Legacy ETL workloads running on Informatica Powercenter can pose a huge challenge for the AI ambitions of modern enterprises. As of 2024, barely 5% of Informatica’s customers have migrated to cloud, leaving a huge opportunity for migration platforms providers.
However, the big question is: “Why migrate the legacy ETL to cloud?” This blog unveils all the challenges associated with the current Informatica ETL pipelines. We will also see how legacy data can be detrimental to an organization’s growth especially in today’s AI first ambitions.
Legacy PowerCenter data pipelines are like old but reliable assembly lines that many organizations still trust to move and process their data. Built using Informatica PowerCenter, these pipelines are designed to extract data from various sources, transform it to meet business needs, and load it into target systems like data warehouses. Think of it like a package delivery service: data gets picked up from a source (like a sales database), cleaned and standardized (fixing formats, removing duplicates), and then delivered to its destination (such as a dashboard or another database). Informatica PowerCenter data pipelines are built using workflows, which are essentially the blueprints for how data should be moved and processed. For PowerCenter developers, building a workflow is like setting up a well-organized assembly line with each step having a clear purpose, and the goal is to ensure that the data is properly transformed and delivered to its destination.
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PowerCenter ETL processes consist of workflows, mappings, transformations, sources, and targets. These elements define how data is ingested from any source and where it is directed after the transformation is applied. For example, a workflow might extract customer data from an Oracle database, apply transformations to clean and enrich it, and then load the final output into a SQL Server-based reporting system.
Workflows: In PowerCenter, a workflow is the process that manages how data is moved and transformed from source to destination. It defines the steps involved in extracting data like for instance customer records, applying necessary changes or clean-up, and then loading it into the final system (like a reporting database). For a business user, think of it as the automated pipeline that ensures the right data is collected, processed, and delivered where it’s needed, accurately and on time.