Data now surround us. People upload videos, take pictures with their phones, text pals, change their Facebook status, write comments on websites, click on advertisements, and so on. Machines, too, are producing and storing an increasing amount of data. Specialized tools are required to process such massive datasets. This course covers both Hadoop and Spark, two fundamental frameworks that provide essential tools for completing massive big data projects.

This course has been designed to cater to all types of learners who want to get into the vast field of Big Data Engineering. Be it theory , hands on or projects, everything is covered in detail without missing any topics in the field.

You will learn the following in details

Introduction to Big Data and Data Engineering- Big Data Engineering

Introduction to Distributed Systems - Hadoop and MapReduce -Big Data Engineering Introduction

Map Reduce & YARN -Big Data Hadoop Map Reduce YARN, Hadoop Map Reduce Hands On

Hive - Theory and Hands On

Hive Hands On- Theory and Hands On

NoSQL and Hbase- Theory and Hands On

Sqoop- Theory and Hands On

Spark- Theory and Hands On

Spark - Introduction

Big Data Engineering using PySpark- RDDs

Spark hands on - RDD

Big Data Engineering using PySpark- Core, Internals, Architecture

Apache Spark Actions_ Transformations

Apache Spark Caching

Big Data Engineering using PySpark- Shared Vars , Coalesce Repartition

Big Data Engineering using PySpark- Dataframes

Spark hands on - Dataframe

Spark hands on - Databricks

Big Data Engineering using PySpark- Catalyst& Tungsten

Spark ML- Theory and Hands On

Spark Streaming- Theory and Hands On

Kafka- Theory and Hands On

Apache Airflow - Workflow Management Platform- Theory and Hands On

Big Data Projects - 3 end to end hands on projects

Big Data Enterprise Architecture