-one’ feature greatly speeds up the operation and maintenance of its solutions. From the perspective of data management let us see what all Apache Spark can do efficiently:
a-Spark SQL: By using SQL language and API, it enables querying of structured data which can be used with Java, Scala, Python or R. It also allows developers, who are familiar with these programming languages, to build and run applications in Spark quickly w/o the need to learn new language altogether
b-Spark Streaming: Where MapReduce only processes data in batches, on the contrary Spark manages large volumes of data in real time. This allows data analyzation as soon as it arrives through a management process in continuous motion.
c-MLlib (Machine Learning): The tool contains various algorithms which offer many utilities to Apache Spark. These utilities include support vector machines (SVM), Bayesian regression tree models, latent Dirichlet allocation (LDA) and much more.
d-GraphX: GraphX is a graphics processing framework which provides an API for making graphs with the data.
4. Real-time stream processing
The real-time data being collected from various sources keeps shooting up exponentially with each passing year. Here comes the processing and manipulation of real time data. Here Spark helps for analyzation of real time data when it is collected. Spark optimally handles real-time data streaming where Spark modifies data in real-time using Spark Streaming. Certain applications like fraud detection, log processing in live streams and electronic trading data are availing a lot of benefits out of Spark Streaming. Spark has lightweight yet powerful API which allows you with the rapid development of streaming applications.
5- Spark integrates with Hadoop and Existing Hadoop Data
Spark can run independently. Also, it can run on Hadoop and other tools in the Hadoop ecosystem which includes Hive and Pig. Spark provides a great advantage by reading from any Hadoop data sources, for example, HDFS and HBase. This makes it suitable for migration of existing Hadoop applications. Spark being flexible and powerful offers scalable implements both batch and stream processing simultaneously, therefore, allows organizations to simplify deployment, maintenance and application development.
Related Post: 10 Top Notch Big Data Trends To Watch Out For in 2017!
Wrappers:
Spark is popularly known as the Swiss army knife of Big Data Analytics. Apache Spark supports machine learning algorithms for future predictions and supports many languages like Java, Scala, Python and R.
There is a reason why Apache Spark is famous for its iterative computing, speed and most importantly caching intermediate data in memory for better access.
Article Resources: Hadooptpoint, Databricks, Analyticstraining, Bbvaopen4u
Ravi Jain
