Working With Multiple Data Sources and Aggregator Transformation

Working With Multiple Data Sources and Aggregator Transformation

This tutorial shows the process of creating an Informatica PowerCenter mapping and workflow which pulls data from multiple data sources and summarize the data using Aggregator Transformation.

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Update Without Update Strategy for Better Session Performance

Update Without Update Strategy for Better Session Performance

You might have come across an ETL scenario, where you need to update a huge table with few records and occasional inserts. The straight forward approach of using LookUp transformation to identify the Inserts, Update and Update Strategy to do the Insert or Update may not be right for this particular scenario, mainly because of the LookUp transformation may not perform better and start degrading as the lookup table size increases.

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User Defined Error Handling in Informatica PowerCenter

User Defined Error Handling in Informatica PowerCenter

Error Handling is one of the must have components in any Data Warehouse or Data Integration project. When we start with any Data Warehouse or Data Integration projects, business users come up with set of exceptions to be handled in the ETL process. In this article,  lets talk about how do we easily handle these user defined error.

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Change Data Capture (CDC) Made Easy Using Mapping Variables

Change Data Capture (CDC) Made Easy Using Mapping Variables

At times we may need to implement Change Data Capture for small data integration projects which includes just couple of workflows.  Introducing a Change Data Capture framework for such project is not a recommended way to handle this, just because of the efforts required to build the framework may not be justified. Here in this article lets discuss about a simple, easy approach handle Change Data Capture.

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An ETL Framework for Change Data Capture (CDC)

An ETL Framework for Change Data Capture (CDC)

Change data capture (CDC) is the process of capturing changes made at the data source and applying them throughout the Data Warehouse. Since capturing and preserving the state of data across time is one of the core functions of a data warehouse, a change data capture framework has a very important  role in ETL design for Data Warehouses. Change Data Capture can be set up on different ways based on Timestamps on rows, Version Numbers on rows, Status indicators on rows etc. Here we will be building our framework based on "Timestamps on rows"  approach.

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