Open source security data lake for threat hunting, detection & response, and cybersecurity analytics at petabyte scale on AWS

Apache XTable (incubating) is a cross-table converter for lakehouse table formats that facilitates interoperability across data processing systems and query engines.

Fastest open-source tool for replicating Databases to Data Lake in Open Table Formats like Apache Iceberg. ⚡ Efficient, quick and scalable data ingestion for real-time analytics. Supporting Postgres, MongoDB and MySQL

Apache Kafka® compatible broker with S3, PostgreSQL and Apache Iceberg

Use SQL to build ELT pipelines on a data lakehouse.

Lakehouse storage system benchmark

Sample Data Lakehouse deployed in Docker containers using Apache Iceberg, Minio, Trino and a Hive Metastore. Can be used for local testing.

Jupyter notebooks and AWS CloudFormation template to show how Hudi, Iceberg, and Delta Lake work

📡 Real-time data pipeline with Kafka, Flink, Iceberg, Trino, MinIO, and Superset. Ideal for learning data systems.

The Control Plane for Apache Iceberg

Stream CDC into an Amazon S3 data lake in Apache Iceberg table format with AWS Glue Streaming and DMS

Icebird: JavaScript Iceberg Client

An open-source, community-driven REST catalog for Apache Iceberg!

Sample code to collect Apache Iceberg metrics for table monitoring

This repo contains examples of high throughput ingestion using Apache Spark and Apache Iceberg. These examples cover IoT and CDC scenarios using best practices. The code can be deployed into any Spark compatible engine like Amazon EMR Serverless or AWS Glue. A fully local developer environment is also provided.

Streaming ETL job cases in AWS Glue to integrate Iceberg and creating an in-place updatable data lake on Amazon S3

A sample implementation of stream writes to an Iceberg table on GCS using Flink and reading it using Trino

DAIVI is a reference solution with IAC modules to accelerate development of Data, Analytics, AI and Visualization applications on AWS using the next generation Amazon SageMaker Unified Studio. The goal of the DAIVI solution is to provide engineers with sample infrastructure-as-code modules and application modules to build their data platforms.

Write-Audit-Publish on the lakehouse in pure Python with bauplan and DBOS

Hands-on workshop with Iceberg, Redpanda, Debezium and Kafka-Connect