Start the Data Generator from the Data Tools application menu or by right-clicking any table header. Saving the model file will save the layouts as well as the data generator settings. dbs model file.įirst, create a layout with the tables for which you want to generate random data.Įach time you need a different set of tables, you have to create a new layout. The generator patterns are saved for each column in the model file. You can choose and edit for each column one of the predefined patterns from the Pattern Repository. The first time you access the Data Generator feature, DbSchema will try to find a pattern for you. All other patterns will be interpreted as Reverse Regular Expressions.$patternA $patternB - combine two patterns.groovy: - use own script to generate values.json_list: json_map: - we use this for MongoDb.Pick up a value from the Primary Key table. load_values_from_pk - foreign key columns (involved in a foreign key, referring other columns) should use this pattern.skip: - skip the column from generator (do not generate).Like int, short, sequence, boolean, groovy, etc.Īny other pattern which doesn't start with one of these keywords will be interpreted as reverse regular expressions. There are dedicated patterns for numbers, date, booleans, starting with a keyword The random generator is using patterns for setting how the generated data should look like. If you use IRI Voracity, you can use its included RowGen synthesis and FieldShield data masking capabilities to find, classify, subset, and mask data, and integrate that data for static development use in lower environments or virtual use in live testing environments.Use the Data Generator to fill database tables with random data. Use RowGen to populate an entire test enterprise data warehouse (EDW) or DataVault. Support for standard and complex data transformations, set files, and conditional selection also contribute to RowGen's value in simulating production table and file formats for a variety of applications.įor database users, RowGen leverages the DDL information for Oracle, DB2 UDB, SQL Server, Sybase, Teradata, and other platforms to create realistic tables with structural and referential integrity. That, along with custom/compound data values, value ranges, and distributions, improve test data realism. It can also randomly select data from set files at the field level. RowGen randomly generates field values in more than 100 data types. BIRT (via ODA) and KNIME (analytic & visualization nodes) in Eclipse.Image files and PDFs (using DarkShield with RowGen).Fixed position text and mainframe blocked.RowGen can create structurally and referentially correct test data for every popular RDBMS with defined constraints, plus test data in custom report layouts or popular file/feed formats like these: To discover (profile, search, and classify), de-identify (encrypt, pseudonymize, blur, redact, etc.), data in production systems and replicate it anonymized in lower dev, test and QA environments.
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