This article documents the connections necessary in IRI Workbench and 64-bit CoSort-compatible runtime environments to work with 64-bit Informix Dynamic Server (IDS) v12 table sources and targets.
In the previous article, we discussed how IRI DarkShield finds sensitive information using Data Class Search Matchers and masks it consistently using Data Rules.
IRI DarkShield is a data masking tool for finding and de-identifying Personally Identifiable Information (PII) and other sensitive data in semi-structured and unstructured files and databases.
IRI Workbench now contains a section of data cleansing, enrichment, and validation rules called Data Quality Rules for use in IRI CoSort data transformation and IRI Voracity ETL, reporting and data wrangling jobs.
Elasticsearch is a Java-based search engine that has an HTTP interface and stores its data in schema-free JSON documents. Unfortunately, a spate of costly and painful breaches of Personally Identifiable Information (PII) continue to plague online Elasticsearch databases:
Were all the PII or other sensitive information in these DBs masked however, successful hacks and development copies may not be problematic.
Whether your SQL Server database is on-premise or in a cloud platform like Azure, its data is accessible for movement and manipulation in IRI Workbench-supported products like CoSort, FieldShield, DarkShield, NextForm and RowGen, or the IRI Voracity platform which includes them all.
Connecting to a database like Oracle can be the most frustrating part of application installation and configuration. The many options available with JDBC and ODBC drivers can be daunting.
Once they have made their database connections, IRI FieldShield and IRI DarkShield users both have a wide range of options for classifying, finding, and de-identifying sensitive data across one or more schemas.
Personally Identifiable Information (PII) like names, Social Security numbers, home addresses, etc. are stored in multiple sources and silos, including semi-structured files in JSON and XML format.
This is the second of a two-part blog series detailing data class validation in IRI Workbench. The first article, here, provided an overview of the validation scripts and how to use them in a data discovery or classification job.
This is the first of a two-part blog series detailing data class validation in IRI Workbench. This article provides an overview of our provided validation scripts and how to use them in a data discovery or classification job.
