Self Paced – Fullstack Data Engineering – Azure | Databricks | AWS

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About Course

Training for Complete Data Engineering course with Big Data Hadoop and Spark. The course focuses on various aspects of Big Data frameworks like Hadoop and Spark. We will be learning about many tools in the Hadoop ecosystem such as hive, sqoop, flume, spark, and Kafka.

Course Content:

  • Azure Data Engineering
  • AWS Data Engineering
  • DataBricks Data Engineering
  • 6 End to End Projects
  • SparkStreaming
  • Python Programming
  • Apache Hadoop
  • Apache Hive
  • PySpark 500 Hands On Exercises
  • SparkSQL
  • Kafka
  • NoSQL

What Will You Learn?

  • Job interview preparation
  • Covers most of the contents for "Databricks Certified Developer For Apache Spark 3.0" Certification
  • In depth understanding of Hadoop Ecosystem components.
  • Resume support.
  • Enhanced understanding with Hands on exercises.

Course Content

Starter Kit

  • Support and Contact Guide
  • Steps To Install PST Application
  • Course Materials Access Guide
  • Study Roadmap Access Guide

M1 – Course Introduction

M2 – Hadoop Ecosystem (HISTORY LESSONS)
To understand how data engineering practices have evolved, you may review the following legacy sessions. For a modern, industry-aligned learning path, I recommend the sequence below: SQL → Python → PySpark → PySpark Projects Before beginning this path, I also suggest covering Hadoop fundamentals up to the YARN architecture, as it provides helpful context for distributed processing. This sequence will give you a strong foundation for the upcoming modules. The legacy sessions are included for those working with older systems who may still find them useful.

M3 – Python for Pyspark

M5 – Linux mastery

M4 – PySpark Essentials For Data Engineering

M5 – Spark Advanced – Optimization Techniques – Industry Scenarios

M6 – Full Stack Data Engineering using Azure Databricks | Part 1

Industry Level PySpark | Scenarios and Databricks Certification Practice

M6 – Kafka Essentials For Data Engineering

M7 – Industry Level PySpark | Spark Streaming

M8 – Data Modelling Essentials

M9 – FullStack Data Engineering Using DataBricks | Part 2

M10 – Azure Data Engineering Complete Course

M11 – AWS Data Engineering Complete Course

M12 – MongoDB NoSQL For Data Engineering

M13 – Complete Airflow For data Engineering

M14 – DevOps in DE | Version Control System Essentials

M15 – CI / CD for data Engineering Pipelines

Course End Projects | Live Projects

Course Material

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