To migrate data warehouse workloads successfully, start with discovery and dependency mapping. Then design the target, move in waves, validate parity and finally optimize continuously. Sounds simple on the surface, right? But the difficulty lies in everything...
What we’ve observed typically goes like this: higher education data challenges are not usually caused by a lack of data. In fact, most colleges and universities have plenty of data: student records, enrollment data, financial aid information, learning management...
Data modeling workflows need to be predictable. Whether teams are importing models through the command line, running workflow scripts, applying Model Conversion Rules or editing multiple entity columns at once, they need confidence that every step can be monitored,...
Enterprise data modeling is no longer just a design exercise. For years, data models helped architects define entities, relationships, keys, attributes and structures before implementation. That work still matters. Conceptual, logical and physical models remain...
For many enterprise data teams, SAP PowerDesigner has been part of the data architecture toolkit for years. It has supported conceptual data models, logical data models, physical data models, warehouse modeling, reverse engineering, impact analysis and database design...
Modern data estates have outgrown the whiteboard. The diagrams that once captured a single warehouse now have to describe dozens of sources, multiple cloud platforms and a web of regulatory obligations that change faster than most teams can document them. When a...
Building a data warehouse is hard, sure. But making sure it stays useful is even harder. Many data warehouse projects are judged on the launch … did the team connect the right sources, build the models, create the dashboards and deliver the first round of reporting?...
Data architectures rarely fail because they were wrong on day one. More often, they fail later, when the business changes faster than the architecture can keep up. New source systems arrive. Definitions change. Mergers happen. Reporting requirements expand. Platforms...
When we flew northbound to attend the Data Innovation Summit, DIS 2026, in Stockholm, we expected AI to dominate the conversation. And it did. But the most intriguing conversations were not about AI in isolation. Rather, they were about what needs to sit underneath...