Learn about the Apache Software Foundation's history, governance model, and why it matters for open source data lakehouse technologies like Iceberg, Parquet, and Arrow.

Apache Software Foundation: History, Purpose, and Process

A deep dive into Apache Parquet's columnar storage format, encoding strategies, and why it's the backbone of modern data lakehouse architectures.

What is Apache Parquet? Columns, Encoding, and Performance

Understand how Apache Iceberg brings ACID transactions, schema evolution, and time travel to object storage, enabling the modern open data lakehouse.

What is Apache Iceberg? The Table Format Revolution

Apache Polaris is the open-source Iceberg REST catalog that enables multi-engine interoperability across your entire lakehouse ecosystem.

What is Apache Polaris? Unifying the Iceberg Ecosystem

Apache Arrow provides a standardized in-memory columnar format that eliminates serialization overhead between query engines, eliminating the cost of data movement.

What is Apache Arrow? Erasing the Serialization Tax

Learn how to combine Apache Iceberg, Apache Parquet, Apache Arrow, and Apache Polaris into a complete, vendor-neutral open data lakehouse.

Assembling the Apache Lakehouse: The Modular Architecture

Explore how the Apache Lakehouse stack enables agentic AI systems to autonomously query, reason over, and act on large-scale structured data.

Agentic Analytics on the Apache Lakehouse

An updated introduction to Apache Iceberg

A 2026 Introduction to Apache Iceberg

A look back at key developments in Apache Iceberg, Polaris, Parquet, and Arrow in 2025.

2025 Year in Review Apache Iceberg, Polaris, Parquet, and Arrow

Walkthrough with the new trial of the Dremio Cloud Platform

Comprehensive Hands-on Walk Through of Dremio Cloud Next Gen (Hands-on with Free Trial)

A curated guide to mastering Apache Iceberg, data lakehouse architectures, and the emerging field of Agentic AI for data professionals.

2025-2026 Guide to Learning about Apache Iceberg, Data Lakehouse & Agentic AI

Dive into the world of commercial Iceberg catalogs and discover how they enhance data lakehouse architectures for modern data engineering.

An Exploration of the Commercial Iceberg Catalog Ecosystem

Exploring paths to a universal lakehouse catalog that supports multiple data formats and engines, building on Apache Iceberg's success.

Building a Universal Lakehouse Catalog - Beyond Iceberg Tables

Learn how to leverage Apache Iceberg with Apache Polaris and Apache Spark to build scalable and efficient data lakehouses.

Intro to Apache Iceberg with Apache Polaris and Apache Spark

What's Coming in Apache Iceberg v4: A Deep Dive into the Future of Open Table Formats

The State of Apache Iceberg v4 - October 2025 Edition

Understanding Iceberg, Delta Lake, Hudi, Paimon, and DuckLake

The Ultimate Guide to Open Table Formats - Iceberg, Delta Lake, Hudi, Paimon, and DuckLake

What is the Data Lakehouse and the Data Lakehouse Ecosystem? This comprehensive guide covers everything you need to know about the Data Lakehouse architecture, open table formats like Apache Iceberg, Delta Lake, Apache Hudi, and Apache Paimon, and the modern data ecosystem that supports them.

The 2025 & 2026 Ultimate Guide to the Data Lakehouse and the Data Lakehouse Ecosystem

Learn how to automate compaction, snapshot expiration, and layout optimization in Apache Iceberg using metadata-driven triggers and orchestration tools for a self-healing lakehouse.

The Endgame — Building an Autonomous Optimization Pipeline for Apache Iceberg

Learn how to scale Apache Iceberg table optimizations across large datasets using parallelism, checkpointing, and fail recovery to ensure reliability and performance.

Managing Large-Scale Optimizations — Parallelism, Checkpointing, and Fail Recovery

Unlocking the Power of Agentic AI with Apache Iceberg and Dremio

Partition evolution in Apache Iceberg is a powerful feature, but if not managed carefully, it can introduce fragmentation and impact compaction performance. Learn how to handle it effectively.

Hidden Pitfalls — Compaction and Partition Evolution in Apache Iceberg

Discover how to use Apache Iceberg's metadata tables to proactively detect small files, bloated manifests, and table fragmentation—so you can trigger compaction only when it's needed.

Using Iceberg Metadata Tables to Determine When Compaction Is Needed

Learn how to design an effective schedule for compaction and snapshot expiration in Apache Iceberg to balance cost, performance, and data freshness.

Designing the Ideal Cadence for Compaction and Snapshot Expiration

Learn how to prevent and clean up metadata bloat in Apache Iceberg by expiring snapshots and rewriting manifests for better performance and manageability.

Avoiding Metadata Bloat with Snapshot Expiration and Rewriting Manifests

Improve query performance in Apache Iceberg by organizing your data layout with sorting and Z-order clustering. Learn how to reduce scan cost and improve filter effectiveness.

Smarter Data Layout — Sorting and Clustering Iceberg Tables

Learn how to design fast, incremental compaction strategies in Apache Iceberg to support high-throughput streaming pipelines without disrupting freshness or performance.

Optimizing Compaction for Streaming Workloads in Apache Iceberg

Learn how standard compaction works in Apache Iceberg and why bin packing your data files is essential for maintaining query performance and cost efficiency.

The Basics of Compaction — Bin Packing Your Data for Efficiency

Learn how Apache Iceberg tables can degrade over time without optimization and what issues this causes for performance, cost, and governance.

The Cost of Neglect — How Apache Iceberg Tables Degrade Without Optimization

Understanding the value of a lakehouse and how to get that value faster

The Data Lakehouse - The Benefits and Enhancing Implementation

What is Apache Iceberg, How it Works, and Why it Matters!

2025 Comprehensive Guide to Apache Iceberg

A Guide on when to use Apache Xtable or Delta Lake Uniform for Data Lakehouse Interoperability

When to use Apache Xtable or Delta Lake Uniform for Data Lakehouse Interoperability

A personal story of how I built Pangolin Catalog over a holiday break using an AI-powered IDE.

Building Pangolin - My Holiday Break, an AI IDE, and a Lakehouse Catalog for the Curious

A Comprehensive Guide to Building a Data Lakehouse with Apache Iceberg

2025 Guide to Architecting an Iceberg Lakehouse

What is cool about Apache Iceberg's Future

10 Future Apache Iceberg Developments to Look forward to in 2025

Auto ingesting data from JSON, CSV, and Parquet files into Apache Iceberg Tables

Deep Dive into Dremio's File-based Auto Ingestion into Apache Iceberg Tables

Intro to SQL using Apache Iceberg and Dremio

The Role of Dremio and Apache Iceberg in AI-Ready Data

Dremio, Apache Iceberg and their role in AI-Ready Data

Hands-on with Apache Iceberg & Dremio on Your Laptop within 10 Minutes

Controlling Access to your Apache Iceberg Tables

A Brief Guide to the Governance of Apache Iceberg Tables

Apache Iceberg Education, Tutorials and more!

Ultimate Directory of Apache Iceberg Resources

Continuing the Understand Apache Iceberg series, this article delves into the Manifest, a critical component of Apache Iceberg's architecture.

Understanding Apache Iceberg Delete Files

Understanding the Apache Iceberg Manifest

Continuing the Understand Apache Iceberg series, this article delves into the Manifest List, a critical component of Apache Iceberg's architecture.

Understanding the Apache Iceberg Manifest List (Snapshot)

The role and content of the metadata.json

Understanding Apache Iceberg's Metadata.json

Understanding Iceberg Catalog Interoperability

What Apache Iceberg REST Catalog is and isn't

Learn about the new open source Iceberg Catalog in Town

Understanding the Polaris Iceberg Catalog and Its Architecture

Apache Iceberg Reliability

Introductory Course to Data Engineering for Apache Iceberg Lakehouses

Video Course - Basics of Lakehouse Engineering - Apache Iceberg, Nessie, Dremio

Benefits of Apache Iceberg Partition Evolution and Hidden Partitioning

Partitioning with Apache Iceberg - A Deep Dive

3 Reasons Data Engineers Should Embrace Apache Iceberg

How to run SQL on your Excel files easily

Running SQL on your Excel Files From Your Laptop with Dremio

Java, Rest and the expanding open lakehouse ecosystem

Understanding the Future of Apache Iceberg Catalogs

A Deep Intro to Apache Iceberg and Resources for Learning More

Ingesting Data and Building BI Dashboards

End-to-End Basic Data Engineering Tutorial (Spark, Dremio, Superset)

Apache Iceberg, Apache Arrow, Nessie, Ibis, Substrait

5 Open Source Data Projects You Should Be Following

Understanding how catalogs work and which one to choose

5 Reasons Dremio is the Ideal Apache Iceberg Lakehouse Platform

Disrupting the Snowflake/Databricks status quo

The Apache Iceberg Lakehouse - The Great Data Equalizer

10 Reasons to Make Apache Iceberg and Dremio Part of Your Data Lakehouse Strategy

A deep dive into the concept and world of Apache Iceberg Catalogs

Deep Dive in Data Lake Table Partitioning

Partitioning Practices in Apache Hive and Apache Iceberg

Understanding how to choose a table format

Table Format FUD - Thinking Through the Table Format Conversion (Apache Iceberg, Apache Hudi, Delta Lake)

Embracing the Future of Data Management - Why Choose Lakehouse, Iceberg, and Dremio?

Resources for learning how to Engineer an Open Data Lakehouse

Open Lakehouse Engineering/Apache Iceberg Lakehouse Engineering - A Directory of Resources

Nessie is the only open-source catalog implementation specifically for Apache Iceberg.

Nessie -  An Alternative to Hive & JDBC for Self-Managed Apache Iceberg Catalogs

This is where the combined power of Dremio’s Lakehouse Management features and Project Nessie's catalog-level versioning comes into play.

Apache Iceberg, Git-Like Catalog Versioning and Data Lakehouse Management - Pillars of a Robust Data Lakehouse Platform

Why is Dremio so useful for Apache Iceberg data lakehouses

Why Dremio is a must for Apache Iceberg Data Lakehouses

How to configure Spark for using Apache Iceberg

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