Static & Dynamic Data Masking in FieldShield
Usually static data masking is performed on production data at rest so it is stored safely, or when replicated to non-production environments for testing or development purposes.
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Automatically Forward Target or Log Data into Splunk
Production or test data targets, as well as the operational log data, created by SortCL-compatible data manipulation or generation jobs in the IRI Voracity platform and its included products like — IRI CoSort, NextForm, RowGen, FieldShield and DarkShield — are all machine-readable.
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Table Filtering in IRI Workbench
IRI Workbench provides a number of features for working across multiple tables in a database. It includes wizards to: profile databases; classify columns; subset, mask and migrate data; generate test data; etc.
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Processing Live Data Feeds in Voracity
This article demonstrates processing a web-based data source in the IRI Voracity data management platform. Static and streaming data defined in URLs — including FTP/S, HTTP/S, HDFS, Kafka, MQTT, and MongoDB — are supported by the default data processing engine in Voracity, CoSort Version 10.
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Browsing the Operational Data Store (ODS)
What Is an ODS?
An operational data store (or “ODS”) is another paradigm for integrating enterprise data that is relatively simpler than a data warehouse (DW).
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Connecting Snowflake DB & IRI Workbench
Connecting to and working with data in an Snowflake AWS database from IRI Workbench (WB) is no different than with an on-premise SQL-compatible source. You browse Snowflake tables and exchange metadata in Workbench via JDBC.
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JSON and the Truth it Captures
“Have you stopped speeding?” You could probably object to a leading question like this in court, but what happens when an important question with only a yes or no answer is solicited on a mandatory form, and the response becomes part of an actionable database record?
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Anonymizing Indirect Identifiers to Lower Re-ID Risk
Quasi-identifiers, or indirect identifiers, are personal attributes that are true about, but not necessarily unique, to an individual. Examples are one’s age or date of birth, race, salary, educational attainment, occupation, marital status and zip code.
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Production Analytic Platform #4/4: Unifying the Worlds of Information…
This is part 4 of a 4-part series on Production Analytics. Processing on Par with Information [Part 1] Data Processing Drives Efficiency [Part 2] Processing Real World Data [Part 3]
In this final article of the series covering the Production Analytic Platform paradigm, we look at data virtualization—a key requirement in today’s multi-source, data-overloaded world.
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Production Analytic Platform #3/4: Processing Real World Data
This is part 3 of a 4-part series on Production Analytics. Processing on Par with Information [Part 1] Data Processing Drives Efficiency [Part 2] Unifying the Worlds of Information and Processing [Part 4]
The inclusion of full function data processing in the Production Analytic Platform simplifies the task of gathering data from external sources such as the Internet of Things and clickstream data that requires both intensive exploratory modeling as well as high-speed application and maintenance of those models on real-time and streaming data.
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Production Analytic Platform #1/4: Process on Par with Information
This is the first of a four-part series of blog articles examining the inherent tradeoffs between data processing and information storage and presentation within traditional ETL paradigms — from the ODS to the data lake.
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