Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A data warehouse is defined as a central repository that allows ...

Snowflake's approach unifies structured and unstructured data analysis within its platform by treating documents as queryable ...

Since the beginning of the data warehousing movement in the early 1990s, there have been two dominant approaches to architecting data warehouses—the Inmon and Kimball models. Recently, two other ...

Modern organizations must react quickly to competitive threats and new potential market opportunities. More then ever before, organizations need up-to-date, comprehensive, and easily accessible data.

Data warehouses were designed in the days when closed and proprietary was the right choice for data storage. But the cloud era requires a less cumbersome, open technology that can cope with real-time ...

Many data warehouse operators have attempted to implement Master Data Management to improve data quality, but most have focused on mastering data after transactions occur. This approach does little to ...

Organizations that really want to take advantage of a higher performance, more agile and lower cost data warehouse architecture, should implement master data management (MDM) to improve data quality.

In the ongoing debate about where companies ought to store data they want to analyze – in a data warehouses or in data lake — Databricks today unveiled a third way. With SQL Analytics, Databricks is ...

We can all agree on one thing: nobody likes to wait. When we go to the airport, we like our flights to be on time, and when we head to the grocery store, we want our ripe avocados to be in stock.