Power*MatchMaker will cleanse your data, validate and correct addresses, identify and remove duplicates, and build cross-references between source and target tables. Power*MatchMaker software provides business users with complete and accurate data, and a single 360º view of each customer, product, sales rep and business unit.
Whether you’re building a Data Warehouse, Data Mart or CRM, the Power*MatchMaker goes a long way towards ensuring the data integrity of your decision support environment or CRM database.
Here are some key features of “Power MatchMaker”:
· Transforms and cleanses Key Dimensions
· Validates and corrects address information
· Accepts user-defined data matching criteria
· User-friendly, highly-intuitive interface for Match Verification
· Allows for user confirmation of duplicates through the online verification facility
· Merges duplicate records and their related data
· Allows for Backup of impacted records prior to data merging
· Builds cross-reference tables to link source systems’ identifiers (Primary Keys) to the target database identifiers
· Runs against the entire database to perform initial data cleanup, or incorporated into the data load process
Usage:
To launch the MatchMaker from the command line, do the following:
% java -jar $MM_HOME/matchmaker.jar
(where $MM_HOME is the directory this README file is in).
For medium-to-large projects (with more than a few thousand records),
the default memory size limit (often 64MB) may not be enough. To extend the limit, use:
% java -Xmx600M -jar $MM_HOME/matchmaker.jar
That will give you 600MB of heap space to play with.
Requirements:
· Java Runtime Environment (JRE) – tested with JRE6 on Ubuntu 7.10
What’s New in This Release:
· This release adds a user preference for auto-login into a repository and new munge steps, including data type conversion steps (eg. Date-to-String) and a CSV writer step.
· It is now possible to specify a set of columns to use as the unique index, even if the table doesn’t have a primary key.
· Of particular importance to new users following the example in the documentation, the example table can now be created properly in the built-in database.
· Matching, merging, and cleansing projects are now fully functional in the built-in repository as well.