Performance of a data warehouse is as important as the correctness of data in the data warehouse because unacceptable performance may render the data warehouse as useless. There is this increasing awareness about the fact that it’s much effective to build the performance from the beginning rather than to tune the performance at the end. In this article we have a few points that you may consider for optimally building the data model of a data warehouse. We will only consider performance considerations for dimensional modeling.
In this "DWBI Concepts' Original article", we put Oracle database and Informatica PowerCentre to lock horns to prove which one of them handles data SORTing operation faster. This article gives a crucial insight to application developer in order to take informed decision regarding performance tuning.
This article is a comprehensive guide to the techniques and methodologies available for tuning the performance of Informatica PowerCentre ETL tool. It's a one stop performance tuning manual for Informatica.
To me, look-up is the single most important (and difficult) transformation that we need to consider while tuning performance of Informatica jobs. The choice and use of correct type of Look-Up can dramatically vary the session performance in Informatica. So let’s delve deeper into this.
Joiner transformation allows you to join two heterogeneous sources in the Informatica mapping. You can use this transformation to perform INNER and OUTER joins between two input streams.
For performance reasons, I recommend you ONLY use JOINER transformation if any of the following condition is true –
Similar to what we discussed regarding the Performance Tuning of Joiner Transformation, the basic rule for tuning aggregator is to avoid aggregator transformation altogether unless...
In our earlier article we have discussed the need of storing historical information in dimensional tables. We have also learnt about various types of changing dimensions. In this article we will pick "slowly changing dimension" only and learn in detail about various types of slowly changing dimensions and how to design them.
In this multi part tutorial we will learn the basics of dimensional modeling and we will see how to use this modeling technique in real life scenario. At the end of this tutorial you will become a confident dimensional data modeler.
A model is an abstraction of some aspect of a problem. A data model is a model that describes how data is represented and accessed, usually for a database. The construction of a data model is one of the most difficult tasks of software engineering and is often pivotal to the success or failure of a project.