![]() ![]() The most frequent cause of failures (but still relatively rare) in a highly collaborative versioning model such as Git is a scenario where individually feature A runs successfully and feature B runs successfully, but feature A and feature B together fail.Ī typical example of this cause of failure is as follows: Once tests run successfully in QA, create a merge request from QA to production.Once happy, create a merge request (MR) from feature-branch-name back to QA.Make and test changes on the feature-branch-name.Cut a feature branch ( feature-branch-name) from QA.The QA development workflow is as follows: It would be best if you treated this environment as the production environment, and any bugs found in QA should be equivalent to allowing bugs through to production. QA is not the place to find elementary bugs in code. In other words, by merging to QA, developers or engineers are confident that, from their perspective, the code is ready to be merged into production. The primary purpose of this environment is to ensure that if something fails in production, it should rather fail in QA before it hits production. The QA branch and environment Ī QA (or quality assurance) branch and environment is a virtually perfect production replica. This section dives into the details of DataOps environments, including using branches. ![]() Note that only Maintainers can commit to protected branches. Should be used for all individual changesĭetails about the variables for environments are found in Project Settings, Project Variables. Optional, basic level of maturity required In a mature environment, you will find other environments beyond your feature branch and production: Name This is precisely what you get with branching. ![]() When branching your DataOps repository, you are, in effect, creating a sandbox where you can edit to your heart's content without the possibility of disrupting anyone else. Once happy, create a merge request (MR) from feature-branch-name back to productionīecause this process is all about data, the ability to create a feature branch data warehouse is one of the most powerful features of the DataOps platform.Make and test your changes on this feature branch ( feature-branch-name).Cut a new feature branch ( feature-branch-name) from the production branch (e.g., your Jira ticket).In its simplest form, the Git development workflow looks like this: The standard pipeline -ci.yml files included in all template DataOps projects are based on this Git model and will need tweaking if a different model is used. As a result, we have adopted a standard and well-accepted Git model that has proven to work well for DataOps. Moreover, the differences between DevOps and DataOps mean that some of the typical DevOps approaches don't work quite as well for DataOps. However, most data teams do not yet have a set of Git best practices. Therefore, if your company has already instituted Git usage best practices, use them. You can read up on these different approaches in DataOps Environments. It contains many different ways of handling branches and merging and releasing these branches, among other functions. One of the standouts of Git as a versioning control system is its environment management capabilities. Therefore, let's dive into environment management and git branching strategies, starting with branching strategy concepts. This could involve the duplication of TB of data. ![]() A fast way of making a new environment look just like production. Great environment management for #TrueDataOps requires all environments to be built, changed, and (where relevant) destroyed automatically. The #TrueDataOps website describes environment management within the DataOps context as follows:Įnvironment management is one of the most complex elements of #TrueDataOps. As endorsed in the fourth #TrueDataOps pillar, environment management forms an integral or foundational part of the DataOps platform, and branching strategies are a big part of this function. ![]()
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