Better Together: Change Data Feed in a Streaming Data Flow
Description
Traditional streaming works great when your data source is append-only, but what if your data source includes updates and deletes? At 84.51 we used DLT and Delta Lake to build a streaming data flow that consumes inserts, updates and deletes while still taking advantage of streaming checkpoints. We combined this flow with a materialized view and Enzyme incremental refresh for a low-code, efficient and robust end-to-end data flow. We process around 8 million sales transactions each day with 80 million items purchased. This flow not only handles new transactions but also handles updates to previous transactions. Join us to learn how 84.51 combined change data feed, data streaming and materialized views to deliver a “better together” solution. 84.51 is a retail insights, media & marketing company. We use first-party retail data from 60 million households sourced through a loyalty card program to drive Kroger’s customer-centric journey. Talk By: Mattias Moser, Data Engineer & Architect, 84.51 LLC ; Scott Gordon, Lead Data Engineer, 84.51˚ Here’s more to explore: Production ready data pipelines for analytics and AI: https://www.databricks.com/solutions/data-engineering The Big Bo…
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