PowerCenter works well with large, structured data files
Anonymous | TrustRadius Reviewer
March 31, 2017

PowerCenter works well with large, structured data files

Score 6 out of 10
Vetted Review
Verified User
Review Source

Overall Satisfaction with PowerCenter

PowerCenter is being used to automate our ETL processes. We have implemented data pipelines to ingest and transform data from our clients. PowerCenter work flows also load data into a DBMS platform and create SAS data libraries.
  • PowerCenter processes input files, performs specified transformations, and maps the input data format to the output data format very quickly. The PowerCenter backend implementation seems to be optimized to process and map structured input records to structured output records and load the records into a database. One of the strengths of PowerCenter is performance of processing petabytes of structured input data files.
  • PowerCenter does not require a software development experience or education. After providing initial hands-on training, the data consultants (who are statisticians, subject matter experts) in our organization were able to implement data ingest and data transformation tasks fairly easily.
  • PowerCenter supports multiple DBMS technologies (for example, Oracle, Netezza). This flexibility allows it to be used by multiple departments within our organization.
  • One of the challenges of PowerCenter is the lack of integration between the components and functionality provided by PowerCenter. PowerCenter consists of multiple components such has the repository service, integration service, metadata service. Considerable time and resources were required to install and configure these components before PowerCenter was available for use.
  • In order to connect to various data sources such as Netezza database or SAS datasets, PowerCenter requires the installation and configuration of separate plug-ins. We spent considerable time trouble-shooting and debugging problems while trying to get the various plug-ins integrated with PowerCenter and get them up and running as described in the documentation.
  • PowerCenter works well with structured data. That is, it is easy to work with input and output data that is pre-defined, fixed, and unchanging. It is much more difficult to work with dynamic data in which new fields are added or removed ad-hoc or if data format changes during the data ingest process. We have not been as successful in using PowerCenter for dynamic data.
  • One of the challenges of learning PowerCenter is that it is difficult to find documentation or publications that help you learn the various details about PowerCenter software. Unlike SAS Institute, Informatica does not publish books about PowerCenter. The documentation available with PowerCenter is sparse; we have learned many aspects of this technology through trial and error.
  • The data pipeline automation capability of Informatica means that few resources are needed to pre-process the data that ultimately resides in a Data Warehouse. Once a workflow is implemented, manual intervention is not needed.
  • PowerCenter did require more resources and time for installation and configuration than was expected/planned for.
  • The lack of or minimal support of unstructured data means that newer sources of dynamic/changing data cannot be easily processed/transformed through PowerCenter workflows.

PowerCenter is well suited for processing of large amounts of data that is structured and pre-defined. It is well-suited for large organizations that have the resources to install, configure and support PowerCenter. It is well suited for large organizations that have a large number of data consultants/analysts that do not have a software development/programming background.

PowerCenter is not a good fit for smaller, agile organizations that work with unstructured data and changing/dynamic data.

Informatica PowerCenter Feature Ratings

Connect to traditional data sources
8
Connecto to Big Data and NoSQL
3
Simple transformations
9
Complex transformations
7
Data model creation
3
Metadata management
6
Business rules and workflow
8
Collaboration
5
Testing and debugging
5
Integration with data quality tools
4
Integration with MDM tools
Not Rated