Best Java Training Insitute with 100% Learning & Implementation

Apache Spark is a powerful platform that provides users with new ways to store and make use of big data.

In this course, get up to speed with Spark, and discover how to leverage this popular processing engine to
deliver effective and comprehensive insights into your data. Instructor Ben Sullins provides an overview of
the platform, going into the different components that make up Apache Spark.
He shows how to analyze data in Spark using PySpark and Spark SQL, explores running machine learning
algorithms using MLib, demonstrates how to create a streaming analytics application using Spark Streaming, and more.

Topics include:

Understanding Spark
Reviewing Spark components
Where Spark shines
Understanding data interfaces
Working with text files
Loading CSV data into DataFrames
Using Spark SQL to analyze data
Running machine learning algorithms using MLib
Querying streaming data
Connecting BI tools to Spark

Apache Spark Next Generation Big Data Framework
History of Spark
Limitations of MapReduce in Hadoop
Introduction to Apache Spark
Components of Spark
Application of In-Memory Processing
Hadoop Ecosystem vs Spark
Advantages of Spark
Spark Architecture
Spark Cluster in Real World
Demo: Running a Scala Programs in Spark Shell
Demo: Setting Up Execution Environment in IDE
Demo: Spark Web UI

Spark provides a machine learning library known as MLlib. Spark MLlib provides various machine learning algorithms such as classification, regression, clustering, and collaborative filtering. It also provides tools such as featurization, pipelines, persistence, and utilities for handling linear algebra operations, statistics and data handling.

Apache Spark provides a graph-parallel computation library in GraphX. Graph-parallel is a paradigm that allows representation of your data as vertices and edges. Spark GraphX provides a set of fundamental operators in addition to a growing collection of algorithms and builders to simplify graph analytics tasks.