Read
Short, practical reads on AI and data — from first principles to RAG, agents, MCP and AI security. Free, no sign-in.
25 guides in Data engineering
What data engineers do, how data flows from source systems to analysis and AI, and the core skills involved.
Data engineering 2 min read 21 Jan 2026 For people
The difference between transforming data before loading and after, and why modern warehouses shifted the default to ELT.
Data engineering 2 min read 20 Jan 2026 For people
The three main architectures for analytical data storage, what each is good for and how they are converging.
Data engineering 1 min read 19 Jan 2026 For people
Star schemas, facts and dimensions: how to design tables that make analysis fast, consistent and easy to understand.
Data engineering 2 min read 18 Jan 2026 For people
When to process data in scheduled batches and when real-time streaming is worth the added complexity.
Data engineering 1 min read 17 Jan 2026 For people
How orchestration tools schedule and manage data pipelines, handle dependencies and failures, and what to look for.
Data engineering 2 min read 16 Jan 2026 For people
Testing data pipelines like software: unit tests, schema checks, data quality assertions and freshness monitoring.
Data engineering 2 min read 15 Jan 2026 For people
How change data capture streams inserts, updates and deletes from databases, and why it beats repeated full extracts.
Data engineering 2 min read 14 Jan 2026 For people
Keeping production data trustworthy with automated checks, freshness alerts, anomaly detection and clear ownership.
Data engineering 2 min read 13 Jan 2026 For people
Agreements between data producers and consumers about schema, meaning and quality that stop pipelines breaking.
Data engineering 2 min read 12 Jan 2026 For people
Ownership, definitions, access control, quality and lifecycle: the building blocks of governing data without slowing everyone down.
Data engineering 2 min read 11 Jan 2026 For people
What Spark is, how it processes large datasets across many machines, and when you need it — or don't.
Data engineering 2 min read 10 Jan 2026 For people
How analytics engineering brings software practices to SQL transformations, and how dbt models, tests and documents data.
Data engineering 2 min read 9 Jan 2026 For people
The building blocks of cloud data platforms — storage, compute, warehouses, orchestration and governance — and how to choose.
Data engineering 2 min read 8 Jan 2026 For people
The decentralised approach to data architecture: domain ownership, data as a product, self-service platforms and federated governance.
Data engineering 2 min read 7 Jan 2026 For people
Common patterns for building reliable pipelines: idempotency, incremental loads, backfills and layered architectures.
Data engineering 1 min read 6 Jan 2026 For people
How to track changes to descriptive attributes over time in data warehouses, with SCD types explained.
Data engineering 1 min read 5 Jan 2026 For people
An introduction to Kafka's concepts — topics, partitions, producers and consumers — and when event streaming fits.
Data engineering 1 min read 4 Jan 2026 For people
Making analytical queries faster and cheaper: execution plans, indexes, partitioning and query patterns.
Data engineering 1 min read 3 Jan 2026 For people
The pipelines behind RAG and LLM features: ingestion, parsing, embedding, indexing and freshness.
Data engineering 1 min read 2 Jan 2026 For people