TX DWA automates the tedious parts of building a data warehouse so you can have your data warehouse ready up to five times faster than it is possible with traditional development tools.
The overall purpose of a data warehouse is to integrate corporate data from various internal and external data sources. Data is collected over time and stores historical events that are often not persisted in the source systems. The data warehouse is also commonly referred to as the “single version of the truth,” making it an important asset for the organization. Data warehouses can be modeled in relational or dimensional forms, of which the latter is the most common.
The star schema model is the most widely used data model for data warehousing and the de facto standard for business-focused reporting. Although the metadata-driven engine in TX DWA is powerful enough to support other models, TX DWA has an emphasis on the star schema model, also known as Kimball dimensional modeling. This means that features such as surrogate keys, slowly changing dimensions, aggregations and almost everything else from the 34 subsystems of ETL are supported.
TX DWA is also built to scale and perform and includes support for a wide range of loading strategies, such as source- and target-based incremental load – just as table partitioning and data compression are five minute operations to implement.
The data staging area is a temporary location for data between the source systems and the data warehouse. This is where source data is copied in the “extract” phase of ETL, and this is where the first integration point for data coming from multiple source systems. This is also where the majority of data cleansing and data validation takes place, prior to copying the cleaned and conformed data into the data warehouse for persistent storage.
The rich transformation and data validation features available in TX DWA allow for maximum flexibility, whether you simply wish to use the staging area as a time-wise integration point for data from multiple sources with different load schedules, or if you want to implement a fine-grained cleansing procedure. TX DWA has everything from rich drag-and-drop features to custom SQL, user-defined functions, and stored procedures.
Load strategies include both source- and target-based incremental load, full load, and windowed loads, and these can be configured on a per table basis.
Data Lineage Diagrams
Most data warehouse developers have experienced the challenge of answering a common question like “what data source fields are we using for this measure?”
If your business is one of the many that either does not have complete end-to-end documentation or has documentation that needs to be updated, the tracing features of TX DWA will come in handy.
Whether you are interested in knowing where a particular object gets its data or what objects get their data from a particular object, a diagram shows you how data flows.
TX DWA includes fully automated documentation generation, including full version control, to ensure that the generated documentation corresponds to the deployed version of the project. Comments may be added to all elements in the project. The documentation comes as a hyperlinked PDF document based on a template that you can modify as you please.
Object and Data Level Security
In TX DWA, you can control access to data on the data warehouse both on the object and on the data level. This is convenient when you do reporting directly from the data warehouse.
Object level security is handled with an integration of SQL Server database roles and the grant/deny concept. On the data level, you can configure what rows a user or database role can access based on the content of one of the columns. For instance, a sales person might only be able to see data where the sales region that matches his own.
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