The modern data warehouse / lakehouse
Cloud warehouses (Snowflake, BigQuery, Databricks) separate storage from compute — store data cheaply, spin up compute on demand, scale independently. This is what makes ELT viable (transform in-warehouse) and analytics fast. The lakehouse (Databricks, open formats like Iceberg/Delta) blends the cheap, flexible storage of a data lake with the structure and performance of a warehouse. Cost levers: right-size compute, partition/cluster tables, and avoid scanning everything.
Feature stores
For ML, a feature store solves two hard problems: consistency (the same feature computed identically for training and serving, killing training/serving skew) and reuse (compute a feature once, share it across models). It provides an offline store (historical features for training, with point-in-time correctness so you don't leak future data) and an online store (low-latency features for real-time inference). Reach for one when features are shared across models/teams or you need real-time features; a single model may not need the overhead.
Data quality — the foundation everything sits on
Bad data silently poisons every downstream model and dashboard. Treat quality as a first-class concern:
- Tests/contracts — assert uniqueness, not-null, referential integrity, freshness, and value ranges (dbt tests, Great Expectations). Fail the pipeline on violation rather than shipping bad data.
- Freshness & volume monitoring — alert if a table didn't update or row counts jumped/dropped anomalously.
- Lineage — know where each column comes from, so when data breaks you can trace and fix the source (and assess blast radius).
- Observability — data-observability tools watch for anomalies across the warehouse.
The AI connection
For AI, data quality is model quality — training on dirty data, leaking labels, or serving stale features degrades the model invisibly (the drift/skew problems from the MLOps course). The data-engineering layer — clean models, idempotent pipelines, tested transformations, consistent features — is the unglamorous foundation that determines whether the AI on top actually works.