“What will be hot and what to learn in 2022? This article is participating in the” Talk about 2022 Technology Trends “essay campaign.

This article is a translation from the following sources.

  • By Matt Campbell and Steef-Jan Wiggers
  • DevOps and Cloud InfoQ Trends Report

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Key Takeaways

  • Hybrid cloud options have evolved beyond the traditional definition, and have expanded to enable the functionality of cloud services to run outside of the cloud. Services such as Azure Arc and Google Anthos allow for a much more seamless “hybrid” experience for developers and operators.
  • Hybrid cloud options have gone beyond the traditional definition and have expanded to allow the capabilities of cloud services to run outside the cloud. Services like Azure Arc and Googleanthos provide a more seamless “hybrid” experience for developers and carriers.
  • In the emerging “no-copy data-sharing” approach it is not necessary to move or replicate the data to be able to access it from different services. We believe that the recently announced “Delta Sharing” open standard will contribute to the upward trajectory of no-copy data sharing.
  • In the emerging “no copy data sharing” approach, there is no need to move or copy data so that it can be accessed from different services. We believe that the recently announced “Delta Sharing” open standard will contribute to the upward trajectory of copy-free data sharing.
  • We believe that there has been limited progress on undoing the confusion between continuous integration (CI) and continuous delivery / (CD) tooling and practices. Both GitOps and site reliability engineering (SRE) practices are increasingly being adopted.
  • We believe that limited progress has been made in eliminating confusion between continuous integration (CI) and continuous delivery /(CD) tools and practices. GitOps and Field reliability (SRE) practices are increasingly being adopted.
  • Observability practices and tooling continue to mature. The logging and metrics domains of the three pillars of observability are relatively well-adopted, but the tracing pillar remains less so. There are a number of encouraging advancements in this space, especially with the more widespread adoption of OpenTelemetry.
  • Observable practices and tools continue to mature. The logging and measurement areas of the three pillars of observability are relatively well adopted, but there are still few trace pillars. There are some encouraging advances in this area, especially with the wider use of Opentelementtry.
  • We are following the innovative developments with FinOps, real-time information flow for cost analysis, aimed at the finance teams with public cloud vendors like Microsoft and AWS at the frontline.
  • We are tracking the innovative development of FinOps, a real-time flow of information for cost analysis, aimed at finance teams at public cloud providers like Microsoft and AWS.
  • Increasingly popular practices, such as “Policy as Code”, as promoted by Open Policy Agent (OPA), and remote access management tooling, e.g. HashiCorp’s Boundary, are pushing forward identity as code and privacy as code.
  • Increasingly popular practices such as Policy as Code, popularized by the Open Policy Agent (OPA), and remote Access Management Tools, Such as HashiCorp’s Boundary), is pushing identity as code and privacy as code.
  • We have seen the Team Topologies book become the de facto reference for arranging teams within an organization to enable effective software delivery. There is also increasing focus on post-incident “blameless postmortems” in becoming more akin to “healthy retrospectives”, from which the entire organisation can learn from.
  • As we have seen, team topology books have become the de facto reference for organizing teams in an organization to achieve effective software delivery. In addition, there is a growing focus on “blameless after-the-fact reviews” after events to make them closer to “health reviews” from which the entire organization can learn.

This article summarizes how we currently see the “cloud computing and DevOps” space, which focuses on fundamental infrastructure and operational patterns, the realization of patterns in technology frameworks, and the design processes and skills that a software architect or engineer must cultivate.

This article summarizes how we currently think about the “cloud computing and DevOps” space, which focuses on infrastructure and operational patterns, the implementation of patterns in technical frameworks, and the design processes and skills that a software architect or engineer must develop.

Both InfoQ and the QCon conference series focus on topics that we believe fall into the “innovator, early adopter, and early majority stages” of the diffusion of technology, as defined in Geoffrey Moore’s book “Crossing the Chasm.” What we try to do is identify ideas that fit into what Moore referred to as the early market, where “the customer base is made up of technology enthusiasts and visionaries who are looking to get ahead of either an opportunity or a looming problem.” We are also looking for ideas that are likely to “cross the chasm” to broader adoption. It is perhaps worth saying, in this context, that a technology’s exact position on the adoption curve can vary. For example, microservices are widely adopted amongst Bay Area companies but maybe less widely adopted and perhaps less appropriate elsewhere.

Both the InfoQ and QCon conferences focus on what we consider to be the “innovators, early adopters, and early mainstreams” of technology diffusion. What we try to do, as Defined by Geoffrey Moore in Crossing the Chasm, is to identify ideas that fit Moore’s early markets, “Customer base is composed of technophiles and visionary, they want to rob before opportunity or imminent problem.” we are also looking for possible “crossing the chasm” and get more widely used. In this case, maybe it is worth mentioning that a technology on the curve’s exact location may be different. Microservices, for example, are widely adopted by companies in the San Francisco Bay Area, but may not be as widely adopted or as appropriate elsewhere.

In this edition of the cloud computing and DevOps trend report, we believe that hybrid cloud approaches have evolved to become more “cloud native”. In late 2019 all the three prominent public cloud vendors brought new hybrid cloud products to the market and over the last two years they have continued to invest heavily in them – Google with Anthos, Microsoft with the Azure Arc and Stack offerings, AWS with Outposts, and more recently, Amazon ECS Anywhere. For enterprises, for instance, it is about not only bringing workloads to the Cloud but also running them on-premise or both, or on multiple clouds. Thus, managing the infrastructure for the workloads centrally with a service like Arc or Anthos delivers value. Furthermore, these products allow enterprises to extend their platform.

In this edition of the Cloud computing and DevOps Trends Report, we argue that the hybrid cloud approach has evolved to become more “local.” In late 2019, three well-known public cloud vendors all brought new hybrid cloud offerings to market, and they’ve continued to invest heavily in them over the past two years — Google with Anthos, Microsoft with Azure Arc and Stack, AWS has partnered with Outposts and, more recently, Amazon ECS Anywhere. For example, for an enterprise, not only does it have to bring the workload to the cloud, but it also has to run locally or both, or in multiple clouds. Therefore, an infrastructure that centrally manages workloads through services such as Arc or Anthos can provide value. In addition, these products allow enterprises to extend their platforms.

There has been increasing adoption (and technological evolution) in the space of “edge cloud” and “edge computing”, and so we believe this topic should move to the early adopter stage of our graph. There is a fair amount of traction here from specific vendor tools, such as Cloudflare Workers, Fastly’s Compute@Edge, and Amazon CloudFront’s Lambda@Edge.

The use of “edge cloud” and “edge computing” is becoming more widespread and the technology is evolving, so we think this topic should move into the early application phase of our graphics. There’s a lot of traction from vendor specific tools like Cloudflare Workers, Fastly’s Compute@Edge, and Amazon CloudFront’s Lambda@Edge.

The participants of this report have also identified an emerging trend named “no copy data sharing.” This can be seen in data management services such as Snowflake, which do not copy or move data, yet enable users to share data at its source. Another example is the Azure Synapse service, which supports no-copy data sharing from Azure Cosmos DB via Azure Synapse Link. The recently announced Delta Sharing open standard is also contributing to the upward trajectory of the no-copy data sharing tendency.

Participants in the report also identified an emerging trend called “copy-free data sharing”, which can be seen in data management services such as Snowflake, which do not copy or move data but allow users to share it at its source. Another example is the Azure Synapse service, which enables copy-free data sharing from the Azure Cosmos DB via the Azure Synapse Link. The recently announced Delta Sharing open standard also contributes to the rising trend of copy-free data Sharing.

Observability continues to be a popular topic within DevOps and SRE. While most organizations have begun to implement some form of observability stack, as Holly Cummins notes, the term is overloaded and therefore should be broken down into its various components. Ideas such as centralized log aggregation are currently commonplace in most organizations, however, logs only make up one of the three pillars of observability.

Observability is still a hot topic in DevOps and SRE. While most organizations have begun to implement some form of an observable stack, as Holly Cummins points out, the term is already overloaded and should therefore be broken down into components. Ideas like centralized log aggregation are common in most organizations today, however, logging forms only one of the three pillars of observability.

The increasingly popular OpenTelemetry project provides a consistent framework for capturing not just logs, but also traces and metrics. The consistency provided by adopting a single framework helps with capturing data across hybrid and heterogeneous environments and also monitoring tooling. The use of service level objectives (SLOs) as a tool to communicate the desired outcome of monitoring and observability is also gaining popularity as seen with the first ever SLOConf earlier this year.

The increasingly popular OpentelementTry project provides a consistent framework for capturing not only logging, but also tracing and measurement. The consistency provided by adopting a single framework helps capture data and monitor tools across mixed and heterogeneous environments. As the first SLOConf meeting earlier this year showed, the use of service level goals (SLOs) is also gaining popularity as a tool to communicate the expected results of monitoring and observability.

“DevOps for Data” has seen increasing adoption over the past year with the rise of both MLOps and DataOps. MLOps focuses on using DevOps style practices (such as CI and CD) to implement continuous training for machine learning models. Open source tooling and commercial services exist to help in this area, such as KubeFlow for deploying ML models on Kubernetes, and Amazon SageMaker Model Monitor for automating monitoring of ML models. DataOps looks to shorten the cycle time of data analytics by applying similar concepts used by DevOps teams to reduce their own cycle times.

The adoption of “DevOps for Data” has increased over the past year with the growth of MLOps and DataOps. MLOps focuses on continuous training of machine learning models using DevOPs-style practices such as CI and CD. Open source tools and commercial services can help in this area, such as KubeFlow, which deploys machine learning models on Kubernetes, and Amazon SageMaker Model Monitor, which automatically monitors machine learning models. DataOps reduces the cycle time for data analysis by applying similar concepts used by the DevOps team.

In the people and organisational space of DevOps we have seen the Team Topologies book, from Matthew Skelton and Manuel Pais, become the de facto reference for arranging teams within an organization to enable effective software delivery. Team Topologies describes four fundamental team types and three team interaction patterns, and dives into the responsibility boundaries of teams and how teams can communicate or interact with other teams.

In the DevOps People and Organization Space, we saw the book Team Topology by Matthew Skelton and Manuel Pais, which became a factual reference for organizing teams within an organization to achieve effective software delivery. The team topology describes four basic team types and three modes of team interaction, and delves into the boundaries of team responsibilities and how teams communicate or interact with other teams.

There is also increasing focus on post-incident “blameless postmortems” in becoming more akin to “healthy retrospectives”, from which the entire organisation can learn from. Key leaders in the computing domain of resilience engineering and the “Learning From Incidents” community have been influential in driving this discussion.

In addition, there is a growing focus on post-event “Blameless Postmortems” to make it more similar to healthy retrospectives, from which the entire organization can learn. Key leaders in the field of resilient engineering computing and the “learn from Events” community have been influential in advancing this discussion.

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