Developer Education Hub
Welcome to GrokOverflow
Tutorials, podcasts, and videos for developers — by Alex Merced, Head of Developer Relations at Dremio and author of 35+ books.
Explore the blog for guides on web development, data engineering, Apache Iceberg, agentic AI, and more. Guest submissions welcome — pitch your idea at alex@grokoverflow.com.
Must Reads — Data Lakehouses & Agentic Analytics
Authoritative guides from the Dremio blog on building intelligent, open lakehouse architectures.
The Semantic Layer: The Definitive Guide
Understand how a semantic layer unifies business logic, accelerates self-service analytics, and becomes the foundation of AI-ready data architectures.
Read on Dremio.com →Apache PolarisApache Polaris: The Catalog Standard for Lakehouses and AI
Learn how Apache Polaris is establishing a universal open catalog standard that lets any engine read and write Apache Iceberg tables without vendor lock-in.
Read on Dremio.com →Table FormatsWhat Are Table Formats and Why Were They Needed?
Trace the evolution from raw Parquet files to modern table formats like Apache Iceberg — the innovation that unlocked ACID transactions on object storage.
Read on Dremio.com →DremioWhat Is Dremio?
A comprehensive overview of Dremio's Intelligent Lakehouse Platform — how reflections, semantic layers, and multi-engine federation work together.
Read on Dremio.com →Apache IcebergWhat Apache Iceberg Native Actually Means
Cut through the marketing: discover what it truly means to be Apache Iceberg-native versus merely Iceberg-compatible, and why the distinction matters.
Read on Dremio.com →Open SourceOpen Source and the Data Lakehouse
Explore how open-source projects — Iceberg, Parquet, Arrow, and Polaris — form an interoperable stack that keeps your data free from proprietary control.
Read on Dremio.com →Agentic AIWhat Is Agentic Analytics?
Discover how AI agents autonomously query, reason over, and act on lakehouse data — fundamentally changing how organizations derive insight at scale.
Read on Dremio.com →LakehouseThe Definitive Guide to the Data Lakehouse
The canonical end-to-end guide: what a data lakehouse is, how it compares to data warehouses and data lakes, and how to architect one for your organization.
Read on Dremio.com →PerformanceHow Dremio Keeps Agentic Analytics Fast Without Manual Tuning
Learn how Dremio's autonomous optimization layer — reflections, compaction, and vectorized execution — keeps AI agent queries fast without manual DBA work.
Read on Dremio.com →Recent Articles & Tutorials
Stay up to date with my latest guides, walkthroughs, and deep dives on data lakehouses, web development, and AI.
AI-Ready Metadata Prevents Query Failures
AI-ready metadata reduces query failures by making ownership, freshness, lineage, quality, and policy visible at execution time.
Read Article →Autonomous Materialization for Agentic Analytics
Autonomous materialization is useful when it is tied to workload evidence, governance checks, and lifecycle management.
Read Article →Composable Semantic Layers for Analytical Agents
AI agents need more than metric names. They need composable business logic that survives multi-step analysis.
Read Article →Built for Agents and Managed by Agents
Dremio Agentic Lakehouse is easiest to understand as two ideas: data built for agent access and platform work managed by agents.
Read Article →ClickHouse in the Loop for Active Agents
Low-latency analytical systems can help active agents, but only when event loops include validation, context, and safety boundaries.
Read Article →The Context Layer for AI Agents
A semantic layer is necessary, but agents also need lineage, quality, freshness, compliance, and ownership context.
Read Article →