As more computing activity makes its way into the cloud, so does storage. This is only logical given the need for proximity to data that cloud systems would need for performance in the same way on-premise machines should have data stored on or near them for faster processing.
In part 4 of this five-part series regarding newly added ASN.1 support in the SortCL program central to the IRI Voracity data management platform and its component structured data processing products — IRI CoSort, NextForm, FieldShield, and RowGen — we take a look specifically at their common graphical user interface (GUI) for job design, IRI Workbench.
In the previous article, new support for SortCL-compatible jobs in the IRI Voracity data processing ecosystem for ASN.1-encoded data files was introduced. This article takes a more in-depth look through sample jobs demonstrating various use cases, ASN.1 encoding rules, schema files, and the Protocol Data Units (PDUs) involved with data defined in ASN.1 schemas.
Abstract: Now that XLS and XLSX files are now also directly supported in IRI FieldShield by virtue of their new /PROCESS (format) support in the CoSort 10.5 SortCL program, IRI Voracity data management platform and standalone data masking users have three different ways to “shield” sensitive data in their spreadsheets.
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.
One of the data masking requirements for IRI FieldShield that we see in PHI anonymization use cases involves the blurring of dates at the row level, instead of the column level while providing a configurable option to retain the interval between those dates.
This article explains how to connect with, and use data from, SharePoint sites — using the file system via OneDrive — for operations in IRI Workbench-supported data management software.
Incremental data replication, masking, integration (ETL), and other data refresh operations are common in frequently updating database environments. These jobs require the detection of additions and updates to tables.