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Data Engineering Weekly #161

Data Engineering Weekly

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. 3) DataOPS at AstraZeneca The AstraZeneca team talks about data ops best practices internally established and what worked and what didn’t work!!!

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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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[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?

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Data Fabric: The Future of Data Architecture

Monte Carlo

These include: Seamless integration A data fabric’s ability to integrate seamlessly with various APIs and software-delivery kits (SDKs) truly differentiates this framework from another type of data management: the ubiquitous data lake. That’s a model worth looking at when it comes to data governance,” says Bob.

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Data Fabric: The Future of Data Architecture

Monte Carlo

These include: Seamless integration A data fabric’s ability to integrate seamlessly with various APIs and software-delivery kits (SDKs) truly differentiates this framework from another type of data management: the ubiquitous data lake. That’s a model worth looking at when it comes to data governance,” says Bob.

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Data Observability Tools: Types, Capabilities, and Notable Solutions

Databand.ai

Data catalog and lineage tools: These tools provide visibility into data lineage by tracking the origin, transformation, and consumption of data across the data pipeline. They help organizations understand the dependencies between data sources, processes, and systems, enabling better data governance and impact analysis.

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Using Trino And Iceberg As The Foundation Of Your Data Lakehouse

Data Engineering Podcast

Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Data lakes are notoriously complex. Visit [dataengineeringpodcast.com/data-council]([link] and use code *depod20* to register today!

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