Business Intelligence (BI)
Before you can make use of the data you have and trust its value for, analytic, testing, and other production jobs, you have to know enough about that data.
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Big Data
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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Business Intelligence (BI)
Oracle Data Visualization Desktop, Oracle DV or DVD for short, is business intelligence (BI) software that can organize, aggregate, and visualize data for informational outcomes. What seems to make this BI software unique is its ability to create slideshow presentations from its different visualizations.
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Big Data
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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Data Transformation
The Schema Data Class Search wizard in IRI Workbench (WB) can be used to match data in entire schemas to configured data classes. The search process compares the matchers in the data classes with the data to determine the best match, if any.
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Business Intelligence (BI)
Power BI is a business analytics and data visualization package from Microsoft that can provide custom-designed dashboards and reports ready for web or mobile display. Like other BI and analytic tools, Power BI can also perform simple data wrangling jobs like sorting and aggregation before and after the data are displayed.
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ETL
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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Data Masking/Protection
“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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Data Masking/Protection
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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Data Masking/Protection
Adding ‘random noise’ to data through blurring or perturbation is a data common anonymization requirement for researchers and marketers of protected health information (PHI) seeking to comply with the HIPAA Expert Determination Method security rule.
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