Apache Iceberg Made Easy: Local Setup with StarRocks, Docker & MinIO

Are you interested in trying Apache Iceberg for real big data analytics but intimidated by the setup? In this practical walkthrough, I cover every step to get Apache Iceberg running locally, without all the cloud hassle, using the open-source StarRocks query engine and MinIO for object storage. In this video, you'll see: - Why I found traditional Apache Iceberg setup daunting and how StarRocks makes it dramatically easier - What StarRocks is, and how it fits as a modern open source query engine and data warehouse - How to use Docker Compose to stand up StarRocks, MinIO, and everything else you need in just minutes - Setting up Iceberg catalogs, databases, and tables with real commands and hands-on tips - Inserting and querying data (with a few tricks for handling pesky issues you might run into) - Visualizing how Iceberg actually stores data and partitions in MinIO (including folder structure by partition) - Pro-tips, pitfalls, and what to look out for as a beginner to Iceberg and StarRocks Whether you’ve struggled with Apache Iceberg setup before, or you’re just getting curious about modern open-source data warehousing, this demo will save you hours and give you a head start! 👉 Timestamps: 00:00 Intro: The challenge with Apache Iceberg 00:37 What is StarRocks? Quick tech overview 01:45 Setting up with Docker Compose 03:7 Initializing Iceberg and StarRocks containers 04:10 Creating catalogs, databases, and tables 08:53 Writing & querying sample data (troubleshooting insert issues) 10:03 Verifying storage in MinIO 12:27 Understanding Iceberg’s storage structure visually 14:00 Final thoughts and what's next 🔗 Useful Links: - StarRocks Iceberg Quick Start: https://docs.starrocks.io/docs/quick_start/iceberg/ - My Substack for long-form data engineering content: https://thefulldatastack.substack.com/ - Connect on LinkedIn: https://www.linkedin.com/in/hoytemerson/ - Learn about my technical content company, Early Signal: https://www.earlysignal.tech/ If you enjoy hands-on data engineering, subscribe for more deep dive tutorials and let me know in the comments what you’d like to see next!