Live online · 90 hours · Beginner to Advanced
AWS Data Engineering Course — 9 Weeks across Glue, EMR, Redshift, Kinesis and MWAA
Nine structured weeks from PySpark foundations to production AWS pipelines — Glue, Lambda, Step Functions, EMR, Redshift, MSK/Kinesis, Airflow and Databricks.
What you will be able to do
- Design a lakehouse on S3 with Glue Catalog, Lake Formation permissions and Iceberg tables
- Build ETL with Glue (Spark and Python shell), EMR Serverless and Step Functions
- Model and tune Redshift: distribution and sort keys, RA3, Serverless, Spectrum, materialised views
- Stream with Kinesis Data Streams, Firehose, MSK (Kafka) and Flink/Managed Service for Apache Flink
- Orchestrate with Amazon MWAA (Airflow) and monitor cost with CUR + Cost Explorer
- Pass AWS Certified Data Engineer – Associate
Curriculum — 9 weeks · 50 weekday sessions + 9 weekend workshops · 5 major projects
Sessions run live; every session is recorded. Labs are hands-on from week one.
- PySpark Introduction & Spark Architecture
- Databricks Introduction
- PySpark Built-in Functions
- Window Functions
- Data Cleaning with PySpark
- Weekend lab: Claude Code + Prompt Engineering for Data Engineers
- spark-submit Internals
- JSON Processing
- JDBC Data Processing
- XML, CSV and Pandas Integration
- Parquet, ORC, Avro, Delta & Iceberg
- Weekend lab: File-Format & Performance Lab
- AWS Introduction for Data Engineers
- Amazon S3 Deep Dive
- AWS IAM
- EC2 & Amazon RDS for Data Engineers
- AWS Glue Introduction
- Weekend lab: S3 -> Glue Crawler -> Glue PySpark -> S3 Project
- AWS Lambda
- Event-Driven Data Pipelines
- AWS Glue Advanced
- AWS Step Functions
- Monitoring & Logging
- Weekend lab: Event-Driven AWS Data Pipeline
- Databricks + Amazon S3 Integration
- Databricks Utilities
- Delta Lake Advanced
- Medallion Architecture on AWS
- Databricks Performance Optimization
- Weekend lab: Lakehouse Federation + Delta Sharing
- Amazon EMR
- Amazon Redshift Basics
- AWS Lake Formation
- Databricks Asset Bundles (DABs)
- Databricks Lakeflow + Unity Catalog
- Weekend lab: Dev -> Test -> Prod Deployment Lab
- Spark Structured Streaming
- Apache Kafka with Spark
- Amazon MSK
- Amazon Kinesis
- Auto Loader & Streaming Internals
- Weekend lab: AWS Streaming Project
- Apache Airflow Introduction
- Airflow Advanced Orchestration
- Amazon MWAA
- Testing & Observability
- Spark Performance Tuning & RDD Internals
- Weekend lab: Production Troubleshooting Workshop
- Snowflake Fundamentals
- Snowflake Stages & Data Loading
- Snowflake Advanced Overview
- Claude Code for Data Engineers
- Prompt Engineering
- Codex & GitHub Copilot
- Git & GitHub
- GitHub Actions CI/CD
- AWS Data Engineering Architecture
- Interview & Resume Preparation
- Weekend lab: Mock Architecture + Interview Clinic
Hands-on projects
You leave with 5 portfolio projects you can demo in an interview — not toy notebooks.
AWS Batch Data Engineering Pipeline
JDBC ingestion through Glue PySpark into medallion processing, then warehouse publishing with validation.
Event-Driven Serverless Pipeline
Event-driven design on Lambda and Step Functions with retries, failure handling, serverless orchestration and operational monitoring.
AWS Databricks Medallion Lakehouse
Incremental ingestion into Delta Lake on S3 with data-quality rules, Unity Catalog governance and layout optimisation.
Real-Time Streaming Pipeline
Kafka/MSK and Kinesis streaming with checkpointing, late-data handling and streaming transformations.
Metadata-Driven Orchestration
Reusable Airflow framework with dynamic task generation, parameterisation and observability.
Tools and technologies covered
Who this course is for
- Data engineers standardising on AWS
- ETL developers migrating from on-prem
- Cloud engineers adding data skills
- DEA-C01 candidates
Prerequisites
- SQL and basic Python
- No prior AWS experience needed — Week 3 covers AWS, S3 and IAM from zero
- An AWS account (free tier); we show how to set a $20 budget alarm before you touch anything
Frequently asked questions
Labs are designed for the free tier plus a few dollars. We set budget alarms in the first session and every lab ends with a teardown checklist. Expect roughly $15–$40 total across the course if you follow the teardown steps.
No — it is the data specialisation. You'll learn the IAM/VPC/S3 you need, but not EC2 fleet management or hybrid networking in depth.
Both. Week 7 teaches Kafka fundamentals properly (partitions, consumer groups, offsets, compaction, exactly-once) and then applies them on Amazon MSK, alongside Kinesis for comparison.
AWS Certified Data Engineer – Associate (DEA-C01). Note that the older Data Analytics – Specialty (DAS-C01) has been retired — do not buy prep material for it.
Venu Katragadda or a course advisor will call or WhatsApp you within one working day with the full syllabus, batch dates and fees. For anything urgent, WhatsApp +91-9247159150.
Free download · PDF
Download the full 90-hour syllabus
Every module, every hour, every lab and all three projects — the same document we hand to corporate clients. No email verification loop; the PDF downloads the moment you submit.
- Every week broken down topic by topic with hours
- All 5 portfolio projects in full
- Prerequisites, tools list and certification mapping
- Fees, EMI options, batch timings and the refund policy
What students say about Venu Katragadda
Verified Google reviews from Sreyobhilashi IT students. Read all 320+ reviews →
“Recently took Databricks classes with Venu to upskill in trending technologies, and the experience exceeded all expectations. While I initially sought guidance only on Databricks, Venu provided in-depth training across the entire ecosystem.”
Databricks · Cleared DE Professional Cert · Verified Google review
“I recently completed the Data Engineering course on Databricks and AWS. Venu Sir delivers instruction at the next level, focusing on high-performance learning. He explains every concept clearly and thoroughly, accompanied by practical examples.”
Databricks & AWS Training · Verified Google review
“I had a truly valuable experience with Venu's Spark training along with AWS & Azure Databricks Training. He is highly knowledgeable, and the sessions are very well structured with extensive hands-on coverage.”
AWS & Azure Databricks Training · Verified Google review
Foundation course or masterclass?
We run two tiers. Most people should start with the foundation course on our sister site and step up later — this page is the advanced one.
Foundation · databrickstraining.in
AWS Data Engineering Training
₹40,000₹25,000
- 70 hours of live instruction
- Covers the job-ready core of the stack
- Best if you are new to the platform or changing careers
- Same trainer, same teaching style
Masterclass · this page
AWS Data Engineering Masterclass
₹40,000₹25,000
- 90 hours — roughly 25–30 extra hours of depth
- Internals, performance tuning and cost engineering modules
- Three reviewed portfolio projects instead of guided labs
- Architecture review and certification drill included
- Best if you already work with the stack and want senior-level depth
Not sure which fits? WhatsApp +91-9247159150 and Venu Katragadda will tell you straight — including when the cheaper one is the right answer.
Related masterclasses
Databricks Masterclass
Eight structured weeks from Spark/PySpark foundations to a production Lakehouse you built yourself — Delta Lak…
PySpark Masterclass
The deepest PySpark course we teach — internals, tuning, testing and streaming, not just the DataFrame API.…
Azure Data Engineering Masterclass
Ten structured weeks from PySpark foundations to production Azure pipelines — ADF, ADLS Gen2, Databricks, Delt…