![]() ![]() 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. Hive cannot drop encrypted databases in cascade when the trash is enabled and leads to an execution error.This means that if the processing dies in the middle of a workflow, you cannot resume from where it got stuck. MapReduce lags in the performance when it comes to the analysis of medium-sized datasets (10 to 200 GB). Hive launches MapReduce jobs internally for executing the ad-hoc queries.Below I have listed down a few limitations of Hive over Spark SQL. Spark SQL is faster than Hive when it comes to processing speed. Spark SQL was built to overcome these drawbacks and replace Apache Hive. Apache Hive had certain limitations as mentioned below. Spark SQL originated as Apache Hive to run on top of Spark and is now integrated with the Spark stack. 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.
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