Big Data and Analytics are transforming the way businesses take informed market-oriented decisions, craft strategies for targeting customer segments that are optimally promising, and remain shielded from market quirks and economic volatilities. These abilities are impacted by mining information that is locked in large data volumes generated online or from other connected sources.

Big Data can be reliably processed with the Apache Spark interface. Apart from facilitating seamless programming for data clusters, Spark also offers proper tolerance for faults and data parallelism. This implies that large datasets can be processed speedily by this open source platform. Apache Spark has an edge over Hadoop in terms of better and sophisticated capabilities on data handling, storing, evaluation and retrieving fronts. Spark framework comes integrated with modules for ML (Machine Learning), real-time data streaming, textual and batch data, graphics, etc., which makes it ideal for different industry verticals.

Scala or Scalable Language is a general-purpose object-oriented language with which Spark is written for supporting cluster computing. Scala offers support with immutability, type interference, lazy evaluation, pattern matching, and other features. Features absent in Java such as operator overloading, named parameters, no checked exceptions, etc. are also offered by Scala.

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Top 9 Reasons To Start Learning Apache Spark and Scala
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