Move past slow batch processing. Learn how to combine event streaming and modern table formats (Iceberg) to build a fast, flexible ‘Data Lakehouse’ architecture.

For organizations buried in data, traditional data warehousing (ELT/ETL) can’t keep up with real-time demands. This article introduces the conceptual evolution from Data Warehouses to Data Lakehouses. We focus heavily on the technology stack that makes this shift possible. We will explore how Apache Kafka captures real-time event streams, and how Apache Iceberg (or Delta Lake) brings reliability, ACID transactions, and schema evolution to huge datasets stored in inexpensive object storage (like AWS S3).

The post will include architectural diagrams illustrating how to use Kafka Connect to stream data into an Iceberg table format, followed by how analytical engines like Trino or Apache Spark can query this data with high performance. This is a strategic guide for data engineers and enterprise architects looking to build the next generation of real-time data platforms.