Design and operate data pipelines using orchestrated tasks, Spark transformations, databases and simple data-quality checks. 1. Who can join Suitable for: Software engineers, Python/SQL developers and aspiring data engineers Mandatory prerequisites: Working Python and SQL; basic Git and command-line usage 2. Duration 64 live hours • Approximately 11 weeks at 6 hours per week; actual timetable to be confirmed. 3. Mode Classroom - Thaiyur / Kelambakkam, OMR Chennai (subject to schedule) Live Online (subject to schedule)
4. Syllabus — five modules
Module 1: Data Engineering Paradigms: Monolithic ETL vs. Modern ELT Architecture Learning outcome: Compare ETL/ELT choices and plan a source-to-target data flow. Module 2: Workflow Orchestration & DAG Construction using Apache Airflow Learning outcome: Create an Airflow DAG with tasks, retries and dependencies. Module 3: Distributed Big Data Processing with Apache Spark & PySpark Learning outcome: Transform sample datasets with Spark or PySpark. Module 4: Building Reliable Data Pipelines with Docker & PostgreSQL Learning outcome: Load validated output into PostgreSQL via reproducible containers. Module 5: Data Warehousing Concepts: Dimensional Modeling & Schema Evolution Learning outcome: Model downstream warehouse tables and handle schema changes. 5. Practical project Source capstone title: Build an End-to-End Orchestrated ETL Pipeline for E-Commerce Data Student deliverable: An e-commerce ETL pipeline loading raw files, transforming data with PySpark, scheduling jobs in Airflow and writing queryable tables.
6. What is included Live trainer-led sessions Practical assignments Sample code or templates, as applicable Project feedback Learning assessment Perizona course-completion certificate subject to assessment 7. Upcoming batch Start date: To be announced. Students can register interest; dates are shared only after confirmation.
