Remove Business Intelligence Remove Data Architecture 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

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. Each of these architectures has its own unique strengths and tradeoffs. The schema of semi-structured data tends to evolve over time.

Data Lake 113
Insiders

Sign Up for our Newsletter

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

article thumbnail

Open, Interoperable Storage with Iceberg Tables, Now Generally Available

Snowflake

Snowflake is now making it even easier for customers to bring the platform’s usability, performance, governance and many workloads to more data with Iceberg tables (now generally available), unlocking full storage interoperability. Iceberg tables provide compute engine interoperability over a single copy of data.

Data Lake 124
article thumbnail

Microsoft Fabric vs. Snowflake: Key Differences You Need to Know

Edureka

It incorporates elements from several Microsoft products working together, like Power BI, Azure Synapse Analytics, Data Factory, and OneLake, into a single SaaS experience. Its multi-cluster shared data architecture is one of its primary features.

BI 52
article thumbnail

Breaking State and Local Data Silos with Modern Data Architectures

Cloudera

Modern data architectures. To eliminate or integrate these silos, the public sector needs to adopt robust data management solutions that support modern data architectures (MDAs). Towards Data Science ). Deploying modern data architectures. Forrester ).

article thumbnail

Cloudera and Snowflake Partner to Deliver the Most Comprehensive Open Data Lakehouse

Cloudera

In August, we wrote about how in a future where distributed data architectures are inevitable, unifying and managing operational and business metadata is critical to successfully maximizing the value of data, analytics, and AI.

article thumbnail

Data Engineering: A Formula 1-inspired Guide for Beginners

Towards Data Science

Anyways, I wasn’t paying enough attention during university classes, and today I’ll walk you through data layers using —  guess what  —  an example. Business Scenario & Data Architecture Imagine this: next year, a new team on the grid, Red Thunder Racing, will call us (yes, me and you) to set up their new data infrastructure.