nessie VS dremio-oss

Nessie is an innovative open-source catalog that extends beyond the traditional catalog capabilities in the Apache Iceberg ecosystem, introducing git-like features to data management. This catalog not only tracks table metadata but also allows users to capture commits at a holistic level, enabling advanced operations such as multi-table transactions, rollbacks, branching, and tagging. These features provide a new layer of flexibility and control over data changes, resembling version control systems in software development.

Try Nessie https://github.com/projectnessie/nessie - it recently got trino support as well ..

Project Nessie (https://projectnessie.org/) will be the catalog that eventually decouples Iceberg from Hive. At that point, I think it will be a no brainer to go Iceberg over Delta.

The Dremio Sonar query engine can query your data where it exists whether it's AWS Glue, S3, Nessie Catalogs, MySQL, Postgres, RedShift and an ever growing list of sources.

We will also need a catalog to track all of these tables, with the open source Project Nessie we can do just that, and also get great versioning features similar to using Git when developing applications allowing data engineers to practice "data as code" and "write-audit-publish" patterns on their data.

Dremio - Dremio is a data lakehouse based on the open-source Apache Iceberg table format. It offers different compute instances to process data that lives in your S3 bucket. You pay for S3 storage independently.

Dremio core is actually fully open source: https://github.com/dremio/dremio-oss

I have been using Dremio to query large volume of CSV files: https://docs.dremio.com/software/data-sources/files-and-dire...

Although having them in some columnar format is much better for fast responses.

GitHub: https://github.com/dremio/dremio-oss

As a Developer Advocate for Dremio I spend a lot of time doing research on technology and best practices around engineering Data Lakehouses and sharing what I learn through content for Subsurface - The Data Lakehouse Community. One of the major topics I've been diving deep into is the topic of Data Lakehouse Table Formats, these allow you to take the files on your data lake and group them into tables data processing engines like Dremio can operate on.

Hearing about all these components sounds great, but what everyone wants isn't to have to setup and configure all these components but instead have a platform and tool that brings this all together in an easy to use package, and that platform is Dremio. With Dremio you can work with the data directly from your data lake. No copies, easy access, high performance.

And as u/pych_phd said, it's not just Databricks, Snowflake and Azure who make these claims, even AWS, GCP, Dremio and I'm sure many others are too.

Dremio

For my home projects I generate parquet (columnar and very well suited for DW like queries) files with pyarrow and use https://github.com/dremio/dremio-oss (https://www.dremio.com/on-prem/) to query them on lake (minio or just local disk or s3) and use Apache Superset for quick charts or dashboards.