In the 60s, automobiles manufactured in Japan consistently beats their competitors in American market. Many refers to the lean manufacturing methodology in the automation as the secret sauce. The software industries borrowed a lot of similar methodologies from TPS (Toyota Production System) into software development industry, which brought about agile software development.
For software to deliver value, it is not just about developing software in agile methodologies. A full SDLC (software development life cycle) includes build, release and upgrades too, some of which are managed in a different department in the organization. DevOps extends agile methodology across departments. DevOps is ultimately about culture but it is the tooling configuration that enables that.
The power horse of the DevOps tooling is automation pipeline (e.g. Jenkins, Azure DevOps, GitHub). These pipelines expedites iterations with frequent feedback about software quality, whether it is common conventional SDLC workflow or more recent infrastructure as code worklfow. For SDLC, the goal is to establish continuous integration (CI) and ultimately continuous deployment (CD).
With serverless deployment, the operation of managing computing resources is abstracted away. Serverless deployment models further simplifies SDLCs and are ideal for some common use cases such as API services, IoT, scheduled and event-driven tasks.
Another creative use of automation pipelines is the data pipelines. Data engineering tasks includes ingestion, ETL, integration, and storage and automation pipelines are ideal automation tools for these tasks.
Observability setup enables instant feedback, an important construct of DevOps. An observability stack consists of metrics collection, log shipping, performance monitoring, request tracing and visualization etc.
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