Developer Education Hub
Tutorials for developers who read the source.
Written by Alex Merced, Head of Developer Relations at Dremio and author of 35+ books.
Guides on web development, data engineering, Apache Iceberg, and agentic AI. Guest submissions are welcome. Pitch an idea at alex@grokoverflow.com.
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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.
Why the Iceberg DataFusion Integration Is Moving to Apache DataFusion
Why the Iceberg DataFusion integration moved to the DataFusion project, and what the split means for users, Comet, and iceberg-rust contributors.
Read Article →What Iceberg v4's Proposed FILE Type Means for Multimodal Tables
Iceberg v4's proposed FILE type brings first-class media references to tables, via Parquet's FILE logical type, ranges, checksums, and pre-signed URLs.
Read Article →Fast Classification Models, LLMs, and the Apache Iceberg Lakehouse
How fast classification models like Jev alongside open alternatives such as GLiClass compare with LLMs, and how to run both together inside an Apache Iceberg lakehouse.
Read Article →How Apache Ossie Is Deciding What Agents and BI Tools Can Ask a Semantic Layer
How Apache Ossie's layered query design gives AI agents both a constrained dimensional interface and a grain-safe SQL interface for semantic layers.
Read Article →CVE-2026-73334 and the Trust Boundary Inside an Encrypted Parquet File
CVE-2026-73334 lets a tampered Parquet footer route a reader's KMS token to an attacker. Here's the fix, Iceberg's safe path, and how to audit your lakehouse.
Read Article →Parquet Page Indexes and the Last Mile of Pruning in Apache Iceberg
Parquet page indexes can cut selective Iceberg scans by an order of magnitude on sorted data. How they work, what they cost, and how to lay out tables.
Read Article →