The Rise of Streaming ETL

Streaming ETL pipelines enable three categories of real-time use cases: human insights, automated insights and automated actions.


  • The confluence of digital transformation and data modernization create the opportunity for real-time data integration, which in turn drives real-time business insights and action.
  • Streaming ETL has emerged as the most efficient, effective method of real-time data integration. As the name suggests, this method extracts streams of live data updates, transforms them in-flight, and loads them in real time to analytics targets.
  • Streaming ETL replaces the inefficient method of batch ETL. Streaming ETL also can reduce the latency of basic data transformations compared with streaming ELT.
  • Enterprises adopt streaming ETL pipelines to enable three categories of real-time use cases: human insights, automated insights, and automated actions. Streaming ETL pipelines support these use cases by integrating with BI products, ML models, business-monitoring tools, and intelligent process automation (IPA) workflows.
  • Data teams that standardize their data pipelines and select simple ML approaches are best poised to scale with the needs of the business. The most effective streaming ETL initiatives will take a cross-functional approach to learning and collaboration.

Report written by Kevin Petrie of Eckerson Group and sponsored by Equalum

Top Design & Implementation Challenges with Change Data Capture (CDC)

When designed and implemented effectively, Change Data Capture (CDC) is the most efficient method to meet today’s scalability, efficiency, and real-time requirements.

It is imperative that your business has the right architecture in place to handle high throughput of data, a simplicity of replicating subsets of data and ever changing schema, as well as the capacity to capture the data and the changes exactly once, then replicate or ETL the CDC data to your data warehouse or data lakes, for analytics purposes.

In this comprehensive guide, we will walk you through Challenges & Best Practices when deploying Modern Change Data Capture.

  • Chapter 1: Where Should My Organization Start When Implementing A Streaming Architecture?
  • Chapter 2: Beware of Source Overhead when Extracting and Transferring Data Using CDC
  • Chapter 3: Optimize Initial Data Capture as You Begin CDC Powered Replication
  • Chapter 4: CDC (extraction) Performance Bottlenecks from your Sources Cause Negative Ripple Effects
  • Chapter 5: Handling High Volume Transactions
  • Chapter 6: “Exactly Once – End To End” is Vital with Change Data Capture Powered Ingestion
  • Chapter 7: The Challenges of Managing CDC Replication Objects at Scale
  • Chapter 8: Data Drift Can Break Streaming Ingestion Pipelines
  • Chapter 9: A Future Proof Way to Deploy a Streaming Ingestion Solution
  • Summary: Optimizing Your Streaming Architecture for Speed, Scalability, Simplicity and Modern CDC

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