If you are still getting into data mining and data warehousing, you might have to think twice about using the same databases that you are currently using. This is because relational databases that are used for online transaction processing work differently and may not be optimal for data warehousing. An OLTP database is ideal for real-time yet common transactions; it is best when you only retrieve or add one row per table at a time. What’s more, an OLTP database needs to be able to handle thousands of users simultaneously accessing it, while also being able to validate the data coming in for any transaction.
Meanwhile, a data warehouse database is perfect for the analysis of different business measures as it can handle unpredictable queries that need to access a number of rows in a table. However, it can support only a few concurrent users, much less than an OLTP database. It does not need real-time data validation.
So what do you need to know when designing a data warehouse in order to have no snags when it comes to database management?
1. A data warehouse can complement your OLTP. Do not get us wrong, having an OLTP or data warehouse database is not mutually exclusive. In fact, if you have an OLTP database, it would make sense to have a data warehouse to store historical data from your OLTP. This helps make sure that you OLTP database runs fast every single time.
2. OLAP. Online analytical processing is designed to give you the best performance for your business intelligence queries. It should work well with dimensional models utilized in data warehouses.
[expand title=”Click here to read more about this article”]
You data warehouse uses a multidimensional view of your data to help it make sense of the different types of queries that is asked by decision makers and analysts. OLAP allows you to organize the data in multidimensional cubes while also making sure that the queries and done fast. With OLAP, you can get the answers in seconds, even when the database has to scour through millions and millions of rows of data in your data warehouse.
3. Data mining. Data mining helps you discover insights hidden in your data by using complex and sophisticated algorithms. OLAP allows you to organize data to make it easy to explore, while data mining gives you data-driven analysis.
Data warehouses should be able to give your users a great experience, and they can function separately from your OLTP systems. A data warehouse has to give you a central repository of data and be able to answer queries fast. Further, you need to have access to analytical tools such as data mining and OLAP.
To achieve all these, your data warehouse needs to be based on dimensional models, and should have historical data. It should also have both summarized and detailed data, while also allowing you to consolidate different types of data coming from different sources even as it maintains consistency. Further, it focuses on a single subject, whether it is finance, sales, inventory and other subjects.
Do your database warehousing right. Call Four Cornerstone today!