MSSQL.DataMask
What Does Activation Key Mean?
An activation key is a code to register or activate a software application. It usually consists of letters and numbers with a dotted fundamental movement between sections. Newer models of software products eliminate the activation key as an authorization mechanism. With the development of cloud computing services, many types of software have been purchased online and used online on a subscription basis. It eliminates the need to use activation keys to authenticate users. An activation key is a by-product of the system where the user purchases the code and execution software for the application and downloads all of the code to their computer or device. New methods are rapidly replacing the traditional form of licensing.
Do you need rich test data to develop, test, or outsource your project? If so, you’ve probably tried either generating from scratch, or cloning from production data. Auto-generating the test data is difficult and error prone for all but the simplest databases due to complex variances, frequencies, and data interdependencies. Business and legal obligations such as HIPAA require that production data clones be thoroughly sanitized (masked) of personal indentifiable information (PII) and/or protected health information (PHI). MSSQL.DataMask is a free, simple tool that quickly sanitizes a clone of your production database into a safe, secure test database. Once built, the process is easily repeatable to refresh your test data from production.
You can either load and re-run a set of data masks from the application, or generate a fully documented tSQL script to modify, run, or schedule as your needs dictate. Why mask your test data? Ã??? Creating test data is more complex and doesn’t always represent actual business scenarios versus masking (anonymizing) production data Ã??? Most data breaches come from within an organization; thus, protecting your dev and test data is critically important. There are three categories of datamasks: 1. Scrub: Overwrite all rows of a column with the “same value” 2. Substitute: Overwrite all rows of a column with a new “unique value” 3. Transform: Overwrite all rows of a column with an “obsfucated original value”.