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Lakehouse at Home: A Practical Guide to DuckDB, Delta Lake, Iceberg, Airflow & Self-Hosted Data Pipelines
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Publisher: Independently Published
Published: Nov 23 2025
Pages: 406
Weight: 2.06
Height: 0.83 Width: 8.50 Depth: 11.00
Language: English
Build a complete, production-grade Lakehouse fully in your homelab - using modern open-source tools, real data pipelines, and fully self-hosted analytics and AI workflows.
Cloud platforms are no longer the only place where serious data engineering happens. Thanks to fast mini-PCs, affordable NVMe storage, Raspberry Pi clusters, and powerful open-source engines like DuckDB, Delta Lake, Iceberg, Polars, MinIO, Redpanda, dbt, and Airflow, anyone can now run a modern Lakehouse architecture entirely at home. This book shows you exactly how.
Lakehouse at Home is a complete, hands-on guide to designing, deploying, and operating a full Lakehouse stack in a homelab or self-hosted environment. Through step-by-step labs and one end-to-end capstone project, you'll build storage layers, streaming pipelines, batch ingestion workflows, declarative ELT models, metadata documentation, BI dashboards, and even a local AI-powered RAG assistant - all running on your own hardware.
Inside this book, you will learn how to:- Deploy MinIO as an S3-compatible foundation for Bronze, Silver, and Gold layers
- Build streaming ingestion using Redpanda and Kafka-compatible pipelines
- Automate batch ingestion with DuckDB, Polars, and Python SDKs
- Create high-performance Delta Lake and Iceberg tables, with partitioning, compaction, schema evolution, and time-travel
- Design robust ELT workflows using dbt 1.8+ with the DuckDB adapter
- Orchestrate multi-step pipelines using Airflow 3+
- Manage data quality, documentation, and metadata contracts
- Build full BI dashboards with Metabase or Grafana
- Add a self-hosted AI layer using embeddings, Qdrant, and a local LLM (Ollama/LM Studio)
- Monitor your entire system with Prometheus, retention rules, and operational dashboards
- Validate your complete architecture using testing, diagnostics, and Airflow-based checks
Every chapter includes Practice Labs, giving you real-world experience in deploying and operating each component. The book concludes with a full Capstone Project, where you build an entire production-grade Lakehouse-storage, streaming, batch, transformations, dashboards, AI retrieval, and monitoring-all integrated and running locally.
Built for the modern data engineer, homelab builder, and self-hosting enthusiastWhether you're an engineer mastering the Lakehouse paradigm, a homelab builder seeking local data autonomy, or a self-hosted automation enthusiast, this book gives you the tools, patterns, and complete workflows needed to build a powerful, private, cloud-free analytics and AI platform.
Why this book stands outMost Lakehouse books assume cloud services, vendor-locked architectures, or enterprise clusters.
This book does the opposite - it teaches you how to build everything yourself, on your hardware, using open tools, with no recurring cloud costs, and with complete control over your data and workloads.
This is the definitive, modern, hands-on guide to building a Lakehouse at home - fast, reliable, private, and production-ready.
If you're ready to build the next generation of self-hosted data pipelines, analytics systems, and AI workflows right inside your homelab, this book will show you how - step by step.
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