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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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5 Layers of Data Lakehouse Architecture Explained

Monte Carlo

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. A visualization of the flow of data in data lakehouse architecture vs. data warehouse and data lake.

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Data Lakehouse Architecture Explained: 5 Layers

Monte Carlo

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. A visualization of the flow of data in data lakehouse architecture vs. data warehouse and data lake.

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Build A Data Lake For Your Security Logs With Scanner

Data Engineering Podcast

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data.

Data Lake 147
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Release Management For Data Platform Services And Logic

Data Engineering Podcast

Summary Building a data platform is a substrantial engineering endeavor. Once it is running, the next challenge is figuring out how to address release management for all of the different component parts. Data lakes are notoriously complex. My thanks to the team at Code Comments for their support.

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Zenlytic Is Building You A Better Coworker With AI Agents

Data Engineering Podcast

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is supported by Code Comments, an original podcast from Red Hat. Data lakes are notoriously complex. Data lakes are notoriously complex. Closing Announcements Thank you for listening!

Building 278
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[O’Reilly Book] Chapter 1: Why Data Quality Deserves Attention Now

Monte Carlo

Your downstream data consumers including product analysts, marketing leaders, and sales teams rely on data-driven tools like CRMs, CXPs, CMSs, and any other acronym under the sun to do their jobs quickly and effectively. But what happens when the data is wrong?