Efficient data management keeps goods flowing smoothly in Denmark. Danske Fragtmaend, the country’s largest national transport and distribution firm, has been moving freight for more than a century. Today, Danske Fragtmaend delivers more than 40,000 consignments each day throughout Denmark, and businesses from small mom-and-pop operations to factories rely on its services.
The firm handles logistics in a central location, where 200 dispatchers keep an eye on the movement of thousands of trucks and their cargo. Both drivers and dispatchers need the latest information to operate efficiently, so they rely on a data platform based on SQL Server 2016. The storage system includes 160 terabytes of flash memory for fast I/O and high uptimes. Throughout the day, drivers continually scan transactions with PDAs and send shipping information including GPS coordinates to the data platform. Fast access to information is essential. Ulf…
This post is the first of a series providing an overview of “bi-temporal data management” and contrasting it with the current standard approach in data warehousing for tracking historical changes in data (“temporal data management”) developed by Ralph Kimball. Bi-temporal data management is a concept that has been under development since the 1980’s, but is familiar to few data professionals even today. Reasons for this include:
in many applications it just isn’t needed, or its complexity is not justified;
the Kimball methodology is predominant for temporal data management in the data warehouse context – and it does not handle or even consider bi-temporality other than via several half-baked patterns;
without built-in support in the major relational database engines, the necessary SQL to implement bi-temporality is devilishly complex; for all practical purposes it is not feasible from perspectives of understandability, maintenance and performance, especially…