Data
insights
Practical notes on Data, newest first.
Building a Data Catalog with Clear Definitions, Ownership, and Usage
A practical framework for documenting data definitions, accountable owners, approved uses, and operational constraints.
Balancing Query Speed and Cost with Warehouse Partitioning
A practical framework for choosing partition and clustering strategies that reduce scans without creating excessive maintenance overhead.
Compacting Small Data Lake Files Without Disrupting Queries
Use snapshots, bounded rewrites, and retention windows to compact data lake files without interrupting queries, streaming writes, or updates.
Reconciling Data Integrity Between Sources and Warehouses
Design traceable, rerunnable reconciliation controls that expose missing, duplicated, delayed, and incorrectly transformed data.
Processing Late-Arriving Data by Event Time
A practical design for assigning late data to the correct period using watermarks, idempotent writes, and auditable corrections.
Preserving Queryable History When Dimension Data Changes
Use versioned dimensions, explicit time boundaries, and stable keys to reproduce the business context that existed when an event occurred.
Versioning Business Metrics Across Reports and APIs
A practical approach to metric contracts, compatibility rules, and controlled migrations across dashboards, APIs, and enterprise systems.
Turning Data Retention Policies into Enforceable Pipeline Rules
Translate policy language into classification, expiry, deletion propagation, and audit controls that every data pipeline can enforce.
Deidentifying Personal Data for Testing, Analytics, and AI
A practical framework for preserving useful test and analytical data while reducing reidentification and AI leakage risks.
Backfilling Historical Data Without Duplicates or Report Errors
A practical approach to idempotent keys, time boundaries, staged merges, report isolation, validation, and rollback for historical backfills.
Choosing a Data Warehouse or Lakehouse for Your Workload
A practical framework for choosing a warehouse or lakehouse based on queries, governance, team capability, and operating cost.
Choosing Log-, Timestamp-, or Event-Based CDC
A practical framework for selecting CDC by consistency, latency, delete capture, source constraints, recovery behavior, and operating cost.
Have a project in mind?
Tell us your industry, current systems and budget range. Free 30-minute consultation.