This Jenkins entry is the fourth in a series of articles on the use of IRI test data management software – that masks, synthesizes, or subsets data – to feed safe, referentially correct test data for DevOps to CI/CD environments.
This is the third addition to the DevOps pipeline series. Here are the links to the DevOps articles related to GitLab and AWS CodePipeline.
What is Being Demonstrated?
This article demonstrates how to automate the execution of IRI DarkShield data masking jobs from SSH commands run from within the AWS CodePipeline to produce fit-for-purpose test data in the DevOps process.
This article demonstrates how to synthesize and write data to CSV, JSON, and XMLfiles, plus an Oracle table all at once via the IRI RowGen New Test Data Job wizard in IRI Workbench.
As of CoSort Version 10.5, the IRI Workbench IDE includes a Data Vault Generator wizard to help IRI Voracity platform users migrate a relational database model to a Data Vault 2.0 (DV) architecture.
SortCL, the IRI language of structured data definition and manipulation, has long had the ability to draw replacement values from external set files containing two or more columns of values.
Abstract Syntax Notation One (ASN.1) is a language for describing the content and encoding of message data exchanged between computers (particularly in the telco industry).
This article is a continuation of the previous article, which introduced IRI support for XLS and XLSX file formats in the SortCL program used for moving and manipulating spreadsheet data in both the IRI Voracity data management (ETL, data quality, etc.)
In addition to all the other structured data sources that IRI software already supports, it is now possible to read and process data from XLS and XLSX files in the SortCL program central to:
IRI CoSort, for fast sorting, transformation, and reporting IRI NextForm, for mapping, migration, and replication IRI RowGen, for random selection or generation of realistic test data IRI FieldShield, for sensitive data masking IRI Voracity, for all the above, plus ETL, data cleansing, and wrangling for analyticsand to write the resulting data back to those or other targets, including one or more sheets.
The names of IRI software products and how they run have at times been a source of mystery, or even confusion, to the uninitiated. This article spells out the pieces and clarifies their interplay, providing a quick primer for prospective users, partners, and new industry analysts.
Automating where you can, when you can, is the basis of DevOps automation. Allowing IRI job script executions which mask or synthesize test data to be handled in the CI/CD pipeline supports this pursuit.