Spark Tutorial For Beginners | Big Data Spark Tutorial | Apache Spark Tutorial | Simplilearn

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This Spark Tutorial For Beginners will give an overview on the history of spark, what is spark, Batch vs real-time processing, Limitations of MapReduce in Hadoop, Introduction to Spark, Components of Spark Project and a comparison between Hadoop ecosystem and Spark. Let's get started with this Big Data Spark Tutorial!

This Apache Spark Tutorial video will explain:
1. History of Spark - 00:00
2. Introduction to Spark - 04:02
3. Spark Components - 05:00
4. Spark Advantages - 12:31

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Apache Spark is an open-source cluster-computing framework. It is an analytics engine that was originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the Apache Software Foundation, which has maintained it since. It is used for processing and analyzing large amounts of data. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.

About Simplilearn's Big Data and Hadoop Certification Training Course:

The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.

Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL.

As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. This Big Data course also prepares you for the Cloudera CCA175 certification.

What are the course objectives of this Big Data and Hadoop Certification Training Course?

This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail

Who should take up this Big Data and Hadoop Certification Training Course?

Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists

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