The Shift Left Architecture — From Batch and Lakehouse to Data Streaming

Data integration is a hard challenge in every enterprise. Batch processing and Reverse ETL are common practices in a data warehouse, data lake or lakehouse. Data inconsistency, high compute cost, and stale information are the consequences. This blog post introduces a new design pattern to solve these problems: The Shift Left Architecture enables a data mesh with real-time data products to unify transactional and analytical workloads with Apache Kafka, Flink and Iceberg. Consistent information is handled with streaming processing or ingested into Snowflake, Databricks, Google BigQuery, or any other analytics / AI platform to increase flexibility, reduce cost and enable a data-driven company culture with faster time-to-market building innovative software applications.

(Originally posted on Kai Waehner’s blog: “The Shift Left Architecture — From Batch and Lakehouse to Real-Time Data Products with Data Streaming”… Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter)

Data Products — The Foundation of a Data Mesh

A data product is a crucial concept in the context of a data mesh that represents a shift from traditional centralized data management to a decentralized approach.