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

A curated list of getting-started guides, hands-on tutorials, and conceptual articles for building an open lakehouse with Apache Iceberg, Nessie, and Arrow.

03 Mins readAlex Merced
Open Lakehouse Engineering/Apache Iceberg Lakehouse Engineering - A Directory of Resources

The concept of the Open Lakehouse has emerged as a beacon of flexibility and innovation. An Open Lakehouse represents a specialized form data lakehouse (bringing data warehouse like functionality/performance to data on a data lake), uniquely characterized by its commitment to open standards and technologies. At the core of this paradigm are tools like Apache Iceberg, Nessie, and Apache Arrow, which collectively empower organizations to build highly efficient, scalable, and interoperable data ecosystems.

Cross-posted. This article’s canonical home is Alex Merced’s Lakehouse Blog.

Unlike conventional data lakehouses which may have high levels of coupling between the storage formats, governance, optimization and more of their data with one vendor with few alternatives, an Open Lakehouse prioritizes the avoidance of vendor lock-in, ensuring that organizations maintain full control over their data infrastructure. This approach not only fosters a more adaptable and resilient data environment but also encourages a collaborative, community-driven development ethos that is instrumental in driving the field forward.

A key platform enabling open lakehouses is Dremio, a cutting-edge lakehouse platform that epitomizes the Open Lakehouse philosophy. Dremio seamlessly integrates various data sources, leveraging the power of open-source technologies to unify data management and analytics. This integration allows for an unprecedented level of flexibility and efficiency, making Dremio an indispensable tool for organizations looking to harness the full potential of their data. Dremio enables the maximization of decentralization in data harnessing the right features for data virtualization (decentralized data), data lakehouse (decentralized access to a single copy of a dataset) and data mesh (decentralized data curation).

This directory serves as a comprehensive resource for anyone looking to dive into the world of Open Lakehouse Engineering. Whether you’re a seasoned data professional or just starting out, the following resources will guide you through the intricacies of building and managing an Open Lakehouse, ensuring you’re well-equipped to leverage these exciting technologies to their fullest extent. Feel free to modify or expand upon this introduction to better fit the tone and scope of

If you are new to the data space I recommend starting with this playlist that will cover lakehouse engineering, modeling, big data concepts and more

Getting Started with Open Lakehouses

Hands-on Articles

Conceptual Content

Share :

Related Posts

Your Data Agents Are CPU-Bound, and the Hardware Market Just Noticed

Your Data Agents Are CPU-Bound, and the Hardware Market Just Noticed

A data team gets budget for an agentic analytics pilot. The plan is simple. Give business users an agent that answers questions by querying the lakehouse, and buy enough GPU capacity, or enough model ...

Iceberg Is Getting a Vector Type, and Parquet Decides What It Looks Like

Iceberg Is Getting a Vector Type, and Parquet Decides What It Looks Like

Open any Iceberg table that feeds a retrieval system and look at the embedding column. It is almost always `list`. That type says "a variable number of floats, any of which can be null." It say...

What Vortex Has to Prove Before Iceberg Adds a Fourth File Format

What Vortex Has to Prove Before Iceberg Adds a Fourth File Format

Every Apache Iceberg table makes a promise that is easy to forget because it has held for so long. Any engine that understands Iceberg can read any Iceberg table. Spark writes it, Trino reads it, Flin...

Work with Alex