Joining Flat-File & RDB Data: Textual ETL (Part 2)
IntroductionThis article demonstrates the Voracity user’s ability to join values in a flat-file file to those in an RDB (Relational Database) table to provide meaningful information. Read More
This article demonstrates the Voracity user’s ability to join values in a flat-file file to those in an RDB (Relational Database) table to provide meaningful information. Read More
To configure both JDBC and ODBC to support the Transport Layer Security (TLS) protocol, there are additional configuration steps that are needed when compared to this previous article. Read More
Corporations and government agencies store a lot of useful information in non-transactional semi-structured and unstructured data sources. Finding that data – in documents, logs, and images – is important not only for data masking, but also for textual ETL. Read More
Three years after the release of CoSort V10, a significant interim update to IRI’s primary data transformation software package has been announced. This article summarizes what’s new in the CoSort high-performance data sorting and ETL tool since Version 10.0.1 was introduced. Read More
An operational data store (or “ODS”) is another paradigm for integrating enterprise data that is relatively simpler than a data warehouse (DW). Read More
“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
“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
A dimension is a structure that categorizes a collection of information so that meaningful answers to questions regarding that information may be obtained. Dimensions in data management and data warehouses contain relatively static data; however, this dimensional data can change slowly over time and at unpredictable intervals. Read More
Dimensional data that change slowly or unpredictably are captured in Slowly Changing Dimensions (SCD) analyses. In a data warehouse environment, a dimension table has a primary key that uniquely identifies each record and other pieces of information that are known as the dimensional data. Read More
Dimensional data that change slowly or unpredictably is captured in Slowly Changing Dimensions (SCD) analyses. In a data warehouse environment, a dimension table has a primary key that uniquely identifies each record and other pieces of information that are known as the dimensional data. Read More