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WORKING FROM HOME BEST PRACTICES FOR DEVOPS & QA TEAMS

Written by Gil Chaouat | Aug 4, 2022 1:57:00 PM

During these hectic days, constant changes and the Coronavirus world-wide situation we need to adjust quickly to the challenges we are facing.

Working From Home (WFH) is a fact now and as such, requires adjustments to keep executing our software Developing, Quality Assurance and Testing jobs.

One of the common solution is to build Dev/Test/QA environments quickly and securely on Public Clouds and allow professional teams to continue work as they were in their own organization network, connected to their organization data center.

Two main challenges evolved, both are resolvable with TDM (Test Data Management) basic practices – (1) Masking and (2) Sub-setting.

Challenge 1 – Protect production sensitive data

Masking data allows the organization to extract data from Production into Dev/Test/QA environments without any danger of exposing the sensitive data on a less secured environments. Masking data on-the-fly do the actual masking at the time of “select” of the data, in such way that data in transit as also masked, and all data leaving the organization network and going to the public cloud is protected. Masking methods are vary as per the organization decision – Substitution, Shuffling, Tokenization and more.

Challenge 2 – Avoiding massive storage, disk space and long data-load on the cloud

The ability to copy only the data needed for the Tes/Dev/QA environment becomes critical when moving into the cloud – not only for reasons of the cost of storage and disks but also for the time it takes to upload data into the cloud (sometimes x100 SLOWER then what we are used to inside out organization network).

Sub-setting means selecting only the data needed for the current development task or a specific test; a common solution and also the easiest one to solve this challenge. Choosing only the data needed, between range of dates, specific tables, specific user, specific rows, etc., allows a quick and easy build of the right dataset to the right environment.