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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 #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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Data Engineering Weekly #196

Data Engineering Weekly

The blog emphasizes the importance of starting with a clear client focus to avoid over-engineering and ensure user-centric development. link] Gunnar Morling: Revisiting the Outbox Pattern The blog is an excellent summary of the path we crossed with the outbox pattern and the challenges ahead.

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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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Delivering Modern Enterprise Data Engineering with Cloudera Data Engineering on Azure

Cloudera

CDP Data Engineering offers an all-inclusive toolset that enables data pipeline orchestration, automation, advanced monitoring, visual profiling, and a comprehensive management toolset for streamlining ETL processes and making complex data actionable across your analytic teams. . A key aspect of ETL or ELT pipelines is automation.

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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.

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

Data Engineering Podcast

However, that's also something we're re-thinking with our warehouse-centric strategy. Contact Info Kevin LinkedIn Blog Hanhan LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Let us know if you have opinions there!