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15 Reasons to Learn Apache Spark

 July 14  | 0 Comments

Hadoop is hot. But its close cousin Spark is even hotter. Developed at UC Berkeley’s AMPLab, Apache Spark is a framework for performing data analytic’s on distributed cluster like Hadoop. It provides in-memory computations to increase speed and data process. It runs on top of existing Hadoop cluster and accesses the HDFS. It can also process structured data in Hive and Streaming data from HDFS, Flume and Kafka.

Apache Spark has moved from a being a component of the Hadoop ecosystem to the Big Data platform of choice. It is also expanding developers’ choice for toolboxes in 2016 and the coming years.

According to a recent survey conducted on Spark, it was found that the awareness and adoption of Spark are on the rise. The Google Trends confirms this as well.

Img: google-spark

Here are some technical features that makes Spark an essential skill to learn:

  • Spark is designed to run on top of Hadoop and is an alternative to the traditionalbatch MapReduce model that can be used for real-time stream data processing and fast interactive queries.
  • Spark as an alternative to Hadoop’s MapReduce than a replacement to Hadoop.
  • Spark uses more RAM instead of network and disk I/O and is relatively fast compared to Hadoop.
  • Spark allows applications in Hadoop clusters to run up to 100x faster in memory,and 10x faster when running on disk.
  • Spark has become another data processing engine in Hadoop ecosystem and makes the Hadoop stack much -more competent.
  • Spark lets you swiftly write applications in Java, Scala, or Python.
  • Spark runs on Hadoop, Mesos, standalone, or in the Cloud and can access varied data sources including HDFS, Cassandra, HBase, S3, etc.
  • Besides the MapReduce operations, Spark supports SQL queries, streaming data, and complex analytics such as machine learning and graph algorithms.
  • All the above mentioned capabilities can be effortlessly in a single workflow.
  • It’s easier to develop for Spark as it contains other functions like Filter, Join and Group-by, along with MapReduce, making it easier to develop for Spark.
  • Capable of performing Iterative Algorithms in Machine Learning.
  • Can perform Interactive Data Mining and Data Processing
  • Spark is a fully Apache Hive-compatible data warehousing system that can run 100x faster than Hive.
  • Can do Stream Processing, Log processing and Fraud detection in live streams for alerts, aggregates, and analysis.
  • Sensor data processing.
  • Spark’s RDD (Resilient Distributed Dataset) abstraction resembles Crunch’s PCollection, which has proved a useful abstraction in Hadoop.
  • In Spark, the data operations are transparently distributed across the cluster,even as you type.

With Spark, the Integration is much easier and applications are far stress-free to maintain, making it cost effective for the organizations and comfortable to use by the developers.

These are just some of the prominent reasons behind its popularity, which is growing in par with Hadoop. This makes it a vital skill to possess by all those aspiring to become Big Data professionals.

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