Data Engineering for Machine Learning

Batch and streaming pipelines, orchestration, data quality and feature stores — get clean, reliable data to your models.

⏰ 9 weeks🎯 Standard🧩 Certificate🖥 Any device · our GPUs

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🎯 About this course

Models are only as good as the data reaching them. This course teaches the engineering that makes data reliable: modelling with a clear grain, batch pipelines that can be re-run and backfilled safely, streaming concepts (event time, windows, watermarks), automated data-quality and drift checks, cloud warehouse and lake design, and point-in-time-correct features.

Every topic ends in an auto-graded lab that runs on your laptop with SQLite and pandas, and the applied studio has you take raw events through bronze, silver and gold layers with tests and a runbook.

Who it is for

Engineers and analysts moving into data and ML platform roles.

What makes it different

The emphasis is on correctness — idempotency, leakage and testing — not just on tool names.

By the end you will be able to

Model analytical data with a stated grain, facts and dimensions, and detect join fan-out.
Build idempotent, parameterised batch pipelines with correct dependency ordering, retries and backfills.
Explain and implement streaming concepts: event time, windows, watermarks and deduplication.
Design layered data-quality tests, data contracts and drift/freshness monitoring.
Choose storage formats and partitioning strategies for lakes and warehouses and estimate scan cost.
Produce point-in-time-correct training data and explain when a feature store is warranted.
Deliver a tested, idempotent pipeline from raw events to leak-free training features with an operations runbook.

Curriculum

Topics covered

Core topics 6 topics

Data modelling for analytics and ML
Model data for analytics and ML: grain, facts and dimensions, and the joins that follow.
Batch pipelines and orchestration
Batch pipelines and orchestration: DAGs, idempotency, retries and backfills.
Streaming ingestion basics
Streaming ingestion basics: events, windows, watermarks, late data and delivery guarantees.
Data quality and testing
Data quality and testing: contracts, checks, anomaly detection and where to put them in a pipeline.
Cloud warehouses and lakes
Cloud warehouses and lakes: storage formats, partitioning, pruning and the cost model behind them.
Feature stores
Feature stores: point-in-time correct training data and consistent online/offline features.

Course at a glance

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Where the time goes

Longer spokes mean more hours on that module.

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Sample questions from this course. Your progress is kept only in your browser.

Week by week

Drag the slider, or press play, to walk through the course and see what you will have built by each week.

Week 1 of 9

Wk 1–3

Pipelines and orchestration

You produceAn orchestrated batch pipeline with tests

Wk 4–6

Streaming and data quality

You produceA streaming ingestion job with data-quality checks

Wk 7–9

Feature store and project

You produceA documented feature pipeline feeding a model

What you'll build

Idempotent daily load with tests

A partition-scoped, backfillable load with automated quality checks.

Data-quality and drift report

Checks with severities and a PSI drift analysis on a feature.

Layered feature pipeline + runbook

Bronze/silver/gold pipeline producing a leak-free point-in-time training set, with runbook.

Why take this course

Build pipelines that are idempotent, tested and backfillable
Catch bad data automatically with checks and drift metrics
Avoid time-travel leakage with point-in-time features
Labs run locally on SQLite and pandas — no cloud bill

You will build

A star schema with a fan-out detector
A mini orchestrator with cycle detection and idempotent loads
A watermark-aware streaming aggregator
A data-quality runner with PSI drift detection
A point-in-time join with TTL
A bronze/silver/gold feature pipeline with a runbook

What you can do afterwards

Hand modelling teams data they can trust
Explain pipeline design trade-offs to stakeholders
Operate and debug data pipelines confidently

Career fit

🧭 Where this fits

Roles whose screened skills map to this course, and the skills it lets you show.

Skills you will build

Batch & streaming pipelines, orchestration
Data quality, testing, cloud warehouses
Feature stores for ML
Pipelines & orchestration, feature stores
Pipelines, orchestration & data quality
SQL & Python, data modelling

How to take it

💳 Delivery & fees

One course, four ways to attend. Fees are per course, in rupees excluding 18% GST (the GST-inclusive amount is shown too) with Canadian-dollar fees alongside.

Your fee includes our GPU lab infrastructure — no cloud credits to buy. See how we compare, with sources →