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Streaming Data Pipelines with SQL

Eric Sammer
Decodable

Streaming data systems have been growing more capable and flexible over the past few years. Despite this, it is still challenging to build reliable pipelines for stream processing. In this Data Engineering Podcast hosted by Tobias Macey, Eric Sammer, discusses the shortcomings of the current set of streaming engines and how they force engineers to work at an extremely low level of abstraction. He explains how Decodable addresses that limitation and lets data engineers build streaming pipelines entirely in SQL.

A Practical Introduction to the Data Mesh

There’s been quite a bit of talk about data meshes recently, both in terms of philosophy and technology. Unfortunately, most of the writing on the subject is thick with buzzwords, targeted toward VP and C-level executives, unparsable to engineers. The motivation behind the data mesh, however, is not only sound but practical and intuitive.

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Demo Day - Developer Experience Tour

Josh Mahonin takes us on a whistle-stop tour of the Decodable developer experience including schema version management and update, debugging, pipeline dependency management and data product navigation via schemas in a data mesh setup.

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Demo Day: Fraud Detection using SQL for ML Feature Extraction

In this SQL-packed demo, see how Moonsense uses Decodable SQL tranformations in multiple pipelines to convert streaming device data into features to populate a fraud detection machine learning model.

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