Over the past few decades, enterprise data warehouses have evolved to accommodate and process zettabytes of data. Businesses are revamping and evolving their legacy architecture with the help of emerging…
Data Warehouse Cleansing: Ensure Consistent, Trusted Enterprise Data
Data Virtualization for Agile Data Warehousing
Enterprises, owing to their operations in diverse global markets, can no longer depend on traditional data warehouse architectures to fulfill their Business Intelligence (BI) requirements. This is because of the…
Data Warehouse Optimization Mistakes to Avoid
Data warehouse optimization, although hard to achieve, is the goal of every progressive organization. This is primarily because a Business Intelligence (BI) system working with a struggling data warehouse is…
Transactional vs. Analytical Databases: How Does OLTP Differ from OLAP
Databases are created to store data, but the way they are designed depends on your business objectives. Most business applications store data in an OLTP (On-Line Transaction Processing) database, which…
Ensuring Stellar Data Warehouse Governance: Best Practices
In a short few years, data warehouse has become an indispensable and integral part of all business activities. To ensure that it remains effective and keeps serving its primary objectives,…
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Implementing Referential Integrity in a Data Warehouse: A (Controversial) Decision with a Lasting Impact
No other feature in relational database management is, arguably, as integral as referential integrity (RI) constraints . The feature plays the all-important, two-fold role of ensuring the accuracy of data…
Data Warehouse Testing: Overview and Common Challenges
Without big data, companies are blind and deaf, wandering out onto the web like deer on a freeway – Geoffrey Data has become the most widely used resource for decision…