Metadata helps with data management and serves as a descriptor for an object that holds some data or information. In data warehouses, it is collectively organized in a catalog called…
Gauging Agility: Testing for Agile Data Warehouse Environment
In this era of data-driven decision making, data quality holds immense significance. Accurate data has been gauged to be a priority factor in customer experience enhancement and creating a business…
Decoding the Abstraction Layer in Data Warehousing
Before the Xbox and iPhone, prior to the first Facebook Like or Tweet, and well before the cloud and tablets, there was the data warehouse. For over 30 years, businesses…
How Data Masking Provides Security within a Data Warehouse
The immense amount of sensitive information stored in data warehouses makes them attractive targets for data hackers. This means that securing a data warehouse efficiently is of high importance. At…
Normalization and Denormalization – Differences through Use Cases
Introduction Today, the most common argument among data warehouse managers is determining which schema is more performance-oriented. However, it’s critical to know that neither of the normalization or denormalization approaches…
Industry Trends: What’s Next in the World of Data Warehousing
Forrester’s Business Technographics Global Data and Analytics Survey shows that the data storage capacity of 59% of companies exceeded 100TB in 2017. This percentage is twice to that of 2016….
The 3 Stages of Data Cleansing to Ensure Data Accuracy in Your Data Warehouse
Are you embarking on a cleansing journey for your data warehouse initiative? In this article, we will cover the 3 phases of data cleansing to help you define a process…
Do You Need a Logical Architecture for Big Data Analytics in Your Organization?
“The world is getting more distributed and it is never going back the other way.” Ted Friedmann, VP Research, Gartner If you’ve been actively researching big data analytics, you are…