What is DevOps Maturity Model?
While some people assume DevOps as having a continuous integration, continuous delivery
pipeline, and infrastructure in place, they overlook its crucial part. DevOps is much
broader and is not restricted only to the technical aspects. To implement a successful
DevOps strategy typically teams struggle with these things that are not technical at
all:
- Assessment of the processes that need to be automated to remove hassles and manual
processes.
- Come up with a proper release management process.
- Encourage teams and managers to adopt a product-centered mindset.
- Building multidisciplinary teams that can take responsibility for their products.
- Improve collaboration between the teams.
- Achieve continuous integration and continuous delivery into reality.
And here comes the DevOps Maturity Model. It is a conceptual model
which measures:
where does an organization stand? Which direction is it heading into (forward or
backward)? And what to aim next.
Areas That Define DevOps Maturity:
Every organization is different and so is its need and goals. And what defines a DevOps
maturity for one can be a minute thing for another. That’s why you need to look at all
the areas that define DevOps maturity. Here is a list of areas that you need to look at:
- Culture & Organization: The important aspect of the DevOps
is to bring a cultural shift in the organization. So it is important to understand
that DevOps should be considered a
cultural
practice rather than a technological one. To get the most out of it, you need to
establish deep
collaboration across different teams and embrace the mentality of rapid failure.
Most
importantly, once you decide to switch to it, you should have confirmation from all
the
stakeholders to proceed further.
- Incorporation of Continuous Integration and Continuous Delivery Pipelines:
The goal of CI/CD is
to deliver better quality software by monitoring issues before they appear
through
testing. This
becomes possible as you develop the ability to identify defects and call quality
issues on
minute changes earlier in the process. This process helps you shorten the
feedback
loop between
the end-users and the development team. Plus, it allows developers to commit
changes
more often
while still managing the stability.
some people think that CI/CD
is
helpful for agile only, yet it is the backbone of the
DevOps initiative as well. Be it agile or DevOps, more layers of manual effort
can
bring down
the success rate of the development to the ground. Hence, it is the best
practice to
automate
the build and testing process and find bugs early without putting your precious
time
into manual
activities. To achieve this process, you also need to build a pipeline that
should
be scalable
over time so that new features and requirements in the automated build processes
can
be added
transparently and quickly.
- Rate of Application Deployment: Nobody wants to see a
lapse in
the time where something has been
built and users are unable to use it. By making small changes in production, we can
decrease
volatility and hesitancy. If you can reduce the cognitive load of shipping to a
minimum, you can
help development teams to achieve efficiency in terms of deployment. It will be a
small thing to
digest and the customer will be able to use that much sooner.
- Efficient Build Process: A good build process includes
artifacts,
logs, and status. It
gets
triggered automatically with each code commit and the history is available for the
team to
review what has happened over the last execution. Whenever a problem is found like a
fault in
the test or the security issues in the build phase, a good build process would mark
this as
failed because the code is analyzed at every stage. Each artifact is tagged and
versioned so
that the build can be traced across the pipeline. Every metric is collected and
further analyzed
so that the entire process can be improved.
- Deployment: Deployment pipelines exist to deploy to all
environments using the same standard
process. Regardless if it is production or not, there is no need for manual tasks,
which makes
the process difficult. There is no human effort required for deployment, and they
are executed
continuously. Releases are disconnected from deployment and features can be done
using flags or
configuration values. No downtime is required whenever getting a new version to
production.
Once the application health is measured on
different levels, it is
expected to ensure that
everything is working fine. If a problem is detected in production, the
deployment
process is
used to rapidly roll forward the fixes, without the need of rolling back
previous
changes nor
making manual changes as each deployment is immutable and there are even
self-healing tools in
place.
- Code Management: To ensure rapid release, there should be
no or
minimal branching - a process
that lets developers run the experiment with varied versions of the underlying
source code in
source control. As more experimentation alters the code submitted by other
developers. And no
feature branch should stay longer than a day. The team performs frequent commits
multiple times
a day. All changes related to the application are stored in version control, which
includes
infrastructure, configuration, and database.
- Data Management: To ensure repeatability and control,
database
changes are done through code
migrations or scripts stored in version control fully automated, versioned, and
performed as
part of the deployment process.
- Continuous Testing: Many organizations are now releasing
code to
production weekly, daily, or
even hourly. Hence, testing and maintenance need to be performed much more quickly
to maintain
the desired output. Continuous testing has evolved out of this need. Continuous
testing is a
type of software testing characterized by the combination of testing early, testing
often,
testing everywhere.
By using automation, it addresses business
risks associated
with a software risk early.
Continuous testing improves code quality, accelerates time-to-market, offers a
continuous
feedback mechanism, and eliminates the disconnect between development, testing,
and
operations.
- Architecture & Design: Application architecture is one of
the main
factors that enable or
prevent a company from achieving a rapid release through DevOps. If the system is
not designed
to be tested quickly, easily, and frequently, you’ll end up with a bottleneck that
won’t help
you. The same goes for deployment.
Therefore, it is critical to
focus on the key
non-functional requirements associated with the
benefits that you want to achieve, like modularity, testability, agility, etc.
Different
architecture styles can support these goals, such as microservices,
service-oriented
or
event-driven architectures. The challenge is to choose the right one that fits
your
needs and
align it with other infrastructure and development technologies.
What Are Various DevOps Maturity Levels?
Whether you’re a pro or a beginner in your DevOps journey, there is a specific level
that not only defines your progress but also encourages you to move to the next level.
If you want to know your current rank in this journey, please take a look at the
following DevOps maturity levels:
- Initial Level: At the initial stages, the organization
might not
be aware of DevOps or its
potential benefits. Hence, the organization has to start from the very beginning.
Organizations
on this level follow a waterfall project management - long approval and change
processes, teams
structured around a skill for their IT projects. They plan and design everything
up-front before
the development teams start coding, and when all is done, separate teams deliver the
application
to production. Tests start very late in the process. It is very likely they do not
practice the
concepts of shift-left security.
Operation is a separate team
that waits for
developers to hand over their applications with a
thick manual of instructions on how to deploy them. The handover of the new
version
and
associated information about deployment is characterized by missing information,
miscommunication, and finger-pointing when things go wrong. For some
organizations,
an initial
DevOps level can be overwhelming. But they can also benefit the most if someone
from
the
leadership decides to take action to solve the problem.
- The Repeatable Level: At this level, organizations know the
core
principles of DevOps and apply
them according to their daily jobs. Environments and their configurations are
versioned and can
be set up consistently. They are on the right way to facilitate the collaboration
between
development and operations. Changes do not come as a surprise but are well
communicated.
Organizations operating at this level are not just
“reactive” to
all that comes across their
path. They are proactive and work their way towards repeatable processes for the
areas they
understand well. However, teams tend to ship rather big features that are
difficult
to manage
and test. Breaking bigger services into smaller microservices remains a
challenge.
Operations
teams need to manually intervene when things in production go wrong.
- Defined Level: A key characteristic of this level is
consistency
across areas and topics.
Processes are repeatable but also standardized. For example, database changes are
performed
automatically with every release, non-production deployments are rolled out
automatically, and
monitoring is integrated with every application. Integration tests are executed
automatically
and act as quality gates for any later stage in the delivery pipeline.
Teams are
organized around projects or products and not around skill-sets. Development
teams
work towards the execution of clear requirements that deliver clear business
value.
All
processes are communicated clearly to all people involved, and documentation and
release notes
are created automatically.
- Managed Level: At the managed level, all of the
environments are
managed effectively. Database
changes and rollbacks are tested with every iteration of the product itself. The
delivery
process is predictable and runs frequently. Therefore, stakeholders know what and
when to
expect. Applications are actively monitored in production, and metrics are gathered.
Teams know how to incorporate feedback for their next iteration. The
organization
uses a
knowledge management tool to capture existing knowledge and bring more knowledge
to
the teams.
Mentors coach the teams to push them forward. Culture does not remain a
bottleneck
and welcomes
change to achieve organizational goals.
- Optimized Level: At the optimized level, processes are
fully
automated and testing is done in
production. They know how to deal with problems like overloaded systems. The system
itself will
scale or adjust to peak requirements. It also adjusts to potential problems, like
network
interruptions or other infrastructure failures.
Conclusion:
DevOps maturity model defines the journey of an organization that is either planning or
already using DevOps. Once the organization gets the idea of ground reality, things
become easier for them as the DevOps maturity model clearly states a particular stage,
its goal, and strategy to move on to the next stage.
All businesses should assess their DevOps maturity level to successfully implement the
core principles of DevOps as early as possible.
