Test Data Management: Test Data Sharing & Persistence (Step…
This article is the last of a 4-step series introduced here.
Step 4: Test Data Sharing & Persistence
Being able to modify, deploy, store, and re-use test data is important. Read More
Test Data Management: Test Data Generation & Provisioning (Step…
This article is part of a 4-step series introduced here. Navigation between articles is below.
Step 3: Test Data Generation & ProvisioningIn prior steps outlined in this series, you have determined the purpose and properties of the data, and who will produce and consume it. Read More
Test Data Management: Test Data Needs Assessment (Step 2…
This article is part of a 4-step series introduced here. Navigation between articles is below.
Step 2: Test Data Needs Assessment
Once the questions of who needs test data for what — and who will be dealing with it along its lifecycle are answered (see Step 1) — a deeper dive is needed into the specific technical aspects of the data itself. Read More
Test Data Management: Goal Setting & Team Building (Step…
This article is part of a 4-step series introduced here. Navigation between articles is below.
Step 1: Goal Setting & Team BuildingSomeone needs test data to do something, like:
stress-testing the functions and performance of applications prototyping database load/query and DW ETL/ELT operations benchmarking prospective new hardware or software outsourcing development or proofs of concept demonstrating systems with real-looking, but not real, sample dataIn all these cases, the most realistic data possible is needed, but it should also be safe and de-personalized. Read More
Test Data Management: A Primer
Welcome to IRI’s primer on test data management. This is the opening article, which is followed by a 4-step series.
IntroductionAs anyone familiar with the challenges of healthcare.gov Read More
Generating Test NID Data: Korean Social Security Numbers
This article discusses the generation of computationally valid social security numbers for the purposes of testing applications specific to Korean business interests. If you are interested in US social security number test data generation, see this article. Read More
Data Profiling: Discovering Data Details
Data profiling, or data discovery, refers to the process of obtaining information from, and descriptive statistics about, various sources of data. The purpose of data profiling is to get a better understanding of the content of data, as well as its structure, relationships, and current levels of accuracy and integrity. Read More
Pairs Testing in RowGen v3 – Valid/Joined Pairs and…
Database and solution architects depend on realistic test data to:
help create new databases, prototype ETL jobs or applications benchmark performance in new or existing platforms stress-test systems protect confidential information in existing systems if database work is outsourced or used for demonstrations. Read More
Making Test Data Realistic – Without Taking It from…
The increasing sophistication of software applications and the expanding role of database testers require high volumes of high quality, realistic test data that can faithfully represent existing, and stress-test new, platforms. Read More
Weighted Distribution of Test Data Values in RowGen
Realistic test data has a number of advantages over real data for anyone creating or changing a database, prototyping ETL operations, or testing applications. First, synthetic data do not expose personally identifiable information (PII) like credit card, social security numbers, birth dates, etc. Read More
Introducing the RowGen Test File Wizard
A test data generator is an important part of the setup process for DevOps and data architects prototyping database and data warehouse operations, testing applications, benchmarking different platforms, and outsourcing work formats. Read More