Informatica Cloud offers REST API for us to interact with the platform programmatically. At this point in time, the latest official reference is found here. In this post, we are going to use Python to trigger jobs through API. Triggering a job is a 2-step process. We need to authenticate …

There will be time when you want to upload a big csv file (with many rows and hundreds of columns) to a relational database table. Talend Open Studio is an open source ETL tool that I use regularly to do odd jobs like that. I like using it because it …

Mappings are where all the magic happens in Informatica Cloud. When I started using it, it took me a while to work out how to rename a mapping job. Since then, a few people asked me the same question. So, I decided to write about it. This is probably the …

Joiner is the stage to join tables in Informatica Cloud (see a quick introduction for Joiner Transformation here). If you have a large volume of data, the joiner transformation becomes very slow without performance optimisation. In this post, we will show you a few tricks that you can use to …

By default, the secure agent can run 2 data synchronisation tasks at a time. This constraint can become limiting quickly especially when multiple developers are building and testing the data synchronisation tasks at the same time. By adding a custom property on the secure agent, you can run more than …

Informatica does not have a dedicated Postgres database connector. Therefore, we need to use the ODBC connector. In this post, I will discuss how to configure Postgres ODBC in both Linux and Windows servers for the Informatica Cloud ODBC connector. Linux Server (Red Hat) There are a few instructions, but …

The Transformer stage has the built-in looping functionality where you can use Stage Variables and Loop Conditions to construct looping logics. In this post, we will present 3 different examples. Ranking Aggregation Vertical Pivot Before going into the examples, here are the useful variables for loop construction. @ITERATION – System …

When flat file has leading and trailing lines that are not part of the table, we can use the filter in the flat file stage to remove them. As an example, the file below has a leading and trailing lines. We want remove them with the flat file stage. Output Steps …

DataStage has three processing stages that can join tables based on the values of key columns: Lookup, Join and Merge. In this post, we discuss when to choose which stage, the difference between these stages, and development references when we use those stages. Use the Lookup stage when: Having a …

Hierarchy Parser in the Informatica Cloud mapping designer can transform JSON or XML files into structured table (see instruction here). In this post, we will transform the JSON file obtained from Google Geocoding API. Geocoding API turn addresses (1600 Amphitheatre Prakway Mountain View CA) into geographic coordinates (latitude: 37.422, Longitude: -122.085 etc) …