PyIceberg

Lakevision is a tool which provides insights into your Apache Iceberg based Data Lakehouse.

Sample code to collect Apache Iceberg metrics for table monitoring

A poc open framework to manage data ingestion into apache iceberg tables

Official companion repository for the "Unleash the power of Apache Iceberg on AWS" Book. Includes code samples, deployment guides, ETL examples, and best practices for implementing scalable Apache Iceberg lakehouses on AWS.

Tansu schema-backed topics, instantly accessible as Apache Iceberg tables with pyiceberg

provenance and immutability checks for iceberg lakehouses

Apache Iceberg with PyIceberg (no Spark/JVM): catalog setup, time travel, CDC, schema evolution, upserts

Apache Iceberg feature store and data lakehouse on Backblaze B2 — versioned feature tables, schema evolution, snapshot rollback, and DuckDB time-travel queries via PyIceberg, with B2 as the warehouse (no data warehouse to run)

Example using PyIceberg to work with Iceberg in S3 via. Glue Data Catalog

Provides a pyiceberg.io.FileIO implementation that uses hdfs-native client.

Locally reproducible Apache Iceberg lakehouse with a read-only FastAPI/DuckDB serving layer and near-real-time micro-batch ingestion. Built stage by stage (Stages 1–2B complete; agentic layer next).

Data Lakehouse implementations showcasing two approaches: one built with Open Source technologies and another with Databricks