Big Data
An operational data store (or “ODS”) is another paradigm for integrating enterprise data that is relatively simpler than a data warehouse (DW). Read More
Big Data
“That’s all you need in life, a little place for your stuff. That’s all your house is, a place to keep your stuff. If you didn’t have so much stuff, you wouldn’t need a house. Read More
Big Data
“Progress is impossible without change, and those who cannot change their minds cannot change anything.”
-George Bernard Shaw
The mathematical symbol for change is the Greek uppercase letter delta: Δ. Read More
Business Intelligence (BI)
Introduction: As with multiple BI platforms discussed throughout this section of the IRI blog site, this article analyzes the relative data preparation performance (and benefit) of using its external data wrangling engine – via the IRI CoSort product or Voracity platform — with QlikView when ‘big data’ sources are involved. Read More
Big Data
Introduction: As with multiple BI platforms discussed throughout this section of the IRI blog site, this article analyzes the relative data preparation performance (and benefit) of IRI CoSort with Tibco Spotfire when ‘big data’ sources are involved. Read More
Big Data
Introduction
The ability to directly move very large database (VLDB) data from/to, and manipulate it within IRI software is essential for those requiring:
data integration (ETL) database acceleration (unloads, loads, reorgs, queries) database migration data masking (encryption, redaction, de-identification, etc.) Read More
Data Transformation
Editors note: CoSort Version 10 was released in mid-2018. Please see this article for links to its features and upgrade details.
What’s New in CoSort 9.5.3
Along with the new website, there is a new release of IRI’s flagship CoSort package for data management and data protection. Read More
Data Transformation
Full disclosure: As this article is authored by an ETL-centric company with its strong suit in manipulating big data outside of databases, what follows will not seem objective to many. Read More