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Unlocking Data Team Success: Are You Process-Centric or Data-Centric?

DataKitchen

Unlocking Data Team Success: Are You Process-Centric or Data-Centric? We’ve identified two distinct types of data teams: process-centric and data-centric. We’ve identified two distinct types of data teams: process-centric and data-centric. They work in and on these pipelines.

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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 #203

Data Engineering Weekly

With Astro, you can build, run, and observe your data pipelines in one place, ensuring your mission critical data is delivered on time. This blog captures the current state of Agent adoption, emerging software engineering roles, and the use case category. Save Your Spot → Chirag Shah & Ryen W.

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Data Engineering Weekly #196

Data Engineering Weekly

Foundation Capital: A System of Agents brings Service-as-Software to life software is no longer simply a tool for organizing work; software becomes the worker itself, capable of understanding, executing, and improving upon traditionally human-delivered services. 60+ speakers from LinkedIn, Shopify, Amazon, Lyft, Grammarly, Mistral, et al.

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The Race For Data Quality in a Medallion Architecture

DataKitchen

This foundational layer is a repository for various data types, from transaction logs and sensor data to social media feeds and system logs. We have also seen a fourth layer, the Platinum layer , in companies’ proposals that extend the Data pipeline to OneLake and Microsoft Fabric.

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Data Engineering Weekly #214

Data Engineering Weekly

One thing that stands out to me is As AI-driven data workflows increase in scale and become more complex, modern data stack tools such as drag-and-drop ETL solutions are too brittle, expensive, and inefficient for dealing with the higher volume and scale of pipeline and orchestration approaches. We all bet on 2025 being the year of Agents.

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Use Consistent And Up To Date Customer Profiles To Power Your Business With Segment Unify

Data Engineering Podcast

The challenge is that most companies have a multitude of systems that contain fragments of the customer's interactions and stitching that together is complex and time consuming. Segment created the Unify product to reduce the burden of building a comprehensive view of customers and synchronizing it to all of the systems that need it.