In the previous two parts, we’ve presented the live database model for a subscription-based business and a data warehouse (DWH) we could use for reporting. While it’s obvious that they should work together, there was no connection between these two models. Today, we’ll take that next step and write the code to transfer data from the live database into our DWH.
Can you design an OLAP database model from an OLTP model? In this article, we’ll show you how!
Welcome to a new series that shows you the practical side of the data warehouse (DWH)! In the first article, we’ll tackle a data model for a subscription business.
When we start a data warehousing project, the first thing we do is define the dimensional tables. Dimensional tables are the interesting bits, the framework around which we build our measurements. They come in many shapes and sizes. In this article, we are going to take a closer look at each type of dimensional table.
The process of defining your data warehousing system (DWH) has started. You’ve outlined the relevant dimension tables, which tie to the business requirements. These tables define what we weigh, observe and scale. Now we need to define how we measure.
Financial institutions, especially banks, usually have really large datasets. To use that data, it must be stored in such a way that it is easily available for generating reports. The trend now is to use a data warehouse to store all your relevant data, and to use smaller data marts (subsets of the warehouse) to keep specific data sets in a convenient place.
In the previous two articles, we considered the two most common data warehouse models: the star schema and the snowflake schema. Today, we’ll examine the differences between these two schemas and we’ll explain when it’s better to use one or the other.
In a previous article we discussed the star schema model. The snowflake schema is next to the star schema in terms of its importance in data warehouse modeling. It was developed out of the star schema, and it offers some advantages over its predecessor. But these advantages come at a cost. In this article, we’ll discuss when and how to use the snowflake schema.
Today, reports and analytics are almost as important as core business. Reports can be built out of your live data; often this approach will do the trick for small- and medium-sized companies without lots of data. But when things get bigger – or the amount of data starts increasing dramatically – it’s time to think about separating your operational and reporting systems.
There’s a lot to keep in mind when you’re designing a database, and very few of us can remember every valuable tip and trick we’ve learned. So, let’s take a look at some online resources that feature database design tips and best practices. As we go, I’ll share my own opinions on the ideas presented, based on my experience in database design.