Big Data Hadoop & Spark

7 Reasons Why Apache Spark Will Be the Future of Big Data

Often denoted by the four V’s; Velocity, Variety, Veracity and Volume, data of humungous sizes like terabytes, petabytes, etc., are coined as Big Data. Today, Big Data is no more a buzzword and organizations are increasingly becoming aware of the importance of Big Data and the huge potential that it gives. As expectations from data kept mounting in terms of providing better insights to make business grow, Apache Hadoop stormed in with its unbeatable feature in processing Big Data using batch processing technology. However, recently, expectations from Big Data enterprises have further escalated which resulted in the invention of Apache Spark; a tool that performs various actions such as processing, querying and generating analytics at a very high speed.
There are various reasons why the Big Data domain believes Spark to be a worthy competitor/replacer of Hadoop’s MapReduce. Top among them stands the exceptional speed and power of Spark’s in-memory computation. Spark stores data in RAM rather than magnetic disk, whereby the data seek time decreases and processing speeds up beyond our imagination. However, RDD (Resilient distributed Data-set) the distributed collection of data, doesn’t contain the data. Just like Hadoop, Spark too provides fault tolerance via replication of nodes. Features like these has made Spark the next Big Data platform when compared to Hadoop.
Although Hadoop still stands the favoured platform, the number of Apache Spark users has exponentially increased and has progressively considered as future of Big Data Platform because Spark is:
Faster than Hadoop’s MapReduce
Spark loads the data in memory to process it thus speeding up the processing time as the data seek time from memory is much less than disk. On the other hand, Hadoop seeks the data from disk which makes it slower when compared to Spark. It has also been seen that in best cases where Spark performed 100 times better than MapReduce.

Easily Integrated with Hadoop
Spark can leverage Hadoop’s storage unit (HDFS) and use its own processing engine to make lightning fast big data analysis. Most of the frameworks have compatibility issues, like, MapReduce can only run on Hadoop. Spark is flexible when it comes to compatibility. It can use various resource manager and run on top it. A perfect example is, Spark using Yarn, Mesos, or even its own cluster manager.

Efficient Memory Management
You must be excited to know what makes Spark so different that it processes at lightning fast speed! Well, the credit goes to RDD (Resilient Distributed Dataset). As the name suggest, it is distributed collection of data that is fault tolerant, parallel data structure which suits best for in-memory cluster computation. Consistent with the Hadoop paradigm, RDDs can persist and be partitioned across a Big Data infrastructure ensuring that data is optimally placed. And of course, RDDs can be manipulated using a rich set of operators.
Significant Uptake of Apache Spark
Spark’s community is one of the most active community building and debugging various Spark releases. Spark 1.2.0 was released in mid-December 2014. Over 1,000 commits were made by the 172 developers contributing to this release – that’s more than 3x the number of developers that contributed to the previous release, Spark 1.1.1.
As of October 30th 2016, the new version, Spark 2.0.1 is available

Generality of Spark
Using Spark, you can combine Spark streaming, SparkSQL, and complex analytics on a single platform.  It has rich libraries that includes Spark SQL, GraphX for graph processing, MLlib for Machine learning, Dataframes, Datasets, and Spark Streaming. Although built in Scala, Spark can be used using Java, Python, and R as well.

Can Run Everywhere
Spark does not have its own storage system. Although most of the time Spark is linked with Hadoop Distributed File System (HDFS), it can also be integrated with other storage system like, Mesos, s3, Cassandra etc. Spark can run using its own standalone cluster, Hadoop, etc.
Efficient Way of Handling Iterative Algorithms
Spark is great at handling programming models involving iterations, interactivity that includes streaming, and much more whereas, MapReduce have shown various inefficiencies in handling iterative algorithms. This is the main reason for Spark replacing MapReduce.
Spark is still a new technology and it is yet to fully sprawl in the Big Data market. The use of Spark is growing quickly among many top-notch companies, like Yahoo, Adobe, and NASA to name a few. Aside from those who belong to Spark community, there are a handful of professionals who are well-versed with Spark and can work on it. This has created a soaring demand for erudite Spark professionals. In such state of affairs, learning Spark can give you a competitive edge.
So what are you waiting for? Hurry up and take AcadGild’s Apache Spark course now and get recognized. And Keep visiting for more updates on the courses.

One Comment

  1. Thank you for sharing great article for Future Scope of Hadoop. i want to also learn Hadoop. your article very helpful for me.

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