Data Engineering Training Syllabus & Modules
Complete dynamic pacing topics, hand-on tools, and project milestones.
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Detailed Syllabus
Below is the comprehensive, module-by-module curriculum. As this training is strictly 1-to-1, we can adjust the syllabus scope or spend more time on specific modules based on your learning speed.
Course Prerequisites
Intermediate programming concepts (preferably Python) and basic SQL understanding.
Full Curriculum Structure
Module 1: Distributed Data Processing with Apache Spark 3.5
4 Weeks- Apache Spark 3.5 Architecture, RDDs, DataFrames & Spark SQL
- Large-Scale Batch ETL Pipelines with PySpark
- Distributed Joins, Partitioning & Memory Optimization
- Data Lake Storage Formats (Parquet, ORC, Delta Lake)
Module 2: Real-Time Event Streaming with Apache Kafka 3.8
4 Weeks- Kafka 3.8 Architecture: Brokers, Topics, Partitions & Producers/Consumers
- Stream Processing with Spark Structured Streaming
- Schema Registry, Avro Serialization & Event-Driven Pipelines
- Handling Late-Arriving Data, Watermarks & Windowing
Module 3: Workflow Orchestration & Data Warehousing
4 Weeks- DAG Authoring & Scheduling with Apache Airflow 2.9
- Data Modeling & Transformations with dbt (Data Build Tool)
- Cloud Data Warehousing with Snowflake & ClickHouse
- Data Quality Testing & Pipeline Monitoring
Module 4: Cloud Data Platform & Live Engineering Internship
4 Weeks- Building End-to-End Medallion Lakehouse Architectures (Bronze/Silver/Gold)
- Containerized Pipeline Deployments with Docker & AWS S3/EMR
- CI/CD for Data Pipelines with GitHub Actions
- Live Company Big Data Engineering Project Internship
Tools & Technologies Mastered
You will gain hands-on operational capability in these tools during screenshare coding loops, creating real repositories.
Hands-on Lab Assignments & Projects
- Project 1: Python ETL pipeline connecting web REST APIs to SQL databases.
- Project 2: Apache Spark data transformation pipeline processing JSON feeds.
- Project 3: Designing a star schema data warehouse in cloud environment.
- Project 4: Pipeline scheduling workflow deployed during the live internship.
Syllabus FAQs
When does the next 1-to-1 training intake start?
Intakes start twice monthly on the 1st and 15th. The next upcoming 1-to-1 intake starts on October 1, 2026 (with secondary intake on October 15, 2026).
Is the Data Engineering & ETL syllabus updated for modern industry standards?
Yes. Our syllabus is continuously updated to cover the latest versions of Apache Spark, PySpark, Apache Airflow, SQL, Data Warehousing, Kafka, Snowflake and modern production software engineering practices.
Can the syllabus be customized for my current skill level?
Because all sessions are strictly 1-to-1, your mentor can adjust module depth or accelerate topics based on your existing knowledge and target career goals.
Does the syllabus focus on theoretical concepts or hands-on coding?
Over 80% of training time is dedicated to live hands-on coding, terminal execution, pull request reviews, and building production applications.
Student Success Stories
Real feedback from students who completed our 1-to-1 virtual Data Engineering Training training.
"Transitioned from traditional DBA to Data Engineering. The 1-to-1 mentor helped me master PySpark and Hadoop HDFS configurations. We built ETL pipelines that pull from web APIs and store in cloud data lakes."
Nikhil P.
Data Engineer (Formerly DBA), Kharadi, Pune"The Apache Spark and advanced SQL modules are very comprehensive. My trainer explained distributed computing concepts so clearly. The hands-on staging pipeline deployment project gave me real confidence."
Swati T.
Platform Engineer, Wakad, Pune"I wanted to learn data warehousing and schema design. The individual virtual sessions allowed me to focus on building star schemas and optimizing SQL queries. The mentor's code feedback was invaluable."