It offers much tighter integration between relational and procedural processing, through declarative DataFrame APIs which integrates with Spark code. Spark SQL blurs the line between RDD and relational table. Let us explore, what Spark SQL has to offer. It majorly works on DataFrames which are the programming abstraction and usually act as a distributed SQL query engine. Spark SQL is not a database but a module that is used for structured data processing. But the question which still pertains in most of our minds is, Is Spark SQL a database? These drawbacks gave way to the birth of Spark SQL. To overcome this, users have to use the Purge option to skip trash instead of drop.
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Spark SQL integrates relational processing with Spark’s functional programming. It provides support for various data sources and makes it possible to weave SQL queries with code transformations thus resulting in a very powerful tool. The following provides the storyline for the blog:
Through this blog, I will introduce you to this new exciting domain of Spark SQL. It supports querying data either via SQL or via the Hive Query Language. Spark SQL is a new module in Spark which integrates relational processing with Spark’s functional programming API. With the advent of real-time processing framework in the Big Data Ecosystem, companies are using Apache Spark rigorously in their solutions.
Apache Spark is a lightning-fast cluster computing framework designed for fast computation.