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The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another stage in the data workflow. Usually, the term data extraction is applied when ( experimental ) data is first imported into a computer from primary sources, like measuring or ...
This lets businesses forgo preload transformations and replicate raw data into their data warehouses, where it can transform them as needed using SQL. After having used ELT, data may be processed further and stored in a data mart. [13] Most data integration tools skew towards ETL, while ELT is popular in database and data warehouse appliances.
While all Access data can migrate to SQL Server directly, some queries cannot migrate successfully. In some situations, you may need to translate VBA functions and user defined functions into T–SQL or .NET functions / procedures. Crosstab queries can be migrated to SQL Server using the PIVOT command.
In other cases data might be brought into the staging area to be processed at different times; or the staging area may be used to push data to multiple target systems. As an example, daily operational data might be pushed to an operational data store (ODS) while the same data may be sent in a monthly aggregated form to a data warehouse.
The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data. [5]
Data virtualization is an approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located, [1] and can provide a single customer view (or single view of any other entity) of the overall data.
Fact_Sales is the fact table and there are three dimension tables Dim_Date, Dim_Store and Dim_Product. Each dimension table has a primary key on its Id column, relating to one of the columns (viewed as rows in the example schema) of the Fact_Sales table's three-column (compound) primary key (Date_Id, Store_Id, Product_Id).
The base data and the dimension tables are stored as relational tables and new tables are created to hold the aggregated information. It depends on a specialized schema design. This methodology relies on manipulating the data stored in the relational database to give the appearance of traditional OLAP's slicing and dicing functionality.