Descriptions
Databricks Data Engineering with AWS, Databricks has become the default lakehouse platform for data engineering on AWS, with over 60% of Fortune 500 companies relying on it. This course provides a complete path from fundamentals to production-grade deployment by covering workspace setup, Unity Catalog governance, Delta Lake internals (including ACID transactions, time travel, constraints, and performance tuning), Medallion Architecture pipelines, data ingestion via Lakeflow Connect (SaaS, database CDC, Auto Loader), and orchestration with Lakeflow Jobs.
Building on these foundations, you will apply your knowledge to a realistic capstone project for an e-commerce business, managing data from five source systems to deliver critical gold-layer analytics. The curriculum guides you through ingesting CDC and file-based data at scale, writing unit and integration tests with pytest, packaging pipelines using Databricks Asset Bundles (DABs), and automating testing, validation, and deployment to UAT environments via GitHub Actions CI/CD pipelines.
What you’ll learn
- Set up and govern a production Databricks workspace on AWS using Unity Catalog
- Master Delta Lake internals — ACID transactions, time travel, constraints, and performance tuning
- Design Medallion Architecture pipelines, first manually, then declaratively with Lakeflow Declarative Pipelines
- Ingest data at scale with Lakeflow Connect — SaaS connectors, database CDC, and Auto Loader
- Orchestrate production pipelines with Lakeflow Jobs — DAGs, retries, control flow, REST API and CLI
- Build a complete production lakehouse for a real e-commerce business, from ingestion through five gold-layer outputs
- Write unit and integration tests for Databricks pipeline code with pytest
- Package and deploy pipelines using Databricks Asset Bundles (DABs)
- Build a CI/CD pipeline with GitHub Actions that tests, validates, and deploys to a UAT environment
Who this course is for
- Practising data engineers who already know Spark and Python and want to move from writing pipelines to running them in production
- Data engineers and analytics engineers looking to add Databricks and AWS to their skill set with real, hands-on practice
- Engineers who want to see an industry-standard, end-to-end lakehouse project — including testing, DABs, and CI/CD — built from scratch
- Anyone already working with Databricks on AWS who wants to see it applied to a full production-style project
Specificatoin of Databricks Data Engineering with AWS
- Publisher : Udemy
- Teacher : Prashant Kumar Pandey
- Language : English
- Level : Intermediate
- Duration : 23 hours and 0 minutes
Content on 2026-7
Requirements
- Working knowledge of Apache Spark DataFrames, transformations, and basic Spark SQL
- Comfortable writing Python and SQL both are used throughout the course and capstone
- An AWS account (a free-tier account is enough to start; later chapters and the capstone incur modest AWS/Databricks usage costs)
- No prior Databricks experience required the course builds this from the ground up
- Basic familiarity with Git and the command line helps in the CI/CD and DABs modules, though it isn’t required going in
Pictures
Sample Clip
Installation Guide
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Quality: 1080p
Download Links
Password file(s): www.downloadly.ir
File size
11.19 GB