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Data News — Week 24.20

Christophe Blefari

Still, I want you to get as always a few takeaways of the conference so here are my favourite talks with a few highlights: Data culture as a product — Abhi already had one of my favourite talk from Data Council 2023 about Metrics tree. Works with multiple images as well to create stories.

Food 130
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Reducing The Barrier To Entry For Building Stream Processing Applications With Decodable

Data Engineering Podcast

In this episode Eric Sammer discusses why more companies are including real-time capabilities in their products and the ways that Decodable makes it faster and easier. It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. With Materialize, you can!

Process 182
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Defining A Strategy For Your Data Products

Data Engineering Podcast

This has led to the broad adoption of data products being the delivery mechanism for information. In this episode Ranjith Raghunath shares his thoughts on how to build a strategy for the development, delivery, and evolution of data products. Can you describe what is encompassed by the idea of a data product strategy?

BI 162
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Using Data To Illuminate The Intentionally Opaque Insurance Industry

Data Engineering Podcast

Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. How can you get the best results for your use case?

Insurance 162
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Building ETL Pipelines With Generative AI

Data Engineering Podcast

Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. With Materialize, you can!

Building 162
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AI debugging at Meta with HawkEye

Engineering at Meta

HawkEye is the powerful toolkit used internally at Meta for monitoring, observability, and debuggability of the end-to-end machine learning (ML) workflow that powers ML-based products. HawkEye supports recommendation and ranking models across several products at Meta. HawkEye’s components. HawkEye’s debugging workflows.

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Supporting Diverse ML Systems at Netflix

Netflix Tech

Since its inception , Metaflow has been designed to provide a human-friendly API for building data and ML (and today AI) applications and deploying them in our production infrastructure frictionlessly. While human-friendly APIs are delightful, it is really the integrations to our production systems that give Metaflow its superpowers.

Systems 90