Implementing Enterprise DevOps Solutions

Our work involved two phases.

Phase 1: Developing an API Solution 

While our client had a lot to accomplish, they first engaged us for a smaller portion of the project. During this phase, we learned more about the root issues our client needed to address.

We collaborated with our client’s architects for context on their infrastructure. We built an API solution using an Azure Function App to do validation for ServiceNow. Then we used Azure DevOps pipelines, Terraform, and Ansible jobs to create infrastructure in our client’s new cloud provider of choice, Google Cloud Platform (GCP).

Phase 2: Standardizing Development Patterns

After we successfully delivered the first engagement result, our client extended our scope to include bigger pieces of the project. 

We went on to build and deploy several standardized development platform patterns. By packaging practices such as security by design, automated audit, and documentation-as-code with cost-effective architectures, our client could offer ready-built platforms for solution teams to start from.

Our team built two initial patterns that paved the way for the enterprise DevOps project:

• Static web app pattern – We built and deployed a static web app pattern from top to bottom. We learned the steps involved in their environment spin-up process, then automated those steps. With automation in place, we tested and confirmed the new standard development practices were working as expected for this pattern.
• Web app pattern – We used Terraform to deploy a web app pattern across multiple cloud providers: GCP for core web services and Azure Active Directory (AAD) for single sign-on (SSO).

We collaborated with other developers and architects, both from the client and from other vendors, on the patterns they built, including:

• VM pattern — We recommended increasing VM configurability, which eliminated the need for users to write code and saved them time. We also built an integration between GitHub Actions Pipeline and Ansible Tower to automate VM hardening.
• Data pattern — We reviewed and tested the data pattern to ensure compliance and consistency with other patterns.
• Complex web pattern using microservices — We collaborated to deploy an example web application to Cloud Run for Anthos.