Remove Architecture Remove Data Governance Remove Data Lake
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

Simplifying Data Architecture and Security to Accelerate Value

Snowflake

What if you could streamline your efforts while still building an architecture that best fits your business and technology needs? Snowflake is committed to doing just that by continually adding features to help our customers simplify how they architect their data infrastructure. Here’s a closer look.

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

Build A Data Lake For Your Security Logs With Scanner

Data Engineering Podcast

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data.

Data Lake 147
article thumbnail

Using Trino And Iceberg As The Foundation Of Your Data Lakehouse

Data Engineering Podcast

Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Data lakes are notoriously complex. To start, can you share your definition of what constitutes a "Data Lakehouse"?

Data Lake 262
article thumbnail

Addressing The Challenges Of Component Integration In Data Platform Architectures

Data Engineering Podcast

In this episode Tobias Macey shares his thoughts on the challenges that he is facing as he prepares to build the next set of architectural layers for his data platform to enable a larger audience to start accessing the data being managed by his team. Data lakes are notoriously complex. With Materialize, you can!

article thumbnail

Troubleshooting Kafka In Production

Data Engineering Podcast

Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. Data lakes are notoriously complex. Materialize]([link] You shouldn't have to throw away the database to build with fast-changing data.

Kafka 245
article thumbnail

Toward a Data Mesh (part 2) : Architecture & Technologies

François Nguyen

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. How do we build data products ? How can we interoperate between the data domains ? We are Data Teams versus we have to patch the server with the latest version and do the tests.