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Unpacking The Seven Principles Of Modern Data Pipelines

Data Engineering Podcast

Summary Data pipelines are the core of every data product, ML model, and business intelligence dashboard. The folks at Rivery distilled the seven principles of modern data pipelines that will help you stay out of trouble and be productive with your data. Closing Announcements Thank you for listening!

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Building Data Pipelines That Run From Source To Analysis And Activation With Hevo Data

Data Engineering Podcast

Building reliable data pipelines is a complex and costly undertaking with many layered requirements. In order to reduce the amount of time and effort required to build pipelines that power critical insights Manish Jethani co-founded Hevo Data. Data stacks are becoming more and more complex.

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Streaming Data Pipelines Made SQL With Decodable

Data Engineering Podcast

In this episode 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. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform!

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How Data Engineering Teams Power Machine Learning With Feature Platforms

Data Engineering Podcast

Summary Feature engineering is a crucial aspect of the machine learning workflow. In this episode Razi Raziuddin shares how data engineering teams can support the machine learning workflow through the development and support of systems that empower data scientists and ML engineers to build and maintain their own features.

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Making The Total Cost Of Ownership For External Data Manageable With Crux

Data Engineering Podcast

In this episode Crux CTO Mark Etherington discusses the different costs involved in managing external data, how to think about the total return on investment for your data, and how the Crux platform is architected to reduce the toil involved in managing third party data. Tired of deploying bad data?

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Moving Machine Learning Into The Data Pipeline at Cherre

Data Engineering Podcast

Summary Most of the time when you think about a data pipeline or ETL job what comes to mind is a purely mechanistic progression of functions that move data from point A to point B. Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Via the UI or via an API.

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A Complete Guide to Scale Your Data Pipelines and Data Products with Contract Testing and Dbt

Towards Data Science

Not too long ago, almost all data architectures and data team structures followed a centralized approach. As a data or analytics engineer, you knew where to find all the transformation logic and models because they were all in the same codebase. There was only one data team, two at most. How did they do it?