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Ultimate Data Engineers Hands-On from Beginner to Advanced

Name: Ultimate Data Engineers Hands-On from Beginner to Advanced
Rating: 4.7 (31 reviews)

Master Python, SQL and Spark with Real World End to End Projects and Master Data Engineering from Scratch!

Highest Rated

Created byHema Sundar Thulugu

Last updated 5/2026

English

What you'll learn

You will master Python, SQL and Spark with Real World End to End Projects
You will Build end-to-end data engineering projects from scratch
You will be able to Confidently crack data engineering interviews
You will be prepared for roles such as Data Engineer, ETL Developer, and Big Data Engineer

Course content

7 sections • 83 lectures • 36h 21m total length

Python Introduction10:41
Master Python introduction for data engineering, covering foundations, data structures, error handling, logging, and debugging, with pandas, databases, and APIs, plus object-oriented programming, concurrency, and production-grade practices.
What is Python in Data Engineering20:00
Learn to build and connect extract, transform, and load tasks in a data engineering DAG, compare task flow API with classic API, and configure retries and DAG arguments.
Project Structuring25:47
Compare executors by highlighting Kubernetes executor's auto scaling and on demand worker creation. Explain how Kubernetes eliminates message brokers and scales with load, unlike local and salary executors.
Project Structuring Part - 222:20
Learn how a demo data pipeline dag uses start date and catch up settings, including future dates, and how tags enable easy UI search and filtering of DAGs.
Data Structures25:28
Explore core data structures in Python for data engineers, including lists, tuples, sets, and dictionaries, focusing on mutability, ordering, indexing, duplicates, deduplication, and configuration schemas.
Data Structures Part - 222:38
Explores deploying an Airflow pipeline with multiple workers and executors, focusing on a Docker-based local executor setup and Docker Desktop installation, and Kubernetes as a production option.
Raw API Response in Python26:14
Explore how a data pipeline's tasks: extract data, transform data, and load data, execute in a dependent sequence, where upstream failures block downstream tasks and UI triggers and monitors runs.
CSV Read and Write in Python27:12
Json in Python27:42
Function Flow in Data Engineering25:19
Non-idempotent in Python26:28
Pandas vs Spark vs SQL in Data Engineering27:32
Pandas Generate27:32
Pandas Clean22:47
Exception Flow in Python24:01
Exception Files in Python25:58
Log Levels in Python26:46
Learn python log levels from info to critical and how to debug with defensive fixes, then connect to Snowflake using the Snowflake connector Python and manage dependencies with pip.
Python - Snowflake Flow26:28
Snowflake Demo25:19
REST Basics in Python26:46
Class Structure26:14
Explore robust api retry patterns using raise for status, time.sleep, and error handling, then learn to structure data pipelines with a class that shares state across etl steps.
Data Pipeline in Data Engineering25:12
Load Data in Data Engineering23:38
Pipeline Config in Python29:37
Multithreading vs Multiprocessing34:33

SQL Introduction5:54
Learn how relational database management systems (RDBMS) and NoSQL differ, explore structure and transactions, and discover why PostgreSQL is a modern open-source choice for banking and e-commerce use cases.
Basics of SQL14:49
Database Objects in SQL20:06
Create Tables Queries23:06
Transitions & ACID Properties25:01
Select Command Deep Dive23:13
Order by, Limit and Fetch Clauses25:01
String, Numeric and Date Functions25:01
Subqueries (Inline, Correlated, Derived)24:24
Explore subqueries in depth, including inline, correlated, and derived tables, using aliases, joins, set operators, common table expressions, and window functions to build complex data queries.
Window Functions27:30

PySpark Introduction5:24
Explore how Apache Spark powers distributed data processing across multiple machines with PySpark, offering a high-level programming model to handle large-scale data and enable horizontal scaling.
Spark Architecture14:56
Understand the Spark architecture with a driver, executors, and cluster manager, including how the driver builds a logical plan and a physical plan via catalyst optimizer and distributes tasks.
Cluster Manager (The Resource Authority)17:02
Conceptual Diagram18:31
Workers in Spart Master22:19
Why Lazy Evaluation Exists24:18
Spark Setup Using Docker25:41
Running Application in Spark24:28
Reading CSV, JSON, Parquet23:13
Explore reading and writing csv, json, and parquet with spark, including infra schema, header handling, and partitioned outputs, and compare performance across formats and the benefits of parquet's columnar storage.
SQL and Dataframe25:55
Write spark dataframes to csv, json, and Parquet, and compare header handling and schemas. See Parquet's faster, columnar reads and smaller storage compared to csv and json.
StructType and DDL Schema Definitions23:44
Compare StructType and DDL schema definitions in Spark, weighing type safety, readability, and IDE collaboration while learning to define explicit schema for nested fields and nullability.
Data frame Transformations21:40
Predicate Pushdown Opportunity23:23
Joins in Spart - Broadcast vs Shuffle Join24:49
Windows Functions - Analytical Power with Hidden Shuffles25:48
Common Skew Mitigation Strategies27:04
Explore common skew mitigation strategies in Spark, including salting the key, partition by, and using window functions, broadcast joins, and filtering before aggregation.
Data Writing & Storage Layer22:28
Master the Spark data writer API to materialize dataframes to external storage using parquet. Understand writing modes: append, overwrite, ignore, and error, plus dynamic partition overwrite with CRC files.
Tables, Metastore & Spark SQL22:23
Explore Spark tables in the metastore, including managed and external types. See how declarative Spark SQL and the programmatic DataFrame API query and manage them.

Power BI Introduction10:55
Power BI Fits in Modern Data Stack17:21
Explore Power BI desktop and service integration, including publish workflows, scheduled refresh, and access control, plus import mode, direct query, and live connections to data sources.
Data Ingestion & Power Query20:52
Build visuals with Your data25:21
Visualization with Power BI in Data Engineering25:21
Learn to build DAX measures, apply row and filter contexts, and assemble KPI visuals and charts in Power BI for data engineering workflows.
Cardinality in Power BI19:41

Snowflake + DBT + Airflow Introduction26:29
Database Initialize25:03
Initialize a secure data stack with Postgres and MinIO using docker-compose and a .env file to hide credentials, then connect with DBeaver and design tables via ERD.
Data Generation20:44
Store Data in Bucket24:01
This lecture shows how to build a Python-based fake data generator using Faker to populate Postgres with customers, accounts, and transactions, with env-based credentials and looped updates.
DBT Initialize25:34
Initialize a Postgres data set with 50 customers, 100 accounts, and 250 transactions; configure a Kafka Debezium stream and a Python consumer to store data as parquet files in MinIO.
DBT Transformation26:43
Data Pipeline24:56
Data Explorer26:43
Generate Final Table24:42
Git Push30:10
CICD Implementation19:28
Implement CI/CD workflows with pull requests and main-branch merges, then deploy via continuous deployment while validating with automated tests, and build Power BI dashboards from Snowflake data modeled with DBT.
Database Creation19:02

Requirements

You do not need to have experience

Description

Step into the world of modern data engineering with this comprehensive, hands-on course designed to take you from absolute beginner to advanced data engineer. In today’s data-driven world, organizations rely on skilled professionals who can build, manage, and optimize large-scale data systems—and this course is your complete roadmap to mastering those skills.

You will start with the fundamentals of data engineering, understanding how data flows through systems and how to design efficient data pipelines. From there, you will progressively dive into real-world tools and technologies used by top companies, gaining practical experience at every step.

This course is built around hands-on projects and real-world scenarios, ensuring that you don’t just learn theory but actually build scalable data solutions. You will work with structured and unstructured data, process large datasets, and design robust pipelines that power analytics and machine learning systems.

Throughout the course, you will master:

* Programming for Data Engineering** using Python

* SQL and Data Modeling** for efficient data storage and retrieval

* ETL & ELT Pipelines** to transform and move data

* Big Data Technologies** like Apache Spark

* Data Warehousing** concepts using modern platforms

* Workflow Orchestration** with tools like Apache Airflow

* Cloud Data Engineering** fundamentals (AWS/GCP/Azure concepts)

* Real-Time Data Processing** basics

* Data Lakes and Lakehouse architectures**

You will also learn how to:

* Design scalable and fault-tolerant data pipelines

* Optimize performance for large-scale data processing

* Handle data quality, reliability, and monitoring

* Work with industry-standard tools and best practices

By the end of this course, you will:

* Build end-to-end data engineering projects from scratch

* Gain job-ready skills aligned with industry demands

* Confidently crack data engineering interviews

* Be prepared for roles such as Data Engineer, ETL Developer, and Big Data Engineer

Whether you are a beginner exploring data careers or a professional looking to upgrade your skills, this course provides everything you need to become a highly skilled, industry-ready Data Engineer.

Who this course is for:

Everyone who wants to master Data Engineering concepts and become a data engineer

Ultimate Data Engineers Hands-On from Beginner to Advanced

What you'll learn

Explore related topics

Course content

Introduction25 lectures • 10hr 32min

SQL for Data Engineering10 lectures • 3hr 34min

PySpark for Data Engineering18 lectures • 6hr 33min

Snowflake for Data Engineering6 lectures • 3hr 33min

Apache Airflow Masterclass6 lectures • 5hr 16min

Power BI For Data Engineering6 lectures • 2hr

End-to-End Banking Data Engineering Project _ Snowflake + DBT + Airflow12 lectures • 4hr 54min

Requirements

Description

Who this course is for: