Big Data Engineering Masters – End to End In Depth, In depth course on Big Data Hadoop, Hive, Spark, HBase, MongoDB, Spark, Databricks, Kafka, Airflow and Projects.
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.