Spark is an analytics engine that is used by data scientists all over the world for Big Data Processing. It is built on top of Hadoop and can process batch as well as streaming data. Hadoop is a framework for distributed computing that splits the data across multiple nodes in a cluster and then uses of-the-self computing resources for computing the data in parallel.

Features of Spark SQL

The following are the features of Spark SQL −

  • Integrated − Seamlessly mix SQL queries with Spark programs. Spark SQL lets you query structured data as a distributed dataset (RDD) in Spark, with integrated APIs in Python, Scala, and Java. This tight integration makes it easy to run SQL queries alongside complex analytic algorithms.
  • Unified Data Access − Load and query data from a variety of sources. Schema-RDDs provide a single interface for efficiently working with structured data, including Apache Hive tables, parquet files, and JSON files.
  • Hive Compatibility − Run unmodified Hive queries on existing warehouses. Spark SQL reuses the Hive frontend and MetaStore, giving you full compatibility with existing Hive data, queries, and UDFs. Simply install it alongside Hive.
  • Standard Connectivity − Connect through JDBC or ODBC. Spark SQL includes a server mode with industry-standard JDBC and ODBC connectivity.
  • Scalability − Use the same engine for both interactive and long queries. Spark SQL takes advantage of the RDD model to support mid-query fault tolerance, letting it scale to large jobs too. Do not worry about using a different engine for historical data.
Data Analysis using Spark SQL | Perform Data Analysis Using Spark SQL
Spark is an analytics engine that is used for Big Data Processing. Let us see an Introduction to Data Analysis using Spark SQL

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