Udemy – Python, Databricks & Apache Spark: Complete ETL Engineering

Welcome to “Python, Databricks & Apache Spark: Complete ETL Engineering” course. Build powerful ETL pipelines using Python, Databricks and Apache Spark to turn raw data into trusted business insights. Python is one of the most powerful and widely used programming languages in data engineering and analytics. Its rich ecosystem, including libraries like Pandas, PySpark and NumPy, allows you to process data efficiently, automate workloads, and build scalable ETL systems.

Databricks is a unified analytics and data engineering platform designed to simplify big data processing and machine learning workflows. Built on Apache Spark, it provides an optimized environment for creating reliable, high-performance ETL pipelines, collaborative notebooks, and enterprise-grade data governance with Unity Catalog. In this course, we will take you through everything you need to know to master data engineering using Python, Databricks and Apache Spark, supported by diagrams, hands-on examples, and real ETL pipeline development.

Designed for all skill levels, this course takes you step-by-step from beginner concepts to advanced techniques. With practical demonstrations, clear explanations, and engaging projects, you’ll master the essential components of modern data engineering. This course will empower you to build efficient, production-ready data pipelines by fully leveraging Python and Databricks. You’ll gain the skills to clean, transform, validate and analyze large datasets, along with the problem-solving techniques to tackle real-world ETL challenges—giving you a competitive edge in the data engineering field.

Ready to build powerful ETL pipelines with Python and Databricks? This course is the perfect starting point!

What You Will Learn:

• ETL Pipeline Architecture (Python & Databricks):Understand how modern ETL workflows operate. Learn Databricks notebook logic, Spark job execution flow, and Python-based transformations.
• Python Foundations for Data Engineering:Master data manipulation with Python essentials, including Pandas, data types, file handling, functions, and automation workflows.
• Databricks Workspace & Notebooks:Learn how to navigate the Databricks interface, use notebooks, manage files, and configure clusters for Spark workloads.
• Apache Spark Fundamentals:Understand core Spark concepts—DataFrames, lazy evaluation, transformations, actions, partitions, and optimized execution.
• Delta Lake & Modern Data Storage:Learn Delta Lake concepts such as ACID transactions, Delta Log, time travel, schema evolution and optimized storage.
• Unity Catalog & Data Governance:Gain hands-on experience with secure data management, catalogs, schemas, tables, and permissions.
• Data Cleaning & Transformation (Bronze → Silver → Gold):Master medallion architecture using real datasets. Perform deduplication, missing value handling, normalization, validation and enrichment operations.
• Python + Spark Data Processing:Write efficient PySpark code for joins, aggregations, window functions, and large-scale transformations.
• Performance Optimization (Python & Spark):Learn best practices such as partitioning, caching, broadcast joins, and query optimization.
• Deploying ETL Workflows:Understand job scheduling, Databricks Jobs, cluster policies, and automation best practices.

By the end of this course, you’ll be confident in building robust and scalable ETL pipelines with Python and Databricks, fully prepared to tackle real-world data engineering projects.

DOWNLOAD LINK: Python, Databricks & Apache Spark: Complete ETL Engineering

Python_Databricks_Apache_Spark_Complete_ETL_Engineering.part01.rar – 1000.0 MB
Python_Databricks_Apache_Spark_Complete_ETL_Engineering.part02.rar – 1000.0 MB
Python_Databricks_Apache_Spark_Complete_ETL_Engineering.part03.rar – 1000.0 MB
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Python_Databricks_Apache_Spark_Complete_ETL_Engineering.part10.rar – 1000.0 MB
Python_Databricks_Apache_Spark_Complete_ETL_Engineering.part11.rar – 550.7 MB

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