Perfect tool for Devsecops

Item: GitLab
Rating: 8
Author: Verified User

Overall Satisfaction with GitLab

Use Cases and Deployment Scope

We are using GitLab primarily to implement a DevSecOps dataops-like solution, so it is a main orchestration plus code repository that links all other tools to help create data products that are published in a shoppable marketplace.We have linked DBT, Monte Carlo, Talend, Snowflake, Collibra, collibra all with GitLab to allow us to create data products.

Pros and Cons

Pros

Yml files handling
GitLab CI pipeline orchestration
Integration with other tools like talend , snowflake , dbt, Monte carlo
Code review

Cons

GitLab ci debugging is difficult in case of failures
When pipeline grows using extends , references , includes make yml files very hard to read
GitLab is not yet smooth to use for project management with the issues in GitLab.

Return on Investment

Devsecops implementation is quite easy
Dataops implementation is also very straightforward
We are able to do weekly releases which were earlier once a month.
Developers velocity has increased.

Usability

GitLab is a good to tool to implement devsecops, dataops , gitops kind of solutions. Quick deployment cycles for release , we have 90-day planning, and due to GitLab, we are able to do 4x more deployments compared to before. GitLab has increased developer productivity as they are able to manage most of the things in GitLab itself.

Alternatives Considered

GitHub and Apache Airflow

It is easy to use , devsecops/ dataops / gitops implementation is quite easy and straightforward compared to other tools which we have tested , it provides seamless integration with tools like dbt , talend , snowflake , collibra and python. Merge issues as well as pipeline failures are easy to debug within GitLab coming in other tools and sending notification to chat boxes is quite convenient.

Security

We are using GitLab's ultimate plan which provides us ultimate security dashboard which provides cross project high level view of entire organization risk. It provides dynamic security testing which test code while it is actually running and when deploying and also when merging feature branches into dev /sat/prod it is quite easy to catch errors and debug in case of multiple errors.

Continuous Integration

With the use of GitLab, it has helped us in making sure each piece of code is tested in a clean, identical environment each time a merge request is made automatic trigger of the release CI happens, which checks for any issues. GitLab CI enables reviews on each merge which enables all developers to see changes and interact with them in a live environment which turns code reviews into collaboration.

Key Insights

Do you think GitLab delivers good value for the price?

Yes

Are you happy with GitLab's feature set?

Yes

Did GitLab live up to sales and marketing promises?

Yes

Did implementation of GitLab go as expected?

Yes

Would you buy GitLab again?

Yes

Other Software Used

Monte Carlo

Likelihood to Recommend

We have developed a dataops solution making GitLab central to host all the code and connections between different tools while making use of GitLab-CI.yml to orchestrate the flow. While all these are nice for implementation, we have found an issue while managing the entire project via GitLab, as there is still a disconnect between project issues and project management in GitLab, so we have to use Jira for that.

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