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

Guide on How to Be Part of the Lakehouse Community

How to Discover or Organize Lakehouse & Apache Iceberg Meetups

Introduction to the terms in data engineering

Introduction to Data Engineering Concepts | What is Data Engineering?

Introduction to Data Engineering Concepts | Understanding Data Sources and Ingestion

Introduction to Data Engineering Concepts | ETL vs ELT – Understanding Data Pipelines

Introduction to Data Engineering Concepts | Batch Processing Fundamentals

Introduction to Data Engineering Concepts | Streaming Data Fundamentals

Introduction to Data Engineering Concepts | Data Modeling Basics

Introduction to Data Engineering Concepts | Data Warehousing Fundamentals

Introduction to Data Engineering Concepts | Data Lakes Explained

Introduction to Data Engineering Concepts | Storage Formats and Compression

Introduction to Data Engineering Concepts | Data Quality and Validation

Introduction to Data Engineering Concepts | Metadata, Lineage, and Governance

Introduction to Data Engineering Concepts | Building Scalable Pipelines

Introduction to Data Engineering Concepts | DevOps for Data Engineering

Introduction to Data Engineering Concepts | Cloud Data Platforms and the Modern Stack

Introduction to Data Engineering Concepts | Data Lakehouse Architecture Explained

Introduction to Data Engineering Concepts | Apache Iceberg, Arrow, and Polaris

Introduction to Data Engineering Concepts | The Power of Dremio in the Modern Lakehouse

Sampling and Prompts in MCP — Making Agent Workflows Smarter and Safer

A Journey from AI to LLMs and MCP - 10 - Sampling and Prompts in MCP — Making Agent Workflows Smarter and Safer

Tools in MCP — Giving LLMs the Power to Act

A Journey from AI to LLMs and MCP - 9 - Tools in MCP — Giving LLMs the Power to Act

Resources in MCP — Serving Relevant Data Securely to LLMs

A Journey from AI to LLMs and MCP - 8 - Resources in MCP — Serving Relevant Data Securely to LLMs

Under the Hood — The Architecture of MCP and Its Core Components

A Journey from AI to LLMs and MCP - 7 - Under the Hood — The Architecture of MCP and Its Core Components

Enter the Model Context Protocol (MCP) — The Interoperability Layer for AI Agents

Journey from AI to LLMs and MCP - 6 - Enter the Model Context Protocol (MCP) — The Interoperability Layer for AI Agents

AI Agent Frameworks — Benefits and Limitations

A Journey from AI to LLMs and MCP - 5 - AI Agent Frameworks — Benefits and Limitations

What Are AI Agents — And Why They're the Future of LLM Applications

A Journey from AI to LLMs and MCP - 4 - What Are AI Agents — And Why They're the Future of LLM Applications

Boosting LLM Performance — Fine-Tuning, Prompt Engineering, and RAG

A Journey from AI to LLMs and MCP - 3 - Boosting LLM Performance — Fine-Tuning, Prompt Engineering, and RAG

How LLMs Work — Embeddings, Vectors, and Context Windows

A Journey from AI to LLMs and MCP - 2 - How LLMs Work — Embeddings, Vectors, and Context Windows

A Journey from AI to LLMs and MCP - 1 - What Is AI and How It Evolved Into LLMs

The Basics of Building a Basic MCP Server

Introduction to Data Engineering Concepts | Scheduling and Workflow Orchestration

A Guide on when to use Helm Charts for Kubernetes Deployment

Using Helm with Kubernetes - A Guide to Helm Charts and Their Implementation

A guide on building AI applications with LangChain, a framework for developing AI applications powered by Large Language Models (LLMs).

Crash Course on Developing AI Applications with LangChain

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 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

Getting Started with Cargo and cargo.toml

Introduction to Cargo and cargo.toml

Using Features like Pattern Matching and Comprehensions for Data Analytics

Leveraging Python's Pattern Matching and Comprehensions for Data Analytics

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

Data Modeling - Entities and Events

All About  Parquet Part 01 - An Introduction

All About  Parquet Part 02 - Parquet's Columnar Storage Model

All About  Parquet Part 03 - Parquet File Structure | Pages, Row Groups, and Columns

All About  Parquet Part 04 - Schema Evolution in Parquet

All About  Parquet Part 05 - Compression Techniques in Parquet

All About  Parquet Part 06 - Encoding in Parquet | Optimizing for Storage

All About  Parquet Part 07 - Metadata in Parquet | Improving Data Efficiency

All About  Parquet Part 08 - Reading and Writing Parquet Files in Python

All About  Parquet Part 09 - Parquet in Data Lake Architectures

All About  Parquet Part 10 - Performance Tuning and Best Practices with Parquet

Advanced GitHub Actions for Data Engineering

Orchestrating Airflow DAGs with GitHub Actions - A Lightweight Approach to Data Curation Across Spark, Dremio, and Snowflake

A Deep Dive Into GitHub Actions From Software Development to Data Engineering

A Guide to dbt Macros - Purpose, Benefits, and Usage

What's Going on in the Data Lakehouse Space

Data Lakehouse Roundup 1 - News and Insights on the Lakehouse

Learning to work with PyArrow to run analytics

Getting Started with Data Analytics Using PyArrow in Python

Process Data from Raw to Clean Aggregated Data

What is Three-Tier Data (Bronze, Silver, Gold) and How Dremio Simplifies It

Controlling Access to your Apache Iceberg Tables

A Brief Guide to the Governance of Apache Iceberg Tables

Learning to work with Python to ingest and query data

Exploring Data Operations with PySpark, Pandas, DuckDB, Polars, and DataFusion in a Python Notebook

Apache Iceberg Education, Tutorials and more!

Ultimate Directory of Apache Iceberg Resources

Handling Synching Changing Data Across Systems

Change Data Capture (CDC) when there is no CDC

Combining Centralization and Decentralization for Data at Scale

Virtualization + Lakehouse + Mesh = Data At Scale

Deep Dive into Data Apps with Streamlit

A Deep Dive into Docker Compose

Hands-on with Apache Iceberg on Your Laptop - Deep Dive with Apache Spark, Nessie, Minio, Dremio, Polars and Seaborn

Why Data Analysts, Engineers, Architects and Scientists Should Care about Dremio and Apache Iceberg

5 Trends in the Data Lakehouse Space

Setting up a quick and easy data environment for data science and analytics

Using the alexmerced/datanotebook Docker Image

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.