Spark is a new platform that was intended. It reinforces these applications while retaining MapReduce's scalability and fault tolerance. Spark unveils an abstraction called resilient files to achieve these goals (RDDs). An RDD is a read-only collection of objects that are sectioned across a set of machines and can be rebuilt if a partition is lost.
You will start to learn the Spark basics. You will later learn the distinctions between Hadoop and Spark. Spark outdoes Hadoop by 10x in iterative machine learning jobs and can be used to query a large dataset interactively with a sub-second response time. Later in this course, you will learn about RDDs.
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Apache SparkSpark is based on the Hadoop distributed file system but does not use Hadoop MapReduce, but its own framework for parallel data processing, which starts with the insertion of data into persistent distributed data records (RDD) and distributed memory abstractions, which computes large Spark clusters in a way that fault-tolerant.
Because data is stored in memory (and on disk if necessary), Apache Spark can be much faster and more flexible than the Hadoop MapReduce task for certain applications described below.
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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