Business Intelligence In Sql Server – Introduction. The tools needed to create these solutions are briefly mentioned. The purpose of this article is to give you a deeper understanding of ETL (Extract, Transform and Load) data flow design. To do this, you need to use SQL Server Data Tools – Business Intelligence (formerly known as BIDS or Business Intelligence Development Studio). In this article, we’ll look at the main features of SQL Server Data Tools and how to use them to keep your data warehouse up to date. It is important to note that there are many different ways to build your ETL solution. This article provides good tips and advice on how to solve the problem. Data warehouse submission
This article assumes that you have already created a denormalized datastore. If you want more information on how to create a database based on a star schema, you can read more here
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Once you’ve created a denormalized database model for your data warehouse, you’ll need to populate it with data and schedule regular updates to keep your SQL Server BI data up-to-date. Depending on your version of SQL Server, you may be able to do this using BIDS or SQL Server Data Tools.
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This program is a very effective ETL and automation tool. You can do anything from running system processes and PowerShell scripts, running raw T-SQL and custom C# scripts to sending emails or connecting to FTP servers or web services. However, a commonly used tool is the Data Flow Task.
The data store does not need to be updated frequently. Since business intelligence is about analyzing historical data, today’s data is not very important. It is also clear that there is no need to generate and populate the database every day. Therefore, it is necessary to maintain a database maintenance history log table.
To enable connections between one or more sources and/or one or more destinations, a connection manager is typically the starting point of an ETL package. There are standard database connections (ODBC, OLEDB, ADO.NET, etc.), connections to HTTP, FTP, and SMTP servers, and connections to various file types such as CSV, flat file, or even Excel. However, for a simple BI data flow, two OLE DB connections are sufficient: one for data storage and one for OLTP/Production databases. You can configure it as follows.
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(Note that this connection is on the local computer and uses Windows authentication. You can always connect directly to another instance in the IP_ADDRESSINSTANCE format, and you can use SQL Server to authenticate it if the instance doesn’t support Active Directory accounts .)
The definition of this role is simple; is a member of a service that connects data to a destination and provides the ability to modify the data flow in several ways. Sorting, transforming, binding, calculating, and gathering are some of the tools that can be used within a data flow task.
For SQL Server, a business intelligence solution is usually to retrieve data from an OLTP database, transform it from a relational model, and put it into your database’s database or ice cube. The formal approach, if you know how to write T-SQL, is to write SELECT statements that can populate destination and dimension fact tables based on your OLTP database tables and store them as views in the database. This allows you to have a direct mapping of an object in your OLTP database to an object in your data warehouse without creating complex ETL packages.
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For fact tables you usually have date/time fields and you can create a simple data flow from the data warehouse view in the OLTP database to the fact table in the data warehouse. You can do this by running the Execute SQL function just before the database function and mapping the result set to a variable that stores the last time this table was modified. You can use the following query for this
As you can see above, you added the “?” parameter to the original query. This allows you to add a deadline to the request. You can do this by clicking “Parameters…” and setting the Parameter0 parameter to your variable.
If you have a source that only receives current data that is not in your database, you can create target joins and map fields to fact table fields.
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After the dataflow task is executed, you need to add an Eecute SQL function to update the log table to prepare for the next day of ETL processing:
Dimension tables usually need to be handled in different ways, but two common ones include comparing source and destination objects and passing variables. This can be reduced to using the Slowly Changing Dimension function or Execute SQL and the MERGE statement if the two databases are in the same instance.
It may seem strange to mention the SQL Server Agent when looking at business intelligence, but it is key to keeping the framework up to date. Once you’ve created an ETL package, you should update it and run it regularly to ensure your data warehouse is up to date.
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Now that you have an ETL solution running and providing your data warehouse with interesting business intelligence data, you can take one of two approaches. If for whatever reason you can’t use SSAS to create a multidimensional cube, you can start building SSRS reports using SQL queries directly against your new data warehouse system.
Having worked on highly transactional production systems and advanced enterprise business intelligence, Evan is now using this expertise to transform the world of health. He is driven by his love for technology and the desire to creatively solve complex problems.
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