To build a data warehouse, data must first be extracted and transformed from an organization’s various sources. Then, the data must be loaded into the database in a structured format. Finally, an ETL ...

Today, the excitement of the big data era is not just about having lots of data. What’s truly interesting is that organizations with all data sizes now each approach data problems in different and ...

I’d hazard a guess that usually, the dissatisfaction arises from trying to force the data warehouse (or analytical database if you prefer) to do something for which it’s not well suited.

By definition, business intelligence (BI) and data warehousing refer to storing all the company’s data in internal or external databases from several sources and analyzing it in order to generate ...

What’s the difference between databases and data lakes? The term “ data lake ” is sometimes used interchangeably with “ data warehouse ” — but this is not correct.

The difference between a data warehouse and a data lake is that a data lake stores data in its natural format, often blobs or files, while a data warehouse stores data as a database. Snowflake in ...

Data warehouse technology is shifting to the cloud with much ballyhoo around new entrants. But do they have the muscle to compete with the tried, tested and surviving incumbents?

Azure SQL Data Warehouse gets less press than its online transaction processing brethren, Azure SQL Database and Azure Cosmos DB. However, it is a powerful cloud engine for processing large ...

Data warehousing is moving from its traditional home in the data center to the increased capacity and flexibility of cloud platforms. Make sure your organization has the training and ...

The Disadvantages of a Data Warehouse. Data warehouses are relational databases that act as data analysis tools, aggregating data from multiple departments of a business into one data store. Data ...