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Zenlytic Is Building You A Better Coworker With AI Agents

Data Engineering Podcast

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?

Building 278
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Building cost effective data pipelines with Python & DuckDB

Start Data Engineering

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.

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Demystifying Azure Storage Account network access

Towards Data Science

Demystifying Azure Storage Account Network Access Service endpoints and private endpoints hands-on: including Azure Backbone, storage account firewall, DNS, VNET and NSGs Connected Network — image by Nastya Dulhiier on Unsplash 1. This setup empowers consumers to perform data science tasks and build machine learning (ML) models.

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Securely Scaling Big Data Access Controls At Pinterest

Pinterest Engineering

Each dataset needs to be securely stored with minimal access granted to ensure they are used appropriately and can easily be located and disposed of when necessary. Consequently, access control mechanisms also need to scale constantly to handle the ever-increasing diversification.

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How to Build Data Experiences for End Users

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.

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Building Meta’s GenAI Infrastructure

Engineering at Meta

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.

Building 145
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Data Owner Responsibilities: Balancing Security, Access, and Sanity

Monte Carlo

It’s because data owners are responsible for ensuring the quality, security, and accessibility of a dataset across the entire organization. Then, data owners guide employees by defining and enforcing data access rules. When data is accessible, secure, and high quality, everyone benefits.

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Embedded Analytics Insights for 2024

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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The Definitive Guide to Embedded Analytics

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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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

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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How to Package and Price Embedded Analytics

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.