Module 01 · Free
The Modern Data Stack
Understand how modern data platforms are organized for analytics, machine learning, real-time systems, and AI applications.
7 lessons · 9 videos · 3h 52m- 01.01
From Data Warehouse to AI Platform
Explain how warehouses, lakes, lakehouses, streaming systems, feature stores, and vector databases fit into an end-to-end AI data platform.
- 01.02
Data Mesh and Domain Ownership
Map a centralized data platform into domain-owned data products with clear ownership, contracts, and consumers.
- 01.03
Lakehouse Architecture Overview
Identify when to use object storage, table formats, compute engines, catalogs, and warehouses in a lakehouse architecture.
- 01.04
Tooling Landscape for ML Engineers
Compare the roles of Snowflake, BigQuery, Databricks, Kafka, Spark, Airflow, dbt, Feast, Pinecone, and MLflow in a modern data stack.
- 01.05
Data Platform Reference Architecture
Draw a reference architecture for an ML data platform covering ingestion, storage, transformation, training data generation, serving, and monitoring.
- 01.06
Batch, Stream, and Serving Boundaries
Decide whether a data use case should be implemented with batch processing, streaming processing, online serving, or a hybrid architecture.
- 01.07
Data Contracts and Governance
Define a data contract that includes schema, freshness, ownership, quality checks, privacy constraints, and downstream ML consumers.