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Sep 19, 2023 · Here are key points to understand about primary keys in data warehousing modeling: Uniqueness: Every value in the primary key column must be unique within the table. Non-null: A primary key column ...
- Protect The Data Warehouse from Unexpected Administrative Changes
- Integrate Same Data
- Add Rows to Dimensions That Do Not Exist in The Source System
- Provide The Means For Tracking Changes in Dimension Attributes Over Time
- Maintaining Junk Dimensions
- Performance Compared to Larger Character Or Guid Keys
- Recommendations
If you are not using a special key as a primary key in a dimension, your onlyoption is to include the current business key as a primary key of the dimensiontable. In the case of a data warehouse, we are dealing with millions of recordswhich can span decades. In the case where we have included a natural businesskey as the primary key of the dimensio...
As said previously, a data warehouse receives data from multiple sources. Sometimes,the same dimension will receive data from multiple sources. For example, for anorganization who has employees at multiple sites, in the data warehouse you needto include all of them in one dimension for analysis purposes. In the case of OLTP,these data sets are main...
A dimension table can have records which are not in the source systems. Whenfact tables are updated, there can be multiple surrogate keys for one fact table. Let's look at the FactSales fact table. In this fact table, for a given record, the ProductKey, OrderDateKey, DueDateKey,ShipDateKey, CustomerKey, PromotionKey and CurrencyKey are surrogate ke...
This is something where you can’t avoid surrogate keys in dimension tables. As we know, keeping historical data is essential in a data warehouse. Therefore,dimension changes need to be tracked in the data warehouse. To facilitatehistorical tracking in a dimension, type two slowly changing dimensions (SCD) areused. Details of this is item are discus...
As we are aware, there are many statuses and flags in OLTP systems. Ideally,these need to be mapped to a dimension table in the data warehouse which will endup with a large number of dimension tables. However, to ease operations, multiplestatuses are combined to one junk dimension table. Since this dimension table doesnot exist, you need to add a p...
As said multiple times in this article, large volumes of data are used in a datawarehouse. Fact tables and dimension tables mainly join via surrogate keys. If thoseare integer columns, it will be better performing than using character columns.
Typically, an auto increment integer column is used as the surrogate key in adimension table. Normally, surrogate keys do not have any meaning except for a surrogatekey in the date dimension. In a date dimension, YYYYMMDD format is used mainly toenhance the data partitioning. 1. Read how to implement surrogate keys inSlowly Changing Dimensions
- Dinesh Asanka
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What is a primary key in data warehousing modeling?
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What are surrogate keys and natural keys in data warehousing modeling?
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Apr 16, 2018 · A surrogate key is a system generated (could be GUID, sequence, unique identifier, etc.) value with no business meaning that is used to uniquely identify a record in a table. The key itself could be made up of one or multiple columns (i.e. Composite Key). The following diagram shows an example of a table with a surrogate key (AddressID column ...
But recently, the standard practice is beginning to shift towards using hash keys instead of sequence values to assign surrogate keys, particularly within the data vault 2.0 approach to data warehousing. A hash key is the output from a hashing algorithm, where a specific input value is transformed into a distinct, unique string per input value.
Nov 29, 2023 · A data warehouse stores summarized data from multiple sources, such as databases, and employs online analytical processing (OLAP) to analyze data. A large repository designed to capture and store structured, semi-structured, and unstructured raw data. This data can be used for machine learning or AI in its raw state and data analytics, advanced ...
Apr 30, 2024 · Techniques like capacity planning, modular design, and embracing cloud computing are your go-to strategies. Incorporate the following data warehouse design best practices: 16. Leverage cloud computing to handle large data sets. Cloud computing leverages remote servers and services to store, process, and analyze data.
Sep 1, 2023 · Think of metadata as the 'data about data.' It gives structure to the data warehouse, guiding its construction, maintenance, and use. It has 2 types: Business metadata provides a user-friendly view of the information stored within the data warehouse. Technical metadata helps data warehouse designers and administrators in development and ...
