Batch ELT pipelines—built on point-to-point extraction and load-then-transform patterns—fail at enterprise scale due to pipeline sprawl, unpredictable costs, poor governance, and brittle dependencies. The post argues that a streaming-first architecture using Apache Kafka as a central integration fabric, combined with open table formats like Apache Iceberg or Delta Lake and any compatible query engine, solves these structural problems. Key benefits include ingesting data once and reusing it across many consumers, shift-left data quality via Schema Registry, lower TCO through consumption-based pricing, and readiness for real-time AI workloads. Common objections—such as Kafka being overkill, microbatching being sufficient, or legacy systems not supporting streaming—are addressed, with CDC highlighted as the bridge for legacy sources.
Table of contents
What You Need for High-Scale Data EngineeringThe Rise and Lingering Flaw of Traditional ELT Data PipelinesWhy ELT Pipelines Break Down at ScaleA Streaming-First Data Integration ArchitectureBatch ELT Pipelines vs Streaming-First Data Integration ArchitecturesDriving Innovation and Growth: Beyond the TechA New Standard for Data EngineeringFAQs – Common Objections Against Streaming Data IntegrationSort: