Data Engineering for AI
AI is only as good as the pipelines feeding it.
Build the data foundation every AI system depends on — batch and streaming pipelines, lakehouse architecture, quality gates, and the retrieval infrastructure that modern LLM applications run on.
- Duration
- 14 weeks
- Weekly effort
- 10 hrs/week
- Level
- Intermediate
- Format
- Live cohort + cloud labs
- Mode
- Classroom in Marathahalli & Live online
- Typical range
- ₹10–32 LPA
Outcomes
What you will be able to do
Not topics covered — capabilities you can demonstrate in an interview and on day one of the job.
- Design and operate batch and streaming pipelines that survive real traffic
- Build a lakehouse with proper table formats, partitioning and time travel
- Enforce data quality and contracts so downstream models can be trusted
- Model data for analytics and for AI retrieval workloads
- Control cost and performance across storage, compute and orchestration
Curriculum
4 modules over 14 weeks
Every module ends in working code that is reviewed by a mentor before you move on.
01Foundations & Modelling
Weeks 1–4
Foundations & Modelling
Weeks 1–4
- Modern data architecture: warehouse, lake, lakehouse and when each fits
- Dimensional modelling, slowly changing dimensions and normalisation trade-offs
- Advanced SQL and warehouse query optimisation
- Python for data engineering: idempotent, testable, restartable jobs
02Batch Pipelines at Scale
Weeks 5–8
Batch Pipelines at Scale
Weeks 5–8
- Apache Spark: the execution model, partitions, shuffles and tuning
- Apache Iceberg and Delta table formats, schema evolution and time travel
- Airflow orchestration, backfills, dependencies and SLAs
- dbt for transformation, testing and documentation
03Streaming & Real Time
Weeks 9–11
Streaming & Real Time
Weeks 9–11
- Kafka fundamentals: topics, partitions, consumer groups, delivery semantics
- Stream processing, windowing and late-arriving data
- Change data capture from operational databases
- Lambda and kappa architectures in practice
04Quality, Governance & AI Workloads
Weeks 12–14
Quality, Governance & AI Workloads
Weeks 12–14
- Data contracts, validation with Great Expectations, and observability
- Lineage, cataloguing, PII governance and access control
- Building ingestion and embedding pipelines for RAG systems
- Cost engineering, and the capstone platform build
Capstone
What you walk out with
Portfolio work substantial enough to carry a technical interview on its own.
A production lakehouse ingesting both batch and streaming sources, with quality gates, orchestration and an analytics layer
An automated document-ingestion pipeline that keeps a vector index fresh for an LLM application
Tools & stack
Roles this leads to
- Data Engineer
- Analytics Engineer
- Data Platform Engineer
- Streaming Engineer
Who it is for
- Developers and SQL analysts moving into data engineering
- ETL and warehouse professionals modernising their stack
- ML engineers who keep getting blocked by data problems
Prerequisites
- Working SQL knowledge
- Basic Python
Enrol
Get the full syllabus for Data Engineering for AI
Request the detailed module-by-module syllabus, current batch dates and fee structure. A counsellor will also check whether this is the right programme for your background.
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