DevOps had a dream run in the year 2021 and is sure to continue it into 2022. According to ResearchandMarkets, the global DevOps market was estimated at $4.31 billion in 2020 and $5.11 billion in 2021. This value is expected to touch $12.21 billion in 2026, growing at a CAGR of 18.95% between 2021 and 2026.
DevOps is innovating at a rapid pace. As such, organizations should proactively monitor technology changes and reinvent IT strategies accordingly. Here are the top DevOps predictions for 2022.
1) Distributed Cloud Environments
After hybrid and multi-cloud environments, distributed cloud networks are rapidly gaining popularity in recent times. A distributed cloud environment hosts backend services on different cloud networks in different geolocations while offering a single pane to monitor and manage the entire infrastructure as single cloud deployment. It allows you to customize more-performing and responsive service delivery for specific apps while following regulations of local governments. Distributed clouds bring high resilience, prevent data losses and service disruptions as your apps keep running even when servers in one region crash. It means you gain 99.99% uptime. Edge computing can be considered as an extension to distributed cloud networks.
Distributed clouds offer amazing benefits to all industries. For instance, autonomous vehicles can monitor and process sensor data on-board while sending engine and traffic data to the central cloud. Similarly, OTT platforms can leverage ‘Intelligent Caching’ wherein content in multiple formats is cached at different CDNs while transcoding tasks are done at the central cloud. That way, a newly released popular series can be seamlessly streamed to multiple mobile devices in the same region in real-time.
2) Serverless Architecture
Serverless architecture is a cloud-native architectural pattern that enables organizations to build and run applications without worrying about the provisioning and management of server resources in the infrastructure. The cloud provider takes care of the allocation and management of server and machine resources on-demand. The serverless architecture delivers accelerated innovation as apps can be deployed faster and better. Apps can be decomposed with clear observability as independent services that are event-based. As such, organizations can reduce costs and focus more on delivering better UX.
Serverless computing is rapidly innovating. Function as a Service (FaaS) is a new trend based on the serverless architecture that eliminates the need for complex infrastructure to deploy and execute micro-services apps. Another growing trend is hybrid and multi-cloud deployments that deliver enhanced productivity and are cost-effective. Serverless on Kubernetes is another trend that helps organizations run apps everywhere where Kubernetes runs. Kubernetes simplifies the job of developers and operations teams by delivering matured solutions powered by the serverless model. Serverless IoT is another model that brings high scalability, faster time to market while reducing overhead and operational costs in data-driven environments. It is also changing the way how data is secured in serverless environments.
DevSecOps is a DevOps pattern that converts security into a shared responsibility across the application product lifecycle. Earlier, security was handled by an isolated team at the final stage of product development. However, in today’s DevOps era wherein apps are deployed in smaller cycles, security cannot wait for the end any longer. As such, DevSecOps integrates security and compliance into the CI/CD pipeline, making it everyone’s responsibility. The year 2022 is going to see more focus on shifting security towards the left of the CI/CD pipeline.
DevSecOps increases automation and policy-driven security protocols as QA teams perform automated testing to ensure that non-compliance and security vulnerabilities are efficiently combated across the product lifecycle. The design for failure philosophy is going to be reinvented as well.
4) AIOps and MLOps
Today, regardless of the size and nature, every organization is generating huge volumes of data every day. As such, traditional analytics solutions are inefficient in processing this data in real-time. For this reason, artificial intelligence and machine learning algorithms have become mainstream in recent times.
AI and ML data scientists normally work outside version control systems. Now, CI/CD and automatic infrastructure provisioning are applied to AIOps and MLOps as well. It means you can version your algorithms and identify how changes evolve and affect the environment. In case of an error, you can simply revert to an earlier version.
5) Infrastructure as Code (IaC)
Infrastructure as Code is another growing trend that will become mainstream in 2022. Infrastructure as Code (IaC) is a method of managing the complete IT infrastructure via configuration files. Since cloud-native architecture is becoming increasingly popular in recent times, IaC enables organizations to easily automate provisioning and management of IT resources on a cloud-native architecture by defining the runtime infrastructure in machine-readable files. IaC brings consistency in setup and configuration, enhances productivity, minimizes human errors, and increases operational efficiencies while optimizing costs.
GitOps is the new entrant in this space. Leveraging the IaC pattern and Git version control system, GitOps enables you to easily manage the underlying infrastructure as well as Kubernetes instances. When combined, organizations can build self-service and developer-centric infrastructure that offers speed, consistency and traceability.
DevOps was introduced between 2007 and 2008 as a revolutionary methodology that would be disrupting the IT development landscape. While DevOps was successful in changing the way how IT thinks and operates, it took a long time for companies to adopt this methodology. The reason for this slow adoption was because businesses took time to understand how DevOps works. While organizations are aggressively embracing this methodology in recent times, there are still some misconceptions that are acting as a barrier to DevOps implementation. Here are the top 10 myths about DevOps development.
1) DevOps is a Tool
The most popular myth about DevOps is that it is a tool or a product which is not true. DevOps is not a tool or a technology that can be purchased or subscribed to. It is a methodology that integrates development, operations, QA and security teams into a cross-functional team to seamlessly collaborate and work on software development projects. The system uses tools, processes and people to achieve this. For instance, continuous integration is achieved using CI servers such as Bamboo, Jenkins, Gitlab etc. while Docker and Kubernetes help in automatic deployment and management of the infrastructure.
2) DevOps is for the Web
DevOps became popular with SaaS-based organizations such as Netflix and Etsy creating an impression that DevOps is only for web companies. While it is true that DevOps favours web platforms, it is equally effective for all types of modern software delivery. While continuous delivery helps web companies to always offer up-to-date software, the same applies to native and non-web software too.
3) DevOps means CI/CD
While DevOps helps organizations achieve continuous delivery by building CI/CD pipelines, it is only a part of the DevOps methodology. DevOps is not confined to CI/CD but deals with the organization more comprehensively. In addition to tools and processes, DevOps brings a cultural shift across the organization by creating cross-functional teams that seamlessly collaborate and communicate across the product lifecycle. To fully leverage DevOps, businesses should equally focus on people, tools and processes.
4) DevOps Solves all Problems
It is a common misconception that when you use DevOps to build continuous delivery pipelines and automate processes, the system will take care of everything without any issues. First of all, you need to design the right DevOps strategy with the right tools for the right processes that are managed by the right people. In addition, to automating processes, you need to set up continuous feedback loops, analyse metrics and constantly update things. DevOps is not a magic wand that automatically sets everything right.
5) DevOps is for Developers and Operations Teams
DevOps stands for Development and Operations. While it started off a system that integrated these two teams to collaboratively work on a software development project, it has greatly evolved now. Today, DevOps cross-functional teams include people from QA, security, administrators, data engineers, analytics engineers and business management. It is interesting to note that members from sales, marketing, and tech support and customer service are also being incorporated into cross-functional teams as and where applicable.
6) DevOps is the End of Operations Teams
In an automated DevOps environment, developers can automatically deploy software to production environments using tools such as Jenkins, TeamCity, Docker, Kubernetes etc. However, it doesn’t mean that it is the end of the road for operations professionals. Actually, Ops teams can take advantage of Infrastructure as Code (IaC) tools to efficiently manage the infrastructure via code. DevOps teams are accountable for the entire lifecycle of the product and Ops teams have an important role to play here.
7) Operations Professionals Should Learn Programming
With infrastructure as code taking the center stage of the infrastructure management landscape, there is a misconception that operations professionals should learn programming languages. While it is true that you need a basic idea of scripting, you don’t need to have expert knowledge of programming languages such as Java or C#. For instance, Ruby is a popular IaC language that is easy to learn. So, Ops teams with basic scripting knowledge can easily pick up this system.
8) DevOps is only for Non-Regulated Industries
Often, people think that DevOps doesn’t work well with highly regulated industries, owing to strict compliance and security policies. DevOps serves a great purpose for regulated industries as well. With DevOps, compliance becomes easy as you can store audit trails of all automated processes. It means business processes are always audit-ready.
9) DevOps is Cloud-based
Often, people believe that DevOps is always deployed in the cloud and some people use the term ‘cloud’ interchangeably with DevOps. DevOps indeed brings dynamic infrastructure resources into the picture. However, it doesn’t mean that DevOps always requires a cloud infrastructure. If you can dynamically test and deploy code, then DevOps works for you.
10) DevOps replaces Agile
A popular myth about DevOps is that it replaces agile which is not true. In fact, DevOps enables agile practices by incorporating continuous integration, continuous testing, continuous deployment and continuous monitoring. So, it actually complements agile software development.
The key to fully leveraging DevOps is choosing the right DevOps strategy. CloudTern is here to help!
DevOps started off as a methodology that integrates Developers and Operations teams to work in tandem in software development projects. It facilitates seamless coordination and communication between teams, reduces time from idea to market and significantly improves operational efficiencies while optimizing costs. Today, DevOps has rapidly evolved to include several other entities of IT systems. A new addition is Business intelligence.
DevOps jelled well with Big Data as both methodologies are contemporary and complement each other in managing of massive volumes of live data moving between development and production that is maintained relevant via seamless coordination between teams. When it comes to business intelligence, data warehousing and analytics are two important components that need to be managed. As BI deals with batches of data, it doesn’t easily integrate with the DevOps environment by default.
Managing Data Warehousing with DevOps
A data warehouse is a central data repository that collects data from various disparate data sources in and outside the organization and hosts them in a central location allowing authorized people and reporting and analytics tools to access it from any location. Managing a robust and sophisticated data warehouse is a challenge as multiple stakeholders are involved in making a change which makes deployments rather slow and time-consuming. Implementing DevOps here can be a revolutionary thing as you can combine data administration teams and data engineering teams to collaborate on data projects. While a data engineer informs potential features that are being introduced to the system, the data administrator can envisage production challenges and make changes accordingly. With cross-functional teams and automated testing in place, production issues can be eliminated. Together, they can build a powerful automation pipeline that comprises data source analysis, testing, documentation, deployment etc.
However, introducing DevOps for data warehouse management is not a cakewalk. For instance, you cannot simply backup data and revert to the backup as and when required. When you revert to a last week’s backup, what about the changes made to the data by several applications?
DevOps for Analytics
The analytics industry is going through a transformation as well. Contrary to the traditional analytics environment that uses a single business intelligence solution for all IT needs, modern businesses implement multiple BI tools for different analytical purposes. The complexity is that all these BI tools share data between them and there is no central management of BI tools. Another issue is that data scientists design models and algorithms for specific data sets to gain deeper insights and offer predictions. However, when these data sets are deployed to the production environment, they serve a temporary purpose. As data sets outgrow, they become irrelevant which means continuous monitoring and improvement is required. The rate at which the data drifting happens is enormous and traditional analytics solutions are inefficient to manage this speed and diversity. This is where DevOps comes to the rescue.
DevOps helps businesses integrate data flow designs and operations to automate and monitor data enabling them to deliver better applications faster. Automation enables organizations to build high performing and reliable build-deploy iterative data pipelines for improving data quality, accelerate delivery and reduce labor and operational costs. Monitoring data for health, speed and consumption-ready status enable organizations to reduce blindness and eliminate performance issues. It means a reliable feedback loop is created that covers data health, privacy and data delivery for ensuring smooth flow of operations for planned as well as unexpected changes.
The Bottom Line
Bringing DevOps into the BI realm is not an easy task as BI environments are not suitably designed for DevOps. However, businesses are now exploring this option. Bringing DevOps into the BI segment gives situational awareness to businesses as they can make informed decisions when they gain insights into relevant data added from multiple sources. Moreover, it brings great collaboration between teams, allows better integration between different application layers while helping businesses to explore and quickly tap into new markets. Most importantly, it makes your business future-proof.