Shift Left: Unifying Operations and Analytics With Data Products

The need for high-quality business data is greater than ever, so preventing and mitigating bad data—across the entire business—has become a critical capability.

Extract-transform-load (ETL) and extract-load-transform (ELT) data pipelines have long been the primary means for getting data into the analytics plane. But data consumers in the analytics domain have had little to no control or influence over the source data model, which is commonly defined by application developers in the operational domain.

Shifting your data processing and governance “left” allows you to eliminate duplicate pipelines, reduce the risk and impact of bad data at the source, and leverage high-quality data products for both operational and analytical use cases.

Download this ebook—which includes a foreword by Jay Kreps, CEO of Confluent—to:

• Learn about the challenges with existing pipeline approaches and multi-hop architectures.
• Explore how to leverage the shift-left strategy to solve the analytical and operational divide.
• Build headless data architectures and reusable data products using Apache Flink® SQL, change data capture (CDC) and Apache Iceberg®.