- #DATABASE WORKBENCH LOGICAL DATA MODELING HOW TO#
- #DATABASE WORKBENCH LOGICAL DATA MODELING LICENSE#
Multidimensional Data Modeling in Pentaho. Train or purchase Pentaho training for those in your organization who use You're now ready to put it all into production. Your data warehouse and Mondrian schema have been created, tested, and With your users occasionally to see if they have any particular concerns The possibility that performance issues may come up in the future. You may not have a real need for aggregation tables. With a relatively small data warehouse or a limited number of dimensions, Re-test and create new aggregation tables as necessary. Using your notes as a guide, create aggregation tables in Pentaho Aggregation Designer to store frequently computed Analyzer reports. Also, enable SQL logging and locate slow-performing queries, andīuild indexes for optimizing query performance. Note of all of the measures that take an unreasonably long time toĬalculate. Testing can only be reasonably done by hand, using Pentaho Tuning your data warehouse database, and by creating aggregation tables. Model, you should try to find performance problems and address them by Once you are satisfied with the design and implementation of your data Implementation and continue to refine the data model until it matches your Multiple fact tables by conforming dimensions. Warehouse and Mondrian schema appropriately. Use the notes you took during the testing phase to redesign your data Performance issues at this time, just concentrate on the completeness and Unhappy with during this initial testing phase. In all likelihood, it will need someĪdjustment, so take note of all of the schema limitations that you are Tools to drill down into your data and see if your first attemptĪt data modeling was successful. You can now start using the data inspection You can use the Pentaho Schema Workbench to create an analysisĪt this point you should have a multi-dimensional data structure with anĪppropriate metadata layer. Mondrian schema to organize and describe it in terms that Pentaho Now that your initial data warehouse project is complete, you must build a Job is Pentaho Data Integration, an enterprise-grade extract, transform, and Once your data model is designed, the next step is to populate it withĪctual data, thereby creating your data warehouse. To your initial data model after you have discovered what your operational Process is coming back to the data warehouse design step and making changes 
Just cover all of your anticipated business needs part of the Youĭo not have to worry too much about getting the model exactly right on yourįirst try. TheĮnd result should be data model in the star or snowflake schema pattern. The subject, and an entire consulting industry dedicated to it already.
#DATABASE WORKBENCH LOGICAL DATA MODELING HOW TO#
This section will notĪttempt to explain how to build this structure - there are entire books on The entire process starts with a data warehouse. You can refine your Pentaho relational metadata and multidimensional Mondrian data models. This process is covered in the Installation documentation.Īll relevant configuration options for these features are covered in this section. A special Pentaho Server package must also be installed
#DATABASE WORKBENCH LOGICAL DATA MODELING LICENSE#
Use of these features requires a Pentaho Analysis Enterprise Edition license installed on the Pentaho Server and workstations that have Schema Workbench and Metadata Editor. Implementations including Infinispan and Memcached. A pluggable Enterprise Cache with support for highly scalable, distributable cache.Pentaho Analysis Enterprise Edition customers, Pentaho also offers expanded functionality for Only when you have a tested and optimized Mondrian schema is your data prepared on a basic The form of a Mondrian schema, which consists of one or more cubes, hierarchies, and members. You have your initial data structure in place, you must design a descriptive layer for it in OLAP requires a properly prepared data source in the form of a star or snowflake schema thatĭefines a logical multi-dimensional database and maps it to a physical database model. Numerical values in each cell are the measures or facts.
That describe and bring meaning to the data in that grid are dimensions, and the hard OLAP relies on a multidimensionalĭata model that, when queried, returns a dataset that resembles a grid. The Mondrian online analytical processing (OLAP) engine.
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