article thumbnail

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

Precisely

The goal of this post is to understand how data integrity best practices have been embraced time and time again, no matter the technology underpinning. In the beginning, there was a data warehouse The data warehouse (DW) was an approach to data architecture and structured data management that really hit its stride in the early 1990s.

article thumbnail

Use Your Data Warehouse To Power Your Product Analytics With NetSpring

Data Engineering Podcast

In this episode Priyendra Deshwal explains how NetSpring is designed to empower your product and data teams to build and explore insights around your products in a streamlined and maintainable workflow. Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Data Warehouses Vs Operational Data Stores Vs Data Lakes – How To Store Your Data For Analytics

Seattle Data Guy

A few months ago, I uploaded a video where I discussed data warehouses, data lakes, and transactional databases. However, the world of data management is evolving rapidly, especially with the resurgence of AI and machine learning.

Data Lake 130
article thumbnail

Building Your Data Warehouse On Top Of PostgreSQL

Data Engineering Podcast

Summary There is a lot of attention on the database market and cloud data warehouses. While they provide a measure of convenience, they also require you to sacrifice a certain amount of control over your data. Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started.

article thumbnail

Announcing New Innovations for Data Warehouse, Data Lake, and Data Lakehouse in the Data Cloud 

Snowflake

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. Customers that require a hybrid of these to support many different tools and languages have built a data lakehouse.

Data Lake 115
article thumbnail

Creating Shared Context For Your Data Warehouse With A Controlled Vocabulary

Data Engineering Podcast

In this episode Emily Riederer shares her work to create a controlled vocabulary for managing the semantic elements of the data managed by her team and encoding it in the schema definitions in her data warehouse. star/snowflake schema, data vault, etc.) What do you have planned for the future of dbtplyr?

article thumbnail

Paving The Road For Fast Analytics On Distributed Clouds With The Yellowbrick Data Warehouse

Data Engineering Podcast

Summary The data warehouse has become the focal point of the modern data platform. With increased usage of data across businesses, and a diversity of locations and environments where data needs to be managed, the warehouse engine needs to be fast and easy to manage.