Accessibility Accessible Building 
Data Engineering Podcast
MAY 18, 2024
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. Are data agents harder to build?
Start Data Engineering
MAY 28, 2024
Building efficient data pipelines with DuckDB 4.1. Use DuckDB to process data, not for multiple users to access data 4.2. Introduction 2. Project demo 3. Cost calculation: DuckDB + Ephemeral VMs = dirt cheap data processing 4.3. Processing data less than 100GB? Use DuckDB 4.4.
databricks
DECEMBER 12, 2024
As enterprises build agent systems to deliver high quality AI apps, we continue to deliver optimizations to deliver best overall cost-efficiency for our.
Engineering at Meta
MARCH 12, 2024
By the end of 2024, we’re aiming to continue to grow our infrastructure build-out that will include 350,000 NVIDIA H100 GPUs as part of a portfolio that will feature compute power equivalent to nearly 600,000 H100s. RSC has accelerated our open and responsible AI research by helping us build our first generation of advanced AI models.
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Organizational data literacy is regularly addressed, but it’s uncommon for product managers to consider users’ data literacy levels when building products. Product managers need to research and recognize their end users' data literacy when building an application with analytic features.
Engineering at Meta
APRIL 6, 2023
Buck2, our new open source, large-scale build system , is now available on GitHub. Buck2 is an extensible and performant build system written in Rust and designed to make your build experience faster and more efficient. In our internal tests at Meta, we observed that Buck2 completed builds 2x as fast as Buck1.
Christophe Blefari
OCTOBER 19, 2023
On the other side the finance data team wants to build a revenue model on top of the core.orders model. The contract is super important because as soon as you expose a model, you have to potential downstream consumers that are building stuff on your models, you can't delete a column or change a type without notifying. See the doc.
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Organizations look to embedded analytics to provide greater self-service for users, introduce AI capabilities, offer better insight into data, and provide customizable dashboards that present data in a visually pleasing, easy-to-access format.
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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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Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.
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Just by embedding analytics, application owners can charge 24% more for their product. How much value could you add? This framework explains how application enhancements can extend your product offerings. Brought to you by Logi Analytics.
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