Tag Archives: Improved features

Automated Performance Optimization

- Hash-bytes – Compressions: supports row-and table based compression on both staging and data warehouse. Implemented automatically based on table classification. Can be manually configured in advance mode.

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Data Warehouse Hierarchy Builder

- Visual builder for GL acccounts dimension (balance sheet, income statement). – Include custom rollup formulas (dimension calculations). – Generic usage for mapping any structure into reporting format.

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Indexes

TX2014 supports both automatic created indexes as well as rich capabilities to add your own non clustered indexes to fine tune the load performance of your data warehouse platform. Automatic indexes are added based on table classification such as dimension/fact/large/normal and transformations done on a table, to ensure well performing code out of the box.

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Lookups

One of the most commonly features of TX2014 is the ability to look up a value across tables using a simple drag and drop interface. Lookups can be used to perform both simple tasks as fetching a single value from a related table based on one or more joined fields, but also in more complex

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Move Projects As XML Between Installations

- Move or copy an entire solution between installations, customers, or environments. – Works across different SQL Server versions and editions.

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Multithreading

As part of getting the best load performance, it is possible to design execution packages for multi-threaded execution – or parallel execution, using a user-defined number of threads per package. The flexible design capabilities allow configurations such as a single threaded master package that controls multi-threaded sub packages. Another common configuration is a “string of

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Table Partitions

Table partitioning offers the ability to separate the contents of a logical table into separate physical storage units. This can greatly improve performance as only the partition(s) affected by a query, an insert or update operation will be touched. TX2014 manages partitions with a template based approach, which allows easy reuse of partition patterns across

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Time Dimensions

The most common of all dimensions is time, since there is virtually no reporting or analysis done without the context of when. A time or calendar dimension can always be derived of source data, as any other dimension. Additionally you can also take advantage of the time table and time dimension features in TX2014 to

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Slowly Changing Dimensions

Slowly changing dimensions type 1 and 2 is a key to Kimball style dimensional modeling and is of course supported in TX2014 with an easy to use interface, that allows implementation within minutes – without any SQL knowledge required. The TX2014 implementation further includes the use of hashed values for better performance on large dimensions.

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