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Data Engineering Weekly #161

Data Engineering Weekly

There will be food, networking, and real-world talks around data engineering. Here is the agenda, 1) Data Application Lifecycle Management - Harish Kumar( Paypal) Hear from the team in PayPal on how they build the data product lifecycle management (DPLM) systems. 4) Building Data Products and why should you?

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The Recommendation System at Lyft

Lyft Engineering

This blog post focuses on the scope and the goals of the recommendation system, and explores some of the most recent changes the Rider team has made to better serve Lyft’s riders. Introduction: Scope of the Recommendation System The recommendation system covers user experiences throughout the ride journey.

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Data Engineering Weekly #137

Data Engineering Weekly

Data Engineering Weekly Is Brought to You by RudderStack RudderStack Profiles takes the SaaS guesswork, and SQL grunt work out of building complete customer profiles, so you can quickly ship actionable, enriched data to every downstream team. So, let's shape the future of Data Engineering together. See how it works today.

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Data Engineering Weekly #125

Data Engineering Weekly

Meta: Presto - A Decade of SQL Analytics at Meta Presto and Kafka are the two systems that greatly impacted data infrastructure in the last decade. As with any good system, Presto went through many optimizations. Twitter: Twitter's Recommendation Algorithm Twitter open-source its recommendation engine code.

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Data Engineering Weekly #127

Data Engineering Weekly

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make collecting data from every application, website, and SaaS platform easy, then activating it in your warehouse and business tools. Editor’s Note: 🚀🌐🎉 Calling all Data Engineering Weekly Readers!

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A summary of Gartner’s recent DataOps-driven data engineering best practices article

DataKitchen

On 24 January 2023, Gartner released the article “ 5 Ways to Enhance Your Data Engineering Practices.” We surveyed 600 data engineers , including 100 managers, to understand how they are faring and feeling about the work that they are doing. The top-line result was that 97% of data engineers are feeling burnout.

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Transforming MLOps at DoorDash with Machine Learning Workbench

DoorDash Engineering

It is amusing for a human being to write an article about artificial intelligence in a time when AI systems, powered by machine learning (ML), are generating their own blog posts. Therefore, it’s critical for the data and engineering teams to have comprehensive support throughout the ML process.