A data warehouse is defined as a central repository that allows enterprises to store and consolidate business data extracted from multiple source systems for the task of historical and trend ...

Thierry Cruanes covers the three pillars of the Snowflake architecture: separating compute and storage to leverage abundant cloud compute resources; building an ACID compliant database system on ...

Learn how one higher education institution is modernizing and strengthening its endowment and fundraising strategy by ...

Srinivasa Sridhar Kavikondala is a seasoned technical architect specializing in data warehousing and cloud solutions, based ...

Data lakehouse architecture combines the best of cloud data lake and warehousing architectures to give teams the most recent data.

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 ...

Nearly every data warehouse ecosystem has attempted to manage master data within its data warehouse architecture, but has focused on mastering data after transactions occur. This approach does little ...

The data lakehouse – it’s not a summer retreat for over-worked database administrators (DBAs) or data scientists, it’s a concept that tries to bridge the gap between the data warehouse and ...

By combining streaming, location, and machine learning analytics into a unified platform, the streaming data warehouse actually untangles a data architecture.