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It is mainly used for structured data processing.
It provides various Application Programming Interfaces (APIs) in Python, Java, Scala, and R. Spark SQL integrate relational data processing with the functional programming API of Spark.Spark SQL provides a programming abstraction called DataFrame and can also act as a distributed query engine (querying on different nodes of a cluster).
Apache Hive was originally developed to run on Apache Spark, but it had certain limitations as follows:Hive deploys MapReduce algorithms for ad-hoc querying.
It uses in-memory computation where the time required to move data in and out of a disk is lesser when compared to Hive.Spark SQL supports real-time data processing.
Spark SQL queries are similar to traditional RDBMS queries.Now, let us understand the architecture of Spark SQL.The architecture of Spark SQLThe architecture of Spark SQL consists of three layers as explained below:Language API: This layer consists of APIs supported by Python, Java, Scala, and R. Spark SQL is compatible with all these programming languages.SchemaRDD: An RDD (Resilient Distributed Dataset) is a special data structure with which Spark Core is equipped.
SchemaRDDs are also known as DataFrames.Data Sources: Spark SQL can process data from various sources.
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Hadoop's MapReduce model is mostly used for disk-intensive operations, while Spark is a more versatile but more expensive in-memory processing architecture.
Despite some speculation that Spark will completely replace Hadoop due to the latter's processing capacity, they are intended to work together, rather than competing with one another A simplified version of the Spark-and-Hadoop architecture is shown below: Organizations that involve batch and stream analysis for various services will benefit from integrating the two methods.
As a consequence, Hadoop and, in particular, YARN, became a vital thread for connecting real-time processing, machine learning, and repeated graph processing.
Each file is divided into blocks and repeated several times through several machines, ensuring that the file can be restored from other blocks if one machine fails.
Data at rest is initially stored in HDFS, which is fault-tolerant due to Hadoop's architecture.
As an RDD is created, a lineage is created as well, which remembers how the dataset was created and, since it is permanent, can be rebuilt from scratch if necessary.
