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Build Data Engineering Pipelines using SQL, Python and Spark


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.






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.


Who this course is for:

Computer Science or IT Students or other graduates with passion to get into IT

Data Warehouse Developers who want to transition to Data Engineering roles

ETL Developers who want to transition to Data Engineering roles

Database or PL/SQL Developers who want to transition to Data Engineering roles

BI Developers who want to transition to Data Engineering roles

QA Engineers to learn about Data Engineering

Application Developers to gain Data Engineering Skills

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