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Seamless SQL And Python Transformations For Data Engineers And Analysts With SQLMesh

Data Engineering Podcast

Summary Data transformation is a key activity for all of the organizational roles that interact with data. Because of its importance and outsized impact on what is possible for downstream data consumers it is critical that everyone is able to collaborate seamlessly. Can you describe what SQLMesh is and the story behind it?

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Composable data management at Meta

Engineering at Meta

In recent years, Meta’s data management systems have evolved into a composable architecture that creates interoperability, promotes reusability, and improves engineering efficiency. Data is at the core of every product and service at Meta. Data is at the core of every product and service at Meta.

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Reflecting On The Past 6 Years Of Data Engineering

Data Engineering Podcast

In that time there have been a number of generational shifts in how data engineering is done. Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? __init__ covers the Python language, its community, and the innovative ways it is being used.

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Data Engineering Weekly #173

Data Engineering Weekly

link] Meta: Composable data management at Meta Meta writes about its transition to a composable data management system to improve interoperability, reusability, and engineering efficiency. seconds, enhancing real-time sports data analytics efficiency!

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How Data Engineering Teams Power Machine Learning With Feature Platforms

Data Engineering Podcast

In this episode Razi Raziuddin shares how data engineering teams can support the machine learning workflow through the development and support of systems that empower data scientists and ML engineers to build and maintain their own features. What is feature engineering is and why/to whom it matters?

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Modern Data Engineering

Towards Data Science

Platform Specific Tools and Advanced Techniques Photo by Christopher Burns on Unsplash The modern data ecosystem keeps evolving and new data tools emerge now and then. In this article, I want to talk about crucial things that affect data engineers. Are your data pipelines efficient? Data warehouse exmaple.

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Zenlytic Is Building You A Better Coworker With AI Agents

Data Engineering Podcast

Summary The purpose of business intelligence systems is to allow anyone in the business to access and decode data to help them make informed decisions. The team at Zenlytic have leaned on the promise of large language models to build an AI agent that lets you converse with your data. Data lakes are notoriously complex.

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