There are existing information systems such as CRM’s (Siebel, etc) and ERP’s (EBS, SAP, etc) where the information is stored in a way in which it is easy to extract information and feed our data warehouses, however a large number of customers still manage their information in so-called flat files (i.e.: .csv, .xls, etc) which are not as user-friendly.
Such situations still commonly exist; therefore, the purpose of this article is to explain the process to resolve this.
When implementing an ETL, one of the most common tasks is the deduplication of rows. If you need to implement this in PL/SQL, one method could be with correlated subqueries. However, in this article we will take the approach sometimes forgotten by developers, which is using Analytic Functions.
The main objective of this article is to compare the two different ways to realize the deduplication.
In today’s business world, quick access to data is key in making the right decisions at the right time.
This article, based on a real case, highlights the importance of using an incremental load using the change data capture technique for loading data to the dashboards for the end users.
One of the greatest advantages of buying an OBI Application – Project, Supply Chain or any other of the many Analytics flavours – is the set of predefined ETL mappings, sessions and workflows that come with it.
Although there is a good chance that the OLTP data source is highly customised, the online Oracle documentation is full of information that can make the ETL developer’s life easier. That said, there are some important ETL tasks whose logic isn’t very easy to find. They are like black boxes: you customise them a little bit – some fields behind the X_CUSTOM placeholder here, a small datatype change in the target table there – and they operate their magic.
So let’s reveal the truth behind the veil of a very important out of the box ETL logic in OBI Apps: the Slowly Changing Dimension management mappings.
In a not so remote past, one of the biggest frustrations for ETL designers was having to replicate a job many times in order to accommodate slight differences depending on the data source. Whether it’s a change in the source table definition or a different clause in the extract SQL, today’s ETL tools have embed capabilities that allow for the reuse of metadata objects to save time and improve maintainability of the workflows.
In this post, I will describe with simple examples how to best reuse ETL mappings in Informatica PowerCenter using the SQL override capability.
