Data warehousing concepts

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…

The 3 Stages of Data Cleansing to Ensure Data Accuracy in Your Data Warehouse

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…

Which Data Integration Approach is Right for You?

An enterprise uses an average of 928 applications, along with many other on-premise systems. This means that you can have about a thousand source systems from different vendors, each storing…

Data warehouse

Which Data Extraction Approach is Best for Your Data Warehouse?

After the initial phases of gathering requirements and conceptualizing data warehouse design, the execution phase begins. Extract-Transform-Load from source data systems to destination staging database or data warehouse. The process…

Data warehouse vs marts vs lakes

Data Marts, Lakes, and Warehouses – Understanding the Differences

Too many buzzwords, too little understanding. With the ubiquity of data and analytics, IT vocabulary is expanding fast, and terms like data marts, data lakes and data warehouses are being…