Data Ingestion Fast & Slow: How to Improve Data Availability & Data Quality w/ Right-Time Processing

Gartner estimates the average business will lose ten million dollars annually due to data quality problems, and every week we hear another cautionary tale of an AI model gone awry. Technology that unifies batch and streaming has massive but overlooked implications for our ability to trust our data.

In this session, we'll demonstrate how architectures that can move between batch and incremental processing without changing the storage and API allow us to solve common data trust problems, such as stale data, as well as production ML risks, such as concept drift. We call this capability “Right-Time Processing.” This session is for data architects and practitioners. You don't need to be an ML or ETL expert to attend, but an interest in data architecture is critical.

Talk by: Dillon Bostwick

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[michaelcheung]

Thanks for the video! But the volume is a bit too low:(