As part of this course, you will learn all the Data Engineering Essentials related to building Data Pipelines using SQL, Python as well as Spark.

About Data Engineering

Data Engineering is nothing but processing the data depending upon our downstream needs. We need to build different pipelines such as Batch Pipelines, Streaming Pipelines, etc as part of Data Engineering. All roles related to Data Processing are consolidated under Data Engineering. Conventionally, they are known as ETL Development, Data Warehouse Development, etc.

Course Details

As part of this course, you will be learning Data Engineering Essentials such as SQL, Programming using Python and Spark. Here is the detailed agenda for the course.

Database Essentials - SQL using Postgres

Getting Started with Postgres

Basic Database Operations (CRUD or Insert, Update, Delete)

Writing Basic SQL Queries (Filtering, Joins, and Aggregations)

Creating Tables and Indexes

Partitioning Tables and Indexes

Predefined Functions (String Manipulation, Date Manipulation, and other functions)

Writing Advanced SQL Queries

Programming Essentials using Python

Perform Database Operations

Getting Started with Python

Basic Programming Constructs

Predefined Functions

Overview of Collections - list and set

Overview of Collections - dict and tuple

Manipulating Collections using loops

Understanding Map Reduce Libraries

Overview of Pandas Libraries

Database Programming - CRUD Operations

Database Programming - Batch Operations

Setting up Single Node Cluster for Practice

Setup Single Node Hadoop Cluster

Setup Hive and Spark on Single Node Cluster

Introduction to Hadoop ecosystem

Overview of HDFS Commands

Data Engineering using Spark SQL

Getting Started with Spark SQL

Basic Transformations

Managing Tables - Basic DDL and DML

Managing Tables - DML and Partitioning

Overview of Spark SQL Functions

Windowing Functions

Data Engineering using Spark Data Frame APIs

Data Processing Overview

Processing Column Data

Basic Transformations - Filtering, Aggregations, and Sorting

Joining Data Sets

Windowing Functions - Aggregations, Ranking, and Analytic Functions

Spark Metastore Databases and Tables

Desired Audience

Here is the desired audience for this course.

College students and entry-level professionals to get hands-on expertise with respect to Data Engineering. This course will provide enough skills to face interviews for entry-level data engineers.

Experienced application developers to gain expertise related to Data Engineering.

Conventional Data Warehouse Developers, ETL Developers, Database Developers, PL/SQL Developers to gain enough skills to transition to be successful Data Engineers.

Testers to improve their testing capabilities related to Data Engineering applications.

Any other hands-on IT Professional who wants to get knowledge about Data Engineering with Hands-On Practice.

Prerequisites

Logistics

Computer with decent configuration (At least 4 GB RAM, however 8 GB is highly desired)

Dual Core is required and Quad-Core is highly desired

Chrome Browser

High-Speed Internet

Desired Background

Engineering or Science Degree

Ability to use computer

Knowledge or working experience with databases and any programming language is highly desired

Training Approach

Here are the details related to the training approach.

It is self-paced with reference material, code snippets, and videos provided as part of Udemy.

One can either use the environment provided by us or set up their own environment using Docker on AWS or GCP or the platform of their choice.

We would recommend completing 2 modules every week by spending 4 to 5 hours per week.

It is highly recommended to take care of the exercises at the end to ensure that you are able to meet all the key objectives for each module.

Support will be provided through Udemy Q&A.

The course is designed in such a way that one can self-evaluate through the course and confirm whether the skills are acquired.

Here is the approach we recommend you to take this course.

The course is hands-on with thousands of tasks, you should practice as you go through the course.

You should also spend time understanding the concepts. If you do not understand the concept, I would recommend moving on and come back later to the topic.

Go through the consolidated exercises and see if you are able to solve the problems or not.

Make sure to follow the order we have defined as part of the course.

After each and every section or module, make sure to solve the exercises. We have provided enough information to validate the output.

By the end of the course, then you can come to the conclusion that you are able to master essential skills related to SQL, Python, and Spark.