Data Engineering and ETL Pipelines
Why colleges run this
Behind every analytics and ML team sits a data pipeline; data-engineering skills are scarce, well-paid, and rarely met in the standard curriculum.
Course outcomes
CO1
Distinguish batch and streaming approaches for a stated data requirement
K4CO2
Build an ingestion step that extracts data incrementally
K3CO3
Implement transformation logic that cleans and joins source data
K3CO4
Schedule a pipeline with dependencies and retry handling
K3CO5
Monitor a running pipeline and report its data quality
K4Modules tap a module for its theory & lab
Hours shown are the recommended 3-day format — module time scales to the duration you pick.
01The data-engineering landscape4 h
Theory
Batch versus stream, warehouses versus lakes, the data-engineer role
Lab
Match approaches to requirements
Output
Approach comparison
02Ingestion4 h
Theory
Sources, connectors, incremental extraction
Lab
Build an incremental ingestion step
Output
Ingestion job
03Transformation4 h
Theory
Cleaning, joining, aggregation, data modelling
Lab
Transform and model source data
Output
Transformation job
04Orchestration4 h
Theory
Scheduling, dependencies, retries with a workflow tool
Lab
Orchestrate the steps into a DAG
Output
Scheduled workflow
05Storage and loading4 h
Theory
Warehouse loading, partitioning, file formats
Lab
Load the output into a warehouse
Output
Loaded dataset
06Pipeline capstone4 h
Theory
Monitoring and data-quality checks end to end
Lab
Assemble a scheduled, monitored pipeline
Output
End-to-end pipeline
Every participant receives
Certificate of completion Course material LMS access Interview question bank Mock interview & viva practice Optional nasscom NSQF assessment