Productizing Structured Streaming Jobs Burak Yavuz Databricks

Structured Streaming was a new streaming API introduced to Spark over 2 years ago in Spark 2.0, and was announced GA as of Spark 2.2. Databricks customers have processed over a hundred trillion rows in production using Structured Streaming. We received dozens of questions on how to best develop, monitor, test, deploy and upgrade these jobs. In this talk, we aim to share best practices around what has worked and what hasn't across our customer base.
We will tackle questions around how to plan ahead, what kind of code changes are safe for structured streaming jobs, how to architect streaming pipelines which can give you the most flexibility without sacrificing performance by using tools like Databricks Delta, how to best monitor your streaming jobs and alert if your streams are falling behind or are actually failing, as well as how to best test your code.
About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Read more here: databricks.com/product/unifie...
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[@nosh3019]

very useful!

[@SpiritOfIndiaaa]

Thank you so much

[@HaraldHusum]

At 19:30 there's a suggestion for pyspark testing: "writing to files and then reading it out from a memory sink". Any good examples of this I can peak at?

[@srinivasreddy478]

link for code repo and presentation please?