S3 → Glue → Databricks → Delta
New batch — Wednesday, 16 September 2026
AWS Data Engineering Syllabus
AWS Data Engineering Syllabus
10 Weeks · 50 Topics · 5 Projects
The complete week-by-week curriculum for the Databricks + AWS Data Engineering program — built around PySpark + AWS + Databricks + Streaming + DevOps + AI/Claude Code. Every weekday topic below is a live, hands-on class with Trainer Venu.
🕗 8:00 AM – 9:30 AM IST
📅 Monday – Friday
🎥 Recordings after every class
🎓 Weekend masterclasses
🛠️ 5 major projects
1200+Professionals trained
4.9 ★Google rating · 200+ reviews
50+Hiring companies
14+ yrsTrainer experience
10 weeks50 live topics + 5 projects
📋 Course Overview
What this program covers
Build strong PySpark foundations first, then connect them to cloud-native data engineering, lakehouse design, streaming, orchestration, governance, testing, performance, CI/CD and AI-assisted engineering productivity.
| Course focus | PySpark + AWS + Databricks + Streaming + DevOps + AI/Claude Code |
|---|---|
| Structure | 10 weeks, 50 weekday topics, weekend masterclasses and 5 major projects |
| Learning style | Concepts + live coding + architecture + troubleshooting + project implementation |
| Batch timing | 8:00 AM – 9:30 AM IST, Monday – Friday |
📚 Detailed Weekly Curriculum
Week-by-week breakdown — all 50 topics
Each numbered session is a focused class with demonstrations and coding exercises. Weekend sessions are used for AI-assisted engineering, metadata-driven design, architecture and project integration. Click any week to expand.
WEEK01PySpark & Databricks Foundations5 weekday topics · 5 topics + weekend masterclass
01PySpark & Databricks Introduction
02Databricks Functions (date, string, numeric)
03Window Functions (rank, lag, lead)
04Data Cleaning with Regex
05Spark-submit Internals
🎓 Weekend Masterclass / Lab — Claude Code + Prompt Engineering
Hands-on session exploring AI-assisted coding, generating/reviewing PySpark, debugging, and prompt patterns for data engineering.
WEEK02Advanced Spark Data Processing5 weekday topics · 5 topics + weekend masterclass
06Spark JSON Processing
07Spark JDBC (Oracle, MySQL, PostgreSQL via RDS)
08XML, CSV & Pandas Data Processing
09Parquet & ORC
10Delta Lake vs Iceberg
🎓 Weekend Masterclass / Lab — File Format & Performance Lab
Deep dive into columnar storage, execution plans, partition pruning, and optimizing read/write performance in Spark.
WEEK03AWS Storage & Integration5 weekday topics · 5 topics + weekend masterclass
11AWS Introduction (Accounts, Regions, IAM basics)
12S3 Buckets, Policies & Data Lake Design
13AWS Glue Introduction & Internals
14Glue Jobs + Databricks Integration
15Parameterization & Glue Data Catalog
🎓 Weekend Masterclass / Lab — Metadata-Driven Glue Pipelines
Design and parameterize reusable Glue pipelines driven by configuration metadata.
WEEK04AWS Glue Advanced & End-to-End Project5 weekday topics · 5 topics + weekend masterclass
16IAM Users, Roles, Policies
17Secrets Management (AWS Secrets Manager vs Databricks Secrets)
18Databricks + S3 Integration (IAM roles, instance profiles)
19Glue Advanced Concepts
Crawlers & schema evolutionPartitioning strategiesJob bookmarks & incremental loadsError handling, retries & best practices for large datasets
20End-to-End Batch Pipeline (S3 → Glue → Databricks → Delta)
🎓 Weekend Masterclass / Lab — Glue Metadata-Driven Pipelines
Advanced implementations of metadata-driven architectures utilizing AWS Glue components.
WEEK05Delta Lake & Databricks Optimization5 weekday topics · 5 topics + weekend masterclass
21Databricks Utilities (dbutils.fs, secrets, widgets)
22Delta Time Travel & Change Data Feed
23Medallion Architecture on AWS (Bronze/Silver/Gold)
24Declarative Pipelines with Databricks
25Databricks Optimization (OPTIMIZE, Z-Ordering, Photon)
🎓 Weekend Masterclass / Lab — Delta Sharing & Lakehouse Federation
Secure data sharing, federated query use cases, and governance considerations across lakehouses.
WEEK06AWS Orchestration & Databricks Deployment5 weekday topics · 5 topics + weekend masterclass
26AWS Lambda for ETL triggers
27AWS Step Functions for orchestration
28Advanced Step Functions (error handling, retries)
29Databricks Asset Bundles (DABs)
30Unity Catalog Advanced (governance, row filters, masks)
🎓 Weekend Masterclass / Lab — CI/CD with GitHub Actions + DABs
Implementing Git-based development, automated testing, and multi-environment deployment using DABs.
WEEK07Streaming on AWS5 weekday topics · 5 topics + weekend masterclass
31Spark Structured Streaming Introduction
32Kafka with Spark (MSK)
33NiFi + Kafka + Spark Streaming
34AWS Kinesis & EventBridge Streaming Project
35Auto Loader + Streaming Internals
🎓 Weekend Masterclass / Lab — Real-Time AWS Streaming Project
End-to-end streaming architecture with MSK/Kinesis into Databricks with checkpointing and state management.
WEEK08Airflow, Testing & Performance5 weekday topics · 5 topics + weekend masterclass
36Apache Airflow Introduction (on EC2/EKS)
37Airflow Advanced Orchestration (Glue + Databricks)
38Testing & Observability (CloudWatch + Databricks Monitor)
39Performance Tuning Deep Dive (joins, skew, AQE)
40Spark RDD Internals
🎓 Weekend Masterclass / Lab — Production Troubleshooting Workshop
Diagnose slow jobs, trace pipeline failures, analyze logs, and prioritize performance fixes.
WEEK09AWS Analytics & BI5 weekday topics · 5 topics + weekend masterclass
41Redshift Core Architecture
42Redshift vs Snowflake vs Databricks Lakehouse
43Real-Time Analytics with Kinesis + Lambda
44Semantic Models with QuickSight
45Preparing AI-Ready Analytics Data in AWS
🎓 Weekend Masterclass / Lab — AWS vs Databricks Architecture Workshop
Comparative analysis of AWS-native tools vs Databricks Lakehouse, evaluating cost, migration patterns, and architecture choices.
WEEK10AI-Assisted Engineering & Interview Prep5 weekday topics · 5 topics + weekend masterclass
46Claude Code for Data Engineers
47Prompt Engineering for ETL/SQL
48GitHub Copilot for Productivity
49GitHub Actions CI/CD with Databricks + AWS
50Interview Tips & Resume Preparation
🎓 Weekend Masterclass / Lab — Mock AWS Data Engineer Interview Clinic
Practice technical scenarios, architecture explanations, and mock interviews to prepare for AWS Data Engineering roles.
🛠️ Hands-On Projects
Project 1 — Glue + Databricks Batch Pipeline
Project 2 — Metadata-Driven Glue Framework
Project 3 — AWS Databricks Lakehouse
Project 4 — AWS Real-Time Streaming
Project 5 — CI/CD & Production Deployment
5 major projects you will build
Projects are deliberately aligned with the course sequence — you learn a concept, then implement it as part of a realistic, resume-ready pipeline.
Config → Glue → Databricks → Logging
S3 → Auto Loader → Delta → Unity Catalog
Kafka/Kinesis → Databricks → Delta → Gold
GitHub Actions → DABs → AWS Environments
🧰 Technology Stack
Tools and services covered
The curriculum focuses on data-engineering use cases. General cloud administration topics are covered only when they directly affect pipeline design, security, performance or operations.
| Area | Technologies / concepts |
|---|---|
| Core Engineering | PySpark, Spark SQL, JDBC, JSON, XML, Parquet, ORC, Delta Lake, Iceberg |
| AWS Storage | S3, Glue Data Catalog, RDS |
| AWS Integration | Glue, Lambda, Step Functions, IAM, Secrets Manager |
| AWS Streaming | Kinesis, MSK (Kafka), EventBridge, NiFi, Structured Streaming |
| Databricks | Databricks, Delta Lake, Unity Catalog, Lakeflow, DABs |
| Governance & Security | IAM, Secrets Manager, Unity Catalog |
| Orchestration & Ops | Airflow, Step Functions, Databricks Workflows |
| DevOps & AI | GitHub, GitHub Actions, Claude Code, Copilot, Prompt Engineering |
👨🏫 Your Trainer
Learn directly from Trainer Venu
Venu Katragadda
Founder, Sreyobhilashi IT · 14+ years in Big Data & Cloud Data Engineering
Venu has trained 1200+ working professionals on Spark, Databricks, AWS and Azure, and still teaches every session himself — no junior trainers, no recorded-only classes. Sessions are built around what actually breaks in production: skewed joins, failing streams, schema drift, cost blow-ups and the interview questions that follow.
PySpark & Spark internalsDatabricks & Delta LakeAWS Glue · EMR · Kinesis
Azure ADF · FabricKafka & Structured StreamingAirflowCI/CD with DABsClaude Code for DE
⭐ Google Reviews
Reviews from our AWS & Databricks students
Real, verified reviews from data engineers who trained with Venu — on Databricks, AWS, Azure, PySpark and streaming.
4.9
★★★★★
Based on 200+ Google 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 — AWS, Kafka, NiFi, Airflow and PySpark.”
AZ
Abhishek Zararia
Databricks · Cleared DE Professional Cert
✅ 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.”
DM
Dhevipriya Marimutbhu
AWS & Azure Databricks Training
✅ Verified Google Review
★★★★★
“This training has exceeded my expectations. Venu explains concepts clearly and uses hands-on examples that make the content easy to understand. I am learning a lot and would definitely recommend it.”
NL
Pataballa N V Lakshminarayana
Databricks Training
✅ 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, with practical examples.”
MM
Mahaboob Mulla
Databricks & AWS Training
✅ Verified Google Review
★★★★★
“Venu sir has explained end to end streaming project, data cleansing, and parsing various source data. This has helped me in my project work. The explanation on Spark architecture and other key concepts helped me understand Spark deeply.”
NS
Nimisha Shah
Databricks Streaming
✅ Verified Google Review
★★★★★
“I had a truly valuable experience with Sreyobhilashi's AWS & Azure Databricks Training & Placement Program. Hands-on coverage of Spark, Kafka, Flink, NiFi, Airflow, Azure and Snowflake.”
NT
Naveen Kumar Tavva
AWS & Azure Databricks Training
✅ Verified Google Review
Next AWS batch starts Wednesday, 16 September 2026
Live online, 8:00 AM – 9:30 AM IST, Monday to Friday. Limited seats so every student gets doubt-clearing time and cloud lab support.