Module 1: Foundations of Data Engineering for AI
Before a model can be trained, before a feature vector can be computed, before an inference endpoint can serve a prediction - data must move. It must be collected, cleaned, transformed, validated, and delivered reliably. Data engineering is that discipline.
This module gives you a complete mental model of modern data engineering for AI systems. You will learn how pipelines are designed, where they break, what patterns experienced engineers use to make them reliable, and how the data layer connects to every other part of the ML system.
What This Module Covers
Lessons 1 and 2 are conceptual foundations. Lessons 3 and 4 go deep on the two core execution models: batch and streaming. Lesson 5 treats data quality as a first-class engineering concern. Lesson 6 connects DE directly to the ML feature layer. Lesson 7 covers orchestration, which is the glue holding every pipeline together in production.
Lessons at a Glance
| # | Title | Core Concept |
|---|---|---|
| 01 | The Data Engineering Landscape | How the modern data stack maps to AI training and serving |
| 02 | Data Pipeline Patterns | ETL vs ELT, Lambda vs Kappa, idempotency, backfill |
| 03 | Batch Processing at Scale | Spark internals, partitioning strategy, handling data skew |
| 04 | Stream Processing for ML | Kafka, Flink, exactly-once semantics, watermarking |
| 05 | Data Quality and Contracts | Schema evolution, data contracts, anomaly detection |
| 06 | Feature Engineering and Feature Stores | Offline vs online stores, point-in-time correctness, backfill |
| 07 | Pipeline Orchestration | DAGs, retries, SLAs, dependency management at scale |
Key Concepts at a Glance
Medallion architecture - the pattern of organizing data into raw (bronze), cleaned (silver), and aggregated (gold) layers. Each layer has a defined owner, schema contract, and quality guarantee. Most AI teams adopt this without naming it.
Idempotency - a pipeline is idempotent if re-running it produces the same result. This is not optional. Pipelines fail and must be retried. If re-running changes the output, you have a data correctness bug.
Point-in-time correctness - when training a model on historical data, you must only use features that were available at the time of the label event. Joining future data into training features is called target leakage and produces models that work in training and fail in production.
Data contracts - explicit agreements between the team producing a dataset and the teams consuming it, covering schema, nullability, freshness, volume ranges, and value distributions. Without contracts, schema drift silently breaks downstream pipelines.
Watermarking - in stream processing, late-arriving events are the norm, not the exception. A watermark is the system's best estimate of how far behind real-time it is willing to wait before closing a time window. Too short: you drop valid data. Too long: latency increases.
Feature freshness - how stale are the features when a model uses them? A recommendation model that computes user preferences once per day may be fine. A fraud detection model that uses features computed 24 hours ago is useless. Freshness requirements drive architecture choices.
When This Knowledge Is Needed in Production
You need this module's knowledge every time:
- An ML model is underperforming and the root cause is dirty training data, not the model itself
- A pipeline fails at 3 AM and you need to diagnose and backfill without corrupting downstream tables
- A new feature needs to be added to a recommendation model and you need to ensure point-in-time correctness
- A streaming pipeline is producing inconsistent aggregates because late-arriving events aren't being handled correctly
- An ML team is blocked because the data team's pipelines have no SLAs and keep delivering data late
- You are interviewing for a senior ML engineer, data engineer, or MLOps role and need to explain trade-offs in pipeline design
This module treats data engineering as the discipline it is: a first-class engineering domain where the same rigor applied to software systems must be applied to data systems. Models are only as good as the data that trained them and the features that serve them.