PySpark Tutorial 1, Introduction To Apache Spark, #SparkArchitecture, #PysparkTutorial, #Databricks

Introduction To Apache Spark, Pyspark Tutorial, #SparkArchitecture, #PysparkTutorial, #Databricks
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What is Apache Spark?
Apache Spark is a parallel processing framework and unified analytics engine that supports
in-memory processing to boost the performance of big-data analytic and machine learning.

What is Apache Spark used for?
Spark is used for many types of data processing. It supports ETL, interactive queries (SQL), advanced analytics (e.g. machine learning) and structured streaming over large datasets.
Spark integrates with many storage systems (e.g. HDFS, Cassandra, MySQL, HBase, MongoDB, S3). Spark is also pluggable, with dozens of applications, data sources, and environments.

Spark API’s
1) Resilient Distributed Dataset RDD - Spark1.0

An RDD stands for Resilient Distributed Datasets. It is Read-only partition collection of records. RDD is the fundamental data structure of Spark. It allows a programmer to perform in-memory computations on large clusters in a fault-tolerant manner. Thus, speed up the task
RDD Limitations: Handling structured data Unlike Dataframe and datasets, RDDs don’t infer the schema of the ingested data and requires the user to specify it.

2) DataFrame - Spark1.3
DataFrame data organized into named columns. For example a table in a relational database. It is an immutable distributed collection of data. DataFrame in Spark allows developers to impose a structure onto a distributed collection of data, allowing higher-level abstraction.
Dataframe Limitations: Compile-time type safety

3) Dataset - Spark1.6
It is an extension to Dataframe API, the latest abstraction which tries to provide best of both RDD and Dataframe. Datasets API provides compile time safety which was not available in Data frames.
DataSet Provides best of both RDD and Dataframe
RDD (functional programming, type safe),
DataFrame (relational model, Query optimazation , Tungsten execution, sorting and shuffling)
The Dataset API is available in Scala and Java

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