Ingesting Apache Kafka using Starburst Galaxy’s Icehouse architecture | Starburst Galaxy
Want to know more about the Starburst Galaxy data Icehouse based on Apache Iceberg and Trino? Read all about it in the blog post: https://www.starburst.io/blog/icehous...
Welcome to our Icehouse data ingestion demo in Starburst Galaxy! 🚀 In this tutorial, we'll walk you through the process of connecting to a Kafka source and ingesting data into an Iceberg table.
Using Starburst Galaxy's streaming ingest capabilities, we demonstrate how easy it is to set up data ingestion from a Kafka source into an Apache Iceberg table. We call the management of Apache Iceberg tables Icehouse, and a data architecture based on this a data Icehouse. We'll show you how to connect to your Kafka source step-by-step to your data Icehouse, defining your target Iceberg table in AWS S3, and configuring a schema inference for efficient data ingestion.
This demo brings together some of the most exciting new technologies in big data today, showcasing the flexibility of Starburst Galaxy's ingest streams. This feature allows you to customize data types, add or remove columns, and flatten nested data to fit your specific use case. This brings the power of Apache Iceberg into your everyday workload using a data Icehouse.
Here's a snapshot of what our Icehouse implementation in Starburst Galaxy offers:
Iceberg Tables Everywhere: Enjoy the benefits of Iceberg format throughout your data ecosystem, ensuring efficiency and flexibility.
Seamless Data Ingestion: Simplify data ingestion from diverse sources like Kafka, guaranteeing reliability and efficiency.
Data Integrity: Our Icehouse implementation ensures exactly once processing, preserving data integrity and reliability.
Data Quality Management: Easily manage data quality with separate Iceberg tables for invalid data, streamlining troubleshooting.
Automated Data Preparation: Streamline data processing with automated transformations, making data consumption-ready.
Schema Flexibility: Adapt seamlessly to schema changes, minimizing disruptions and data downtime.
Automated Maintenance & Optimization: Automate maintenance tasks and optimize performance effortlessly, freeing up resources for innovation.
Efficient Querying: Unlock cost-effective SQL querying on Iceberg tables, complemented by robust governance features.
If you'd like to know more about Starburst Galaxy's data Icehouse managed tables, we have an entire blog post on the topic: https://www.starburst.io/blog/introdu...
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