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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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Data Engineering Weekly #196

Data Engineering Weekly

impactdatasummit.com Thumbtack: What we learned building an ML infrastructure team at Thumbtack Thumbtack shares valuable insights from building its ML infrastructure team. The blog emphasizes the importance of starting with a clear client focus to avoid over-engineering and ensure user-centric development.

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Building a Scalable Search Architecture

Confluent

Software projects of all sizes and complexities have a common challenge: building a scalable solution for search. Building a resilient and scalable solution is not always easy. It involves many moving parts, from data preparation to building indexing and query pipelines. You might be wondering, is this a good solution?

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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. link] Jack Vanlightly: Table format interoperability, future or fantasy?

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Building for Inclusivity: The Technical Blueprint of Pinterest’s Multidimensional Diversification

Pinterest Engineering

Our commitment is evidenced by our history of building products that champion inclusivity. We know from experience that building for marginalized communities helps make the product work better for everyone. To ensure an unbiased approach, we also leveraged our skin tone and hair pattern signals when building this dataset.

Building 109
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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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The Race For Data Quality in a Medallion Architecture

DataKitchen

We have also seen a fourth layer, the Platinum layer , in companies’ proposals that extend the Data pipeline to OneLake and Microsoft Fabric. The need to copy data across layers, manage different schemas, and address data latency issues can complicate data pipelines. However, this architecture is not without its challenges.