Shift Left: Data Warehouse Ingestion

Data teams often rely on complex batch-based ETL/ELT pipelines to centralize and process data in data warehouses for analytical use cases. These legacy pipelines result in stale, siloed, redundant, and error-prone data requiring expensive processing in downstream systems. Despite the criticality of fresh, reliable, and contextual data in business decisions and user experiences, organizations are held back from getting full value out of their data due to technical and operational overheads.

This white paper covers how to implement a solution for connecting, processing, and governing data streams for data warehouses. You'll learn about:

• Sourcing from data warehouses (including use of Change Data Capture)
• Sinking to data warehouses
• Shifting processing and governance left to data in flight with stream processing
• Incorporating Security and Data Privacy, Data Governance, Performance and Scalability, and Monitoring & Reliability

Download the white paper today to get started with a shift approach to data streaming that brings real-time, reusable, and reliable data products to your data warehouse (Snowflake, Amazon Redshift, Azure Synapse, Google BigQuery, Databricks Delta Lake, and more)