Post by romanamitaseo22 on May 19, 2024 5:32:33 GMT -5
Kimball-style dimensional modeling has been the go-to architecture for most data warehouse developers over the past couple of decades. The denormalized nature of these schemas, coupled with optimization for history maintenance, makes the dimensional model an ideal tool for the data warehousing arsenal, especially for reporting through business intelligence (BI) tools. On the face of it, the idea is simple: Fact tables contain transactional information, and dimensions provide context to these facts through foreign key relationships. The questions that arise, however, are the following: How easy is it to load and maintain data in fact and dimension tables? And Is it worth the effort? Your Complete Guide To Data Warehousing DOWNLOAD WHITEPAPER Let’s take a scenario where you’ve set up an architecture for your data warehouse.
Loading data into dimension tables can be simple, given that you’re not looking to maintain history. In such a case, you would only want to update the destination records, which can be performed via Slowly Changing Dimensions Type 1 (SCD1). Here’s a snippet of what that query would look like: However, it is unlikely that this would be sufficient in a practical business scenario. It’s important to maintain Antigua and Barbuda Email List at least some history in a data warehouse to identify trends and patterns. That is where other, more complicated, SCD types come into play, such as SCD 2, 3, and 6. If you intend to use SCD 2 or 6 on certain fields, the table needs to contain record identifiers as well to recognize the active row for each record. This could be a true/false flag, an effective expiration date range, or just a version number for each record, to name a few examples.
You would likely require different levels of history for different fields. Let’s say, for instance, that you have an employee dimension that contains employees’ salary information and phone number. Here, you may want to keep track of how an employee’s salary is changing but just update the phone number. Save Time And Increase Efficiency With Data Warehouse Automation LEARN MORE For cases like this, you would use multiple SCD types; SCD 1 for the fields that merely require updates and SCD 2, 3, or 6 for those fields that require a certain level of history to be maintained. With so many things to take into account, you can imagine how complex the query would get! So far, We’ve focused on the population and maintenance of dimension tables. These dimensions provide context to the information stored in fact tables.
Loading data into dimension tables can be simple, given that you’re not looking to maintain history. In such a case, you would only want to update the destination records, which can be performed via Slowly Changing Dimensions Type 1 (SCD1). Here’s a snippet of what that query would look like: However, it is unlikely that this would be sufficient in a practical business scenario. It’s important to maintain Antigua and Barbuda Email List at least some history in a data warehouse to identify trends and patterns. That is where other, more complicated, SCD types come into play, such as SCD 2, 3, and 6. If you intend to use SCD 2 or 6 on certain fields, the table needs to contain record identifiers as well to recognize the active row for each record. This could be a true/false flag, an effective expiration date range, or just a version number for each record, to name a few examples.
You would likely require different levels of history for different fields. Let’s say, for instance, that you have an employee dimension that contains employees’ salary information and phone number. Here, you may want to keep track of how an employee’s salary is changing but just update the phone number. Save Time And Increase Efficiency With Data Warehouse Automation LEARN MORE For cases like this, you would use multiple SCD types; SCD 1 for the fields that merely require updates and SCD 2, 3, or 6 for those fields that require a certain level of history to be maintained. With so many things to take into account, you can imagine how complex the query would get! So far, We’ve focused on the population and maintenance of dimension tables. These dimensions provide context to the information stored in fact tables.