However, there are requirements when the OOTB cleanse functions are not enough and there is a need for comprehensive functions to achieve data cleansing and standardization, for e. This blog post describes the various options to integrate Informatica MDM and IDQ, explains the advantages and disadvantages of each approach to aid in deciding the optimal approach based on the requirements. Figure 1: Informatica Platform Staging Process Advantages Stage tables are immediately available to use in the Developer tool after synchronization eliminating the need to manually create physical data objects. Changes to the synchronized structures are reflected into the Developer tool automatically.
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EC Aug 31, AM in response to jagadish. Business analysts and developers use Informatica Analyst for data-driven collaboration. You can perform column and rule profiling, scorecarding, and bad record and duplicate record management. You can also manage reference data and provide the data to developers in a data quality solution. Use Informatica Analyst to accomplish the following tasks: Profile data.
Create and run a profile to analyze the structure and content of enterprise data and identify strengths and weaknesses. After you run a profile, you can selectively drill down to see the underlying rows from the profile results.
You can also add columns to scorecards and add column values to reference tables. Create rules in profiles. Create and apply rules within profiles. A rule is reusable business logic that defines conditions applied to data when you run a profile. Use rules to further validate the data in a profile and to measure data quality progress.
Score data. Create scorecards to score the valid values for any column or the output of rules. Scorecards display the value frequency for columns in a profile as scores. Use scorecards to measure and visually represent data quality progress. You can also view trend charts to view the history of scores over time.
Manage reference data. Create and update reference tables for use by analysts and developers to use in data quality standardization and validation rules. Create, edit, and import data quality dictionary files as reference tables.
Create reference tables to establish relationships between source data and valid and standard values. Developers use reference tables in standardization and lookup transformations in Informatica Developer.
Manage bad records and duplicate records. Fix bad records and consolidate duplicate records. Informatica Developer is an application client that developers use to design and implement data quality and data services solutions. The following figure shows the Developer tool: The Developer tool includes an editor, in which you can edit objects. Depending on the object in the editor, the Developer tool displays views, such as the default view.
The Developer tool also includes the following views that appear independently of the objects in the editor: Object Explorer. Shows projects, folders, and the objects they contain. Shows dependent objects in an object. Shows object properties. Data Viewer. Shows the results of a mapping, data preview, or an SQL query. Validation Log. Shows object validation errors.
Cheat Sheets. Shows cheat sheets. Informatica Data Explorer is a profiling tool that you can use to find the content, quality, and structure of data sources of an application, schema, or enterprise. The data source content includes value frequencies and data types. The data source structure includes keys and functional dependencies. As part of the discovery process, you can create and run profiles in Data Explorer.
A profile is a repository object that finds and analyzes all data irregularities across data sources in the enterprise and hidden data problems that put data projects at risk.
Profiling the current data sources using Data Explorer gives you a good understanding of the strengths and weaknesses of data and metadata. With Data Explorer, you can use the Analyst tool and Developer tool to analyze the source data and metadata.
Analysts and developers can use these tools to collaborate, identify data qualilty issues, and analyze data relationships.
Based on your job role, you can use the capabilities of either the Analyst tool or Developer tool. The degree of profiling that you can perform differs based on which tool you use. You can perform the following tasks in both the Developer tool and Analyst tool: Perform column profiling. The process includes discovering the number of unique values, null values, and data patterns in a column.
Create scorecards to review data quality. Create and assign tags to data objects. You can perform the following tasks in the Developer tool: Discover the degree of potential joins between two data columns in a data source.
Determine the percentage of overlapping data in pairs of columns within a data source or multiple data sources. Compare the results of column profiling. Generate a mapping object from a profile. Build a profile model for profiling and data discovery.
Discover primary keys in a data source. Discover foreign keys in a set of one or more data sources. Discover functional dependency between columns in a data source.
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