ETL
Data profiling is the essential discovery process that helps you analyze, classify, cleanse, integrate, mask, and report on data in your repositories. With the information profile processes produce, you can make decisions about data integration and analysis, migration and masking, etc.
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Data Masking/Protection
IRI is now also delivering fuzzy search functions, both in its free database and flat-file profiling tools, and as available field-function libraries in IRI CoSort, FieldShield, and Voracity to augment data quality, security, and MDM capabilities.
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Big Data
IRI has completed a software development kit (SDK) to help you use FieldShield‘s column-level encryption, decryption, hashing, and redaction algorithms in your own Java and .NET projects for more dynamic data masking requirements.
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Data Masking/Protection
Singapore NRICs are used in much the same way Social Security numbers are used in the United States. The Singapore NRIC identifies citizens, permanent residents, or any other person who has entered the country for an extended stay on visa.
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Big Data
UPDATE: Q1’17: Included JDBC and ODBC drivers, and pending native JSON handling will make the connections faster and more seamless than the approach shown below. Please contact fieldshield@iri.com or voracity@iri.com for details.
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Big Data
UPDATE: Q1’17: Included JDBC and ODBC drivers, and pending native JSON generation will make the connections faster and more seamless than the approach shown below. Please contact rowgen@iri.com or voracity@iri.com for details.
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Test Data
Credit card numbers (CCN) or primary account numbers (PAN) are the string of digits on the front of most credit cards. The major cards here in the United States are 16 digits long.
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Big Data
Introduction: This example demonstrates an older method of using IRI RowGen to generate and populate large or complex collection prototypes for testing or system capacity. As you will read, RowGen would create the necessary test data and create a CSV file that would be loaded into MongoDB using the Mongo Import Utility.
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Big Data
Introduction: This example demonstrates an older method of using IRI FieldShield to protect data found within MongoDB tables. As you will read, the MongoDB Export Utility would be used to extract data and create a CSV file that FieldShield would then process and apply data protections prior to loading the newly secured data back into MongoDB.
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Data Masking/Protection
Update: Q2’16: In addition to the database profiling wizard in the data discovery menu group in IRI Workbench described below, IRI has introduced robust data classification that enables the application of field rules for multi-source data transformation and protection through data class libraries.
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