Top 5 DevOps Trends 2020-2021

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I would like to share in this post, the list of the Top 5 DevOps Trends for 2020 and that will continue to be the focus for 2021.

1. Cloud native environments

According to the CNCF Survey, the use of cloud native technologies in production has grown over 200%, since December 2017.

Cloud native is not about microservices or infrastructure as code. Microservices enable faster development cycles on smaller distinct functions, but monolithic applications can have the same features that enable them to be managed effectively by software and can also benefit from cloud native infrastructure.

CLOUD NATIVE DEFINITION

The definition of a cloud native application is still evolving. There are other definitions available from organizations like the CNCF.

Cloud native applications acquire these traits through various methods. It can often depend on where your applications run5 and the processes and culture of the business. The following are common ways to implement the desired characteristics of a cloud native application:

Microservices
Health reporting
Telemetry data
Resiliency
Declarative, not reactive

Source of Definitions on this section

2. Observability

In control theory, observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. The observability and controllability of a linear system are mathematical duals. The concept of observability was introduced by Hungarian-American engineer Rudolf E. Kálmán for linear dynamic systems. A dynamical system designed to estimate the state of a system from measurements of the outputs is called a state observer or simply an observer for that system.

On our next articles, we will be covering some important tools that can be used to enable your team / company to have and use observability platforms such as Grafana/Prometheus and Elasticsearch and Kibana.

3. Site Reliability Engineering - SRE

Practice supported by skilled engineers, to establish and adhere to availability targets, service level objectives (SLOs), and error budgets set by end users. SREs have the ability to modify code to ensure adherence, and have basic telemetry capability to monitor where and when to modify applications. SRE implements DevOps practices.

You can see more about SRE ih this article.

4. Kubernetes

Kubernetes (commonly stylized as k8s) is an open-source container-orchestration system for automating computer application deployment, scaling, and management.

You can see more about Kubernetes in this page.

5. Automation

Zero touch in the things is the new normal.

Automation is the part of the DevOps culture, and every team should seek the opportunities that automation has to speed up the development and deployment processes.

The role of automation extends to the following key tasks of the DevOps SDLC pipeline:

Code Development: Automation in applications such as source control allow developers to keep on top of the DevOps SDLC pipeline. For instance, defining certain changes to the build and triggering appropriate changes or process activities save time and simplify the development of large, complex software projects.
Visibility: Ops teams can keep on top of the code changes, existing issues and the resulting impact on project goals by automating traceability and the issue tracking processes. Devs and QA need to collaborate internally and across teams to ensure that the SDLC process runs smoothly. For instance, if a security team member identifies code issues weeks after it was submitted, Devs already have a challenge at their hand to fix the root cause without delaying or wasting the progress made since. A tight feedback loop between Devs, QA and Ops is required to eliminate silos and bottlenecks between the teams.
Continuous Testing: Automation is critical to support, execute and manage continuous testing in DevOps environments. Since testing in DevOps frameworks is performed on a continuous basis during the SDLC pipeline, Devs and QA must also manage the requirements of a continuous testing environment. These range from triggering automated communications between systems and team members, integrating multiple tests, tracking and predicting issues, and facilitating continuous integration of approved automated builds.
Enabling CI/CD: Automation in the Continuous Integration and Continuous Delivery pipeline ensures that appropriate software builds, data, tests and code changes are delivered to appropriate target environments. DevOps teams can therefore perform frequent code changes, stage the builds for testing and ultimately push frequent software changes to the market.
Monitoring and Incident Management: The log and metrics big data cosmos can be overwhelming for organizations with infrastructure of all sizes and scale. Instead of swimming through the flood of alerts, a high-level incident reporting is required to make sense of infrastructure performance and potential issues. Automation therefore becomes necessary to intelligently prioritize events, identify root cause and deliver proactive actionable intelligence. To enable true DevOps, IT Ops might often need to replace antiquated IT service assurance strategies with automation capabilities and software-defined IT Ops, among other automation strategies.

Source: https://www.bmc.com/blogs/automation-in-devops/

References

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Published on Jul 03, 2020 by Vinicius Moll

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Top 5 Devops Trends 2020-2021