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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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Using Trino And Iceberg As The Foundation Of Your Data Lakehouse

Data Engineering Podcast

In this episode Dain Sundstrom, CTO of Starburst, explains how the combination of the Trino query engine and the Iceberg table format offer the ease of use and execution speed of data warehouses with the infinite storage and scalability of data lakes. What do you have planned for the future of Trino/Starburst?

Data Lake 262
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Strategies And Tactics For A Successful Master Data Management Implementation

Data Engineering Podcast

Summary The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. Master Data Management (MDM) is the process of building consensus around what the information actually means in the context of the business and then shaping the data to match those semantics.

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Aligning Velox and Apache Arrow: Towards composable data management

Engineering at Meta

This new convergence helps Meta and the larger community build data management systems that are unified, more efficient, and composable. Meta’s Data Infrastructure teams have been rethinking how data management systems are designed.

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Reducing The Barrier To Entry For Building Stream Processing Applications With Decodable

Data Engineering Podcast

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team.

Process 182
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Java for Data Science – When & How To Use

Knowledge Hut

In recent years, quite a few organizations have preferred Java to meet their data science needs. From ERPs to web applications, Navigation Systems to Mobile Applications, Java has been facilitating advancement for more than a quarter of a century now. Is Learning Java Mandatory? So let us get to it.

Java 52
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Data News — Week 24.11

Christophe Blefari

Understand how BigQuery inserts, deletes and updates — Once again Vu took time to deep dive into BigQuery internal, this time to explain how data management is done. Pandera, a data validation library for dataframes, now supports Polars. Arrow doing a lot of the data operation heavy lifting.

Metadata 272