Handling rapidly changing dimensions are tricky due to various performance implications. This article attempts to provide some methodologies on handling rapidly changing dimensions in a data warehouse.
"We have a simple data warehouse that takes data from a few RDBMS source systems and load the data in dimension and fact tables of the warehouse. I wonder why we have a staging layer in between. Why can’t we process everything on the fly and push them in the data warehouse?"
SQL is a language for accessing and manipulating database standardized by ANSI. To be successful with database-centric applications (which includes most of the applications Data Warehousing domain), one must be strong enough in SQL. In this article, we will learn more about SQL by breaking the subject in the form of several question-answer sessions commonly asked in Interviews.
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...