Deep dives into data engineering pipelines, architecture patterns, analytics at scale, and data governance — written by practitioners, for practitioners.
A comprehensive guide to implementing the Medallion (multi-hop) architecture in modern data lakehouses. We cover raw ingestion, cleansing, and curated business layers with real Databricks examples.
How to organize dbt projects at scale — folder conventions, naming, testing strategies, and documentation that doesn't go stale.
Practical patterns for data validation, anomaly detection, and quality scoring — without drowning your team in YAML configs.
From partition tuning to broadcast joins and AQE — the optimizations that deliver real-world performance gains on large datasets.
A clear-eyed comparison of two dominant architectural philosophies — organizational vs. technological approaches to scaling data.
Master RANK, DENSE_RANK, LAG, LEAD, NTILE, and running aggregates with real business scenarios — cohort retention, moving averages, and more.
Evaluating Alation, Datahub, OpenMetadata, and Collibra — what matters for discoverability, lineage, and governance at different team sizes.
End-to-end guide to building low-latency streaming pipelines: schema registry, exactly-once semantics, and state management.
The analytics engineer role explained: where it sits between data engineering and BI, what skills you need, and what a typical day looks like.