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.Before we tackle basic data modeling, we need some background on the systems involved. We can roughly divide systems in two categories: operational and reporting systems. Operational systems are often called Online Transaction Processing (OLTP). Reporting and analytical systems are referred to as Online Analytical Processing (OLAP). OLTP systems support business processes. They work with “live” operational data, are highly normalized, and react very quickly to user actions. On the other hand, the primary purpose of the OLAP systems is analytics. These systems use summarized data, which is usually placed in a denormalized data warehousing structure like the star schema. (What is denormalization? Simply put, it’s having redundant data records for the sake of better performance.
With the release of SQL Server 2016’s Community Technology preview, we’re finally able to get an in-depth look at the exciting options SQL Server 2016 includes. Two of the most anticipated areAlways EncryptedandIn-memory OLTP.Always Encryptedis a new method of consistent encryption intended to greatly simplify data protection. In-memory tables, while technically included in SQL 2014, were painful to implement and had significant limitations; SQL Server 2016 removes many of these and makes
IntroductionAs I mentioned in my article “OLAP for OLTP practitioners” , I am working on a project that needs to create ananalyticaldatabase for on-line analytical processing (OLAP). I have mostly worked with on-line transaction processing (OLTP) with some limited reporting features. OLAP is a new area for me. In OLAP, the main focus of the database itself is simply to store data for analysis; there is limited maintenance of data. In the previous article, I focused on the differences in
I am currently working on a project where we need to create a database that will be primarily used to store data for reporting and forecasting. In the past, I have mostly worked with databases used for typical CRUD (create, retrieve, update, and delete) operations of data with some limited reporting features. When performing CRUD operations, normalization is important; while in analytics, a de-normalized structure is generally preferred.Here is a simple example of a normalized order table: