Posts - Page 36
All the articles I've posted, page 36.
- 11 MIN READ•Mar 1, 2026
Dremio's Built-in Open Catalog: Your Zero-Configuration Apache Iceberg Lakehouse
Dremio Cloud's built-in Open Catalog gives you a managed Iceberg lakehouse with no setup. Create tables, use branching, time travel, and autonomous reflections.
dremioconnectorsdata federation - 10 MIN READ•Mar 1, 2026
Connect Dremio Software to Dremio Cloud: Hybrid Federation Across Deployments
Connect self-managed Dremio Software to Dremio Cloud for hybrid federation, incremental cloud migration, and consolidated governance. Setup steps included.
dremioconnectorsdata federation - 5 MIN READ•Feb 18, 2026
Data Engineering Best Practices: The Complete Checklist
A prioritized data engineering checklist covering pipeline design, data quality, idempotency, schema management, testing, and observability, with reasons.
data engineeringbest practiceschecklist - 5 MIN READ•Feb 18, 2026
Data Modeling Best Practices: 7 Mistakes to Avoid
Seven data modeling mistakes and fixes: undefined grain, cryptic names, skipped conceptual models, over-normalizing, thin docs, and ignored governance.
data modelingdata engineeringbest practices - 7 MIN READ•Feb 18, 2026
Semantic Layer Best Practices: 7 Mistakes to Avoid
Seven early design mistakes that sink semantic layers, from defining metrics in several places to securing data only in the BI tool, and how to avoid each one.
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.
data engineeringbest practicesobservability - 5 MIN READ•Feb 18, 2026
Data Vault Modeling: Hubs, Links, and Satellites
Data Vault modeling explained: Hubs hold business keys, Links hold relationships, and Satellites hold history, plus how to expose Data Vault data for analytics.
data modelingdata engineeringdata vault - 5 MIN READ•Feb 18, 2026
How a Self-Documenting Semantic Layer Reduces Data Team Toil
How a self-documenting semantic layer cuts data team toil with AI-generated descriptions, suggested labels, and metadata that propagates through views.
semantic layerdata engineeringdocumentation - 5 MIN READ•Feb 18, 2026
Testing Data Pipelines: What to Validate and When
What to test in a data pipeline and when: a testing pyramid for data, schema and contract tests, runtime validation, and regression and anomaly detection.
data engineeringbest practicestesting - 5 MIN READ•Feb 18, 2026
Headless BI: How a Universal Semantic Layer Replaces Tool-Specific Models
Headless BI explained: why tool-specific models in Tableau or Power BI drift apart, and how one universal semantic layer serves every BI tool and notebook.
semantic layerdata engineeringheadless BI - 5 MIN READ•Feb 18, 2026
Partition and Organize Data for Performance
A table with 500 million rows takes 45 seconds to query. After partitioning it by date, the same query : filtering on a single day, returns in 2 seconds.
data engineeringbest practicespartitioning - 5 MIN READ•Feb 18, 2026
Data Modeling for Analytics: Optimize for Queries
Why analytics needs a different data model than your application: denormalization, wide tables, and pre-aggregation patterns that make analytical queries fast.
data modelingdata engineeringanalytics