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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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Build and Manage ML features for Production-Grade Pipelines

Snowflake

When scaling data science and ML workloads, organizations frequently encounter challenges in building large, robust production ML pipelines. A key benefit of Snowflake Feature Store is its use of Dynamic Tables to automate and abstract the complexity of data and feature engineering pipeline and backfill management.

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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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Managing Database Access Control For Teams With strongDM

Data Engineering Podcast

Summary Controlling access to a database is a solved problem… right? It can be straightforward for small teams and a small number of storage engines, but once either or both of those start to scale then things quickly become complex and difficult to manage.

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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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Release Management For Data Platform Services And Logic

Data Engineering Podcast

Summary Building a data platform is a substrantial engineering endeavor. Once it is running, the next challenge is figuring out how to address release management for all of the different component parts. The services and systems need to be kept up to date, but so does the code that controls their behavior.

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Securely Scaling Big Data Access Controls At Pinterest

Pinterest Engineering

Soam Acharya | Data Engineering Oversight; Keith Regier | Data Privacy Engineering Manager Background Businesses collect many different types of data. 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.