Learn How to Ingest and Process Streaming Data Effortlessly | Demo

Thanks to Snowpipe Streaming and Materialized Tables, two new capabilities currently in private preview and development, Snowflake users will soon be able to create streaming data pipelines that provide an effortless way to work with streaming data and data ingestion. In this video, Tyler Akidau, Principal Software Engineer at Snowflake, demonstrates how it all works. To do so, he takes on the role of a data engineer at a fictional retailer, “SportsCo,” which wants to better understand its ad spend and performance across different channels and campaigns so it can allocate budgets efficiently.

First, Tyler will show how to use Kafka Connector, built with Snowpipe Streaming to ingest streaming data, in a serverless fashion–no need to manage clusters. Data just streams in. Then he’ll show how Materialized Tables, which are declarative data pipelines, can be used to join the streaming ad data with a campaign reference table, and then aggregate the total number of clicks and cost. Typically, this would require a lot of effort configuring separate streaming systems and connecting them to the reference data that lives in Snowflake using complex, imperative programming. Materialized Tables eliminate that complexity and make it simple to join and transform data in near real-time. For more information, visit https://www.snowflake.com/workloads/d.... For more of our live demos here: https://www.snowflake.com/live-demo/.

If you missed the Snowflake Summit opening keynote featuring Snowflake CEO, Frank Slootman; Co-Founder & President of Product, Benoit Dageville; and SVP of Product, Christian Kleinerman, as they unveil the next wave of technological firsts, make sure to catch the on-demand replay at snowflake.com/summit/livestream.

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