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Data logs: The latest evolution in Meta’s access tools

Engineering at Meta

Here we explore initial system designs we considered, an overview of the current architecture, and some important principles Meta takes into account in making data accessible and easy to understand. Users have a variety of tools they can use to manage and access their information on Meta platforms. feature on Facebook.

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Introducing the dbt MCP Server – Bringing Structured Data to AI Workflows and Agents

dbt Developer Hub

We expect that over the coming years, structured data is going to become heavily integrated into AI workflows and that dbt will play a key role in building and provisioning this data. We are committed to building the data control plane that enables AI to reliably access structured data from across your entire data lineage.

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Part 1: A Survey of Analytics Engineering Work at Netflix

Netflix Tech

Analytics Engineers deliver these insights by establishing deep business and product partnerships; translating business challenges into solutions that unblock critical decisions; and designing, building, and maintaining end-to-end analytical systems. DJ acts as a central store where metric definitions can live and evolve.

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Data Integrity for AI: What’s Old is New Again

Precisely

These are all big questions about the accessibility, quality, and governance of data being used by AI solutions today. And then a wide variety of business intelligence (BI) tools popped up to provide last mile visibility with much easier end user access to insights housed in these DWs and data marts.

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The Definitive Guide to Embedded Analytics

The Definitive Guide to Embedded Analytics is designed to answer any and all questions you have about the topic. We hope this guide will transform how you build value for your products with embedded analytics. Access the Definitive Guide for a one-stop-shop for planning your application’s future in data.

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Why are Cloud Development Environments Spiking in Popularity, Now?

The Pragmatic Engineer

This means more repositories are needed, which are fast enough to build and work with, but which increase fragmentation. Executing a build is much slower while on a call. Plus, a CPU and memory-intensive build can impact the quality of the video call, and make the local environment much less responsive.  Larger codebases.

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Using Trino And Iceberg As The Foundation Of Your Data Lakehouse

Data Engineering Podcast

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. To start, can you share your definition of what constitutes a "Data Lakehouse"? Your first 30 days are free!

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