Working for a while with Jupyter, both for ML processes and Data Engineering ETL’s, we came up with interactive solution to make our ETL’s stable, reliable and maintainable — read more about how we build our interactive platform in the following story:

After having all infrastructure ready to work - we started writing our ETL pipelines in Jupyter and schedule it in Google Cloud Composer. In our ETL’s we came across multiple problems — we wanted to resolved them with Jupyter Magics infrastructure. Our goal was to create more modular, maintainable and flexible source of code. …


If you were in the data engineering industry for a while you probably get know all the existing notebooks solutions out there. in this article I’m gonna focus on what and how we use it in our Data & M.L platform.

First let talk about us…

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All our…

Ben Mizrahi

Bugs always exists between the keyboard to the screen :)

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