Tag: Data Engineering

All the articles with the tag "Data Engineering".

• 24 MIN READ•May 22, 2026

  Maintaining Apache Iceberg Tables: Compaction, Snapshot Expiration, and Orphan File Cleanup

  An in-depth guide to orchestrating maintenance operations on Apache Iceberg tables, covering bin-packing, sort-based, Z-Order compaction, snapshot expiration, and orphan file removal, with query acceleration details for the Dremio engine.

  Apache IcebergCompactionData Engineering
• 24 MIN READ•May 22, 2026

  Apache Iceberg with Spark: Create, MERGE, Upsert, and Evolve Tables End to End

  A comprehensive developer guide to configuring Apache Spark with Apache Iceberg, executing transactional writes, and managing schema evolution.

  apache sparkapache icebergdata engineering
• 21 MIN READ•May 22, 2026

  Common Misconceptions About Data Lakehouse and Apache Iceberg

  Addressing common search queries and reader confusion about Data Lakehouse architectures, Apache Iceberg catalogs, partitions, and lock-in.

  data lakehouseapache icebergdata engineering
• 5 MIN READ•Feb 18, 2026

  Data Engineering Best Practices: The Complete Checklist

  Best practices documents are easy to write and hard to use. They list principles without context, advice without prioritization, and rules without explaining...

  data engineeringbest practiceschecklist
• 5 MIN READ•Feb 18, 2026

  Data Modeling Best Practices: 7 Mistakes to Avoid

  A bad data model doesn't announce itself. It hides behind slow dashboards, conflicting numbers, confused analysts, and AI agents that generate wrong SQL. By ...

  data modelingdata engineeringbest practices
• 7 MIN READ•Feb 18, 2026

  Semantic Layer Best Practices: 7 Mistakes to Avoid

  Semantic layers don't fail because the technology is wrong. They fail because of design decisions made in the first two weeks : choices that seem reasonable ...

  semantic layerdata engineeringbest practices
• 5 MIN READ•Feb 18, 2026

  Pipeline Observability: Know When Things Break

  An analyst messages you on Slack – "The revenue numbers look wrong. Is the pipeline broken?" You check the orchestrator – all green. You check the target tabl...

  data engineeringbest practicesobservability
• 5 MIN READ•Feb 18, 2026

  Data Vault Modeling: Hubs, Links, and Satellites

  Dimensional modeling works well when your source systems are stable and your business questions are predictable. But what happens when sources change constan...

  data modelingdata engineeringdata vault
• 5 MIN READ•Feb 18, 2026

  How a Self-Documenting Semantic Layer Reduces Data Team Toil

  Every data team knows documentation is important. And almost every data team has a backlog of undocumented tables, unlabeled columns, and outdated descriptio...

  semantic layerdata engineeringdocumentation
• 5 MIN READ•Feb 18, 2026

  Testing Data Pipelines: What to Validate and When

  Ask an application developer how they test their code and they'll describe unit tests, integration tests, CI/CD pipelines, and coverage metrics. Ask a data e...

  data engineeringbest practicestesting
• 5 MIN READ•Feb 18, 2026

  Denormalization: When and Why to Flatten Your Data

  Normalization is the first rule taught in database design. Eliminate redundancy. Store each fact once. Use foreign keys. It's the right rule for transactiona...

  data modelingdata engineeringdenormalization
• 5 MIN READ•Feb 18, 2026

  Headless BI: How a Universal Semantic Layer Replaces Tool-Specific Models

  Your organization uses Tableau for executive dashboards, Power BI for operational reports, and Python notebooks for data science. Revenue is defined in Table...

  semantic layerdata engineeringheadless BI