Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.
This week: Meta launches its much-leaked Threads microblogging app. A spinoff from Instagram, it’s already reached 30 million signups at the time of writing—even before it’s available in the EU. But how is Meta’s DevOps team making it scale? (more…)
The Fourth of July 2023 was not just a day of fireworks and barbecues but also the hottest day on Earth in as many as 125,000 years. The global average temperature reached 17.18 degrees Celsius (62.92 degrees Fahrenheit), breaking the record set just the day before. According to preliminary data from the ERA5 dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), the global average 2m temperature reached 16.88°C on Monday, breaking the previous record of 16.80°C from August 2016. The National Oceanic and Atmospheric Administration reports that these record-breaking temperatures are a clear sign of escalating climate change. This intense heat poses a significant challenge to our digital infrastructure, particularly data centers, the nerve centers of our digital world.
The resilience of data centers was put to the test in the summer of 2022. Intense heatwaves across California and the European Union strained these facilities. According to the BBC, over 50% of the UK’s oldest active weather stations recorded their hottest day ever in 2022, based on Met Office data. In the U.S., Los Angeles sweltered under a record-breaking 44.5°C (112°F). CNN reported that last September, Twitter experienced the loss of its Sacramento (SMF) data center region due to extreme heat. In London, record summer heat forced Google and Oracle into outages when cooling systems failed at data centers. The culprit was the critical temperature threshold of 27°C (80.6°F), at which point servers automatically shut down to prevent irreversible hardware damage.
Data centers house numerous servers that generate significant amounts of heat during operation. These servers rely on proper cooling mechanisms to maintain optimal performance and prevent overheating. The critical temperature threshold serves as a safety mechanism to protect the integrity and functionality of the servers. Today’s servers, reports Nautilus Data Technologies, contain the hottest processors and GPUs in history and are producing more than 1kW of heat per rack unit — or more than 40kW per rack. Many data centers simply cannot produce enough chilled air to keep those servers from overheating.
Exceeding the critical temperature threshold can lead to issues like reduced performance, data corruption and even permanent damage to the hardware components. By shutting down the servers when temperatures rise too high, data center operators can mitigate the risk of such detrimental consequences.
To counter these escalating challenges, the tech industry is driving forward with innovative solutions. For instance, in 2023, LiquidStack, a pioneer in cooling solutions, unveiled its new immersion cooling technology. This system submerges server components in a non-conductive fluid that carries away heat 1,200 times more effectively than air, offering a promising solution to heatwave-induced outages.
Data Center Frontier reported that at the Open Compute Summit, Meta, the social media giant formerly known as Facebook, outlined a roadmap for a gradual shift to a water-cooled infrastructure, using cold plates to provide direct-to-chip cooling for AI workloads.
A third promising innovation comes from Submer, who introduced a next-generation immersion cooling system in 2023. Their technology uses a proprietary dielectric fluid to cool server components directly, reducing the need for traditional air conditioning systems and making data centers significantly more energy-efficient.
As we continue to grapple with the increasing severity of climate change, it’s clear that the resilience of our digital infrastructures hinges on our ability to innovate and adapt. The tech industry is leading the charge, and while the journey is challenging, these emerging solutions offer a beacon of hope. The urgency to develop and implement such solutions is underscored by the IPCC’s predictions for more frequent and severe heatwaves, highlighting the crucial interplay between climate change and our digital world.
At Ecotech Insights, we bring together top thought leaders from various industries to discuss the intersection of sustainability and climate change. Techstrong Group’s Sustainability Contributor, Bonnie Schneider, hosts engaging conversations that explore innovative solutions and strategies to address environmental challenges.
Are you a visionary in the field of sustainability or climate change? We invite you to be featured on our show and share your expertise, insights and initiatives with our global audience. This is an exceptional opportunity to inspire others, spark meaningful discussions and drive positive change.
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Businesses increasingly use DevOps and TestOps practices to develop and deliver high-quality software products. In this article, I will explore the concept of TestOps, its integration with DevOps and the benefits of implementing automated testing to enhance product quality, accelerate release cycles and increase the transparency of CI/CD processes.
TestOps, a combination of testing and operations, is a software development methodology emphasizing the importance of integrating testing throughout the development life cycle. On the other hand, DevOps focuses on bridging the gap between development and operations to achieve faster delivery and higher-quality products.
The key benefits of seamlessly integrating TestOps and DevOps include the following:
By properly setting up TestOps, companies can make their DevOps processes more efficient, transparent and bug-free. Well-integrated testing in the CI/CD process allows for faster bug detection, ultimately saving companies resources, money and time.
Organizations can reap several benefits by seamlessly integrating testing into the deployment pipeline. In this section, I will outline the key goals of integrating TestOps with DevOps and how they contribute to the success of software development projects.
Integrating DevOps and TestOps aims to facilitate shift-left testing, enabling organizations to identify and address issues early in development. By incorporating testing from the outset, organizations can achieve the following goals:
TestOps, when integrated with DevOps, empowers organizations to develop bug-free software and enhance product quality by offering distinct bug detection, cost savings and resource optimization advantages. The following benefits can be realized through this integration:
While the process of integrating TestOps with DevOps can vary depending on the maturity of the company and the project, certain key steps can be taken to ensure success:
Also, it is essential to consider the following factors when defining a test automation strategy:
Cloud orchestration refers to the coordination and management of various interconnected cloud services and resources to automate and streamline complex IT processes. In simple terms, it is the process of integrating, synchronizing, and automating various cloud-based components to create a seamless and unified workflow.
Cloud orchestration aims to optimize the deployment, management, and use of resources in a cloud environment, improving efficiency, reducing costs, and ensuring that cloud services are delivered in a timely and effective manner. It is related to the concept of CloudOps (cloud operations), which uses cloud orchestration concepts to allow more efficient management of cloud resources. Cloud orchestration is a critical aspect of modern IT infrastructure management, as it enables organizations to manage and control their cloud resources more effectively.
Though the terms cloud orchestration and cloud automation are often used interchangeably, they have distinct differences.
Cloud automation refers to the process of automating individual tasks and processes within the cloud environment. It involves the use of scripts, templates and other tools to automate repetitive tasks, such as provisioning resources, deploying applications and configuring environments. The primary goal of cloud automation is to reduce the time and effort needed to manage cloud resources.
Cloud orchestration is a broader concept that encompasses not only the automation of tasks but also the coordination and management of the entire cloud ecosystem. It involves the integration of various cloud services, tools and resources to create a unified, streamlined workflow. Cloud orchestration ensures that all components in the cloud environment work together seamlessly, automating complex processes and managing dependencies between tasks.
In essence, cloud automation is a subset of cloud orchestration. While cloud automation focuses on automating individual tasks, cloud orchestration takes a more holistic approach, orchestrating multiple tasks and resources to achieve a higher level of automation and efficiency.
One of the main benefits of cloud orchestration in a DevOps context is the ability to increase efficiency and speed in the software development and deployment process. By automating and streamlining various tasks, cloud orchestration eliminates manual processes, reduces the potential for human error and accelerates the overall development cycle.
Cloud orchestration fosters better collaboration and communication between development and operations teams by providing a unified platform for managing cloud resources. This shared platform enables teams to work together more effectively, breaking down traditional silos and ensuring that all stakeholders have visibility into the development and deployment process.
Cloud orchestration simplifies the management of complex cloud infrastructure by providing a single, unified platform for cloud optimization—improving the management of resources, applications and services in the cloud.
This centralized management approach enables organizations to more easily monitor, control and optimize their cloud environment, ensuring that resources are used effectively and that applications are running smoothly. With cloud orchestration, organizations can more easily scale their infrastructure to meet changing demands, ensuring that they have the resources they need when they need them.
Cloud orchestration provides improved monitoring and troubleshooting capabilities, enabling organizations to more effectively identify and address issues in their cloud environment. With comprehensive monitoring tools and dashboards, teams can gain insight into the performance of their applications, identify bottlenecks and pinpoint potential issues before they become critical.
Despite the numerous benefits of cloud orchestration, integrating it into DevOps practices can present several challenges. Some of the common challenges include:
To successfully integrate cloud orchestration into DevOps practices and overcome the challenges mentioned above, organizations should consider the following best practices:
Cloud orchestration is an essential tool for organizations looking to streamline and optimize their IT infrastructure, particularly in the context of DevOps practices. By automating and coordinating complex cloud processes, organizations can improve efficiency, speed, collaboration and overall performance.
However, successfully integrating with DevOps can be challenging, requiring a clear strategy, the right tools and technologies and a commitment to continuous improvement. By following the best practices outlined in this guide, organizations can overcome these challenges and fully realize the benefits.
In my recent article Revolutionizing the Nine Pillars of DevOps with AI-Engineered Tools, I explained that the elastic infrastructure pillar involved automated provisioning and management of computing resources, often leveraging cloud-based solutions for scalability and resilience. This includes practices like infrastructure-as-code (IaC).
In this article, I explain how AI can help manage cloud resources for DevOps by predicting future needs based on usage trends and automatically adjusting resources to optimize cost and performance.
Here are some common DevOps use cases that IaC practices aim to address, along with potential AI tools that can assist in each scenario:
Private and Public Cloud Agnosticism:
• Use Case: Needing to deploy and manage infrastructure across multiple cloud providers or environments without vendor lock-in.
• AI Tool Example: AI-powered cloud management platforms (e.g., Turbonomic, CloudHealth) can analyze resource utilization, performance and cost across various cloud providers to optimize workload placement.
Reducing Infrastructure Configuration Bottlenecks:
• Use Case: Avoiding manual and error-prone configuration processes that delay deployment and cause inconsistencies.
• AI Tool Example: Intelligent configuration management tools (e.g., Puppet, Chef, Ansible) can automate the provisioning, configuration and management of infrastructure resources.
Infrastructure Cost Optimization:
• Use Case: Optimizing resource allocation and minimizing infrastructure costs.
• AI Tool Example: AI-driven cost optimization platforms (e.g., ParkMyCloud, Cloudability) can analyze usage patterns, recommend resource resizing or right-sizing and suggest cost-saving opportunities.
Infrastructure Sharing:
• Use Case: Sharing infrastructure resources securely across multiple teams or projects.
• AI Tool Example: AI-powered resource scheduling and sharing tools (e.g., Kubernetes with cluster autoscaling) can dynamically allocate and release resources based on demand and workload requirements.
Infrastructure Quality:
• Use Case: Ensuring consistency, reliability, and adherence to best practices in infrastructure deployments.
• AI Tool Example: AI-based infrastructure validation tools (e.g., Terrascan, Checkov) can scan IaC templates, provide security and compliance checks and offer recommendations for improvement.
Infrastructure Security:
• Use Case: Integrating security controls and policies into the infrastructure deployment process.
• AI Tool Example: AI-driven security tools (e.g., Aqua Security, Sysdig Secure) can analyze infrastructure configurations, detect vulnerabilities and enforce security policies during deployment.
Dynamic Orchestration:
• Use Case: Needing to dynamically manage and scale infrastructure resources based on workload demands.
• AI Tool Example: AI-powered orchestration platforms (e.g., Kubernetes with horizontal pod autoscaling) can automatically scale resources up or down based on metrics such as CPU utilization or request latency.
Integration and Control of Infrastructure with CICD Pipelines:
• Use Case: Seamlessly integrating infrastructure provisioning and deployment with CI/CD pipelines.
• AI Tool Example: AI-enabled CI/CD platforms (e.g., Jenkins with ML plugins, Harness) can leverage machine learning algorithms to optimize pipeline execution, provide intelligent recommendations and automate release processes.
Monitoring Infrastructure Performance:
• Use Case: Monitoring and ensuring the optimal performance of infrastructure resources.
• AI Tool Example: AI-powered monitoring solutions (e.g., Dynatrace, Datadog) can leverage machine learning to detect anomalies, predict performance issues and provide actionable insights for infrastructure optimization.
Rapid Restore of Infrastructure:
• Use Case: Quickly recovering and restoring infrastructure in the event of failures or disasters.
• AI Tool Example: AI-driven backup and recovery tools (e.g., Veeam, Rubrik) can automate backup processes, intelligently prioritize data restoration and leverage machine learning for predictive data recovery.
An organization may face a number of challenges when transitioning to AI tools for infrastructure, as follows:
Lack of Expertise and Skill Set:
• Challenge: Organizations may lack the necessary expertise and skill set to implement and operate AI tools effectively.
• Solution: Invest in training and upskilling programs for employees to acquire the required knowledge and skills. Partnering with external consultants or hiring experts in AI and infrastructure can also bridge the expertise gap.
Data Availability and Quality:
• Challenge: AI tools heavily rely on high-quality data for training and accurate decision-making, but organizations may face challenges in obtaining relevant data and ensuring its quality.
• Solution: Implement robust data governance practices to ensure data availability, quality and consistency. Invest in data collection, storage and processing frameworks that support AI initiatives. Data cleansing and preprocessing techniques can be employed to improve data quality.
Integration Complexity:
• Challenge: Integrating AI tools into existing infrastructure and workflows can be complex, especially when dealing with legacy systems and heterogeneous environments.
• Solution: Plan for seamless integration by conducting a thorough assessment of existing systems and infrastructure. Leverage APIs, automation frameworks and standardized protocols to enable smooth integration. Adopt modular and scalable architectures that facilitate the incorporation of AI components.
Ethical and Legal Considerations:
• Challenge: AI tools introduce ethical and legal considerations, including privacy, security, bias and regulatory compliance, which can pose challenges during infrastructure transformation.
• Solution: Develop clear guidelines and policies around data privacy, security and ethical considerations. Perform rigorous testing and validation to detect and mitigate biases in AI models. Engage legal and compliance teams to ensure alignment with relevant regulations and standards.
Change Management and Cultural Shift:
• Challenge: The adoption of AI tools often requires a cultural shift within the organization, including changes in mindset, processes and collaboration between teams.
• Solution: Develop a change management strategy to communicate the benefits of AI tools and address any concerns or resistance. Foster a culture of experimentation, learning and collaboration. Encourage cross-functional teams to work together and share knowledge. Provide ongoing support and training to ensure successful adoption and adaptation to the new tools and practices.
Incorporating AI into IaC practices for DevOps brings transformative benefits that drive efficiency, agility and scalability. By leveraging AI tools, organizations can optimize resource allocation, streamline deployments, enhance security and improve performance monitoring. Embracing AI for IaC empowers teams to automate and intelligently manage infrastructure, resulting in faster time-to-market, cost savings and increased productivity. Unlock the full potential of DevOps by embracing AI and revolutionize your infrastructure management with the power of intelligent automation.
It’s important to note that challenges and solutions are to be expected when transforming to the use of AI, and the specific context and requirements of each organization may vary. Therefore, it’s advisable to assess the organization’s unique needs and constraints to tailor the solutions accordingly.
In my recent article Revolutionizing the Nine Pillars of DevOps with AI-Engineered Tools, I explained that the continuous integration (CI) Pillar is about developers frequently merging code changes into a central repository. After that, automated builds and tests are run, successful builds are containerized and release candidate artifacts are signed. The main objectives of CI are to detect problems early, reduce integration issues and provide high-quality, secure release candidates. In this article, I explain how AI can enhance each step in the CI phase of DevOps CI/CD pipelines.
Code Branching: In DevOps, managing multiple branches can be challenging, especially with large teams. This can lead to merge conflicts, confusion about where code should be committed and difficulty tracking changes.
• AI Solution: AI can help by automatically managing branches and offering recommendations on merging. AI-based systems can analyze code patterns and project management styles to suggest optimal branching strategies, minimizing conflicts and maintaining organization.
• AI Tool Example: Microsoft’s IntelliCode offers AI assistance for software development which could potentially help manage branches by suggesting optimal strategies.
Code Commits: Commits need to be made regularly to maintain the codebase, but they can introduce errors. Miscommunication and the lack of a unified commit policy can lead to problems.
• AI Solution: AI can analyze commit histories to identify patterns and recommend best practices. Additionally, AI tools can provide real-time feedback on code changes, suggesting potential improvements before the code is committed.
• AI Tool Example: Kite is a coding assistant that uses AI to help with code changes and improvements before the commit stage.
Merges: The process of merging code changes can lead to merge conflicts, particularly in large, complex codebases.
• AI Solution: AI tools can help predict potential merge conflicts and offer solutions before they occur. They can analyze code structures and change patterns to predict and prevent these conflicts.
• AI Tool Examples: Tools like DeepCode or Sider use AI to review and analyze your code to help prevent merge conflicts.
Static Scanning: This process can be time-consuming and complex with a high potential for false positives.
• AI Solution: AI can improve static analysis tools by learning from past scans to improve accuracy, reduce false positives, and even suggest potential fixes for issues identified.
• AI Tool Example: DeepCode is an AI tool that analyzes your code, learns from past scans and suggests fixes for issues identified.
Builds: Build failures can occur due to a number of issues, including poor code quality, incompatible changes, and environment configuration issues.
• AI Solution: AI can predict build failures by analyzing historical data and identifying patterns that lead to such failures. AI can also automate the build process and manage environment configurations to reduce human error.
• AI Tool Example: CircleCI Insights uses machine learning to analyze and predict the success of builds, helping to mitigate build failures.
Scanning for Quality Errors and Security Vulnerabilities: This process can be slow and yield false positives, and the sheer number of potential issues can be overwhelming to manage.
• AI Solution: Machine learning can be trained on previous scans to recognize patterns, improving the speed and accuracy of scans. AI can also prioritize issues based on potential impact, helping teams focus their efforts.
• AI Tool Example: ShiftLeft scans for vulnerabilities in the code and uses AI to prioritize the issues found.
Unit Testing: Writing and maintaining unit tests can be time-consuming and it’s often difficult to achieve good coverage.
• AI Solution: AI can assist in generating unit tests based on code analysis and historical data, ensuring good coverage and saving development time.
• AI Tool Example: Diffblue Cover uses AI to automatically write unit tests, aiming to improve coverage and save developer time.
Containerization: Misconfiguration of containers can lead to issues such as application failures, security vulnerabilities or inefficient resource usage.
• AI Solution: AI can help solve this challenge by automating and optimizing the configuration process.
• AI Tool Example: Magalix can analyze historical configuration data and current application requirements to suggest optimal configuration settings for a container. It can flag potential misconfigurations or inefficiencies and suggest improvements based on best practices learned from past data.
Functional Integration Testing: These tests often require significant resources and time to write and maintain.
• AI Solution: AI can generate functional integration tests based on system behavior analysis, reducing the time and effort required to maintain these tests.
• AI Tool Example: The test.ai tool uses AI to generate and execute functional tests, reducing the need for manual test case creation.
Determining Whether a Build is Suitable as a Release Candidate Artifact: Manual analysis of build quality can be slow and subjective.
• AI Solution: AI can analyze build artifacts, test results and other data to predict the suitability of a build as a release candidate, reducing the time and subjectivity involved in this decision.
• AI Tool Example: Google’s Cloud Build can be leveraged with machine learning to predict the success of builds.
Digitally Signing Artifacts in an Artifact Repository: This process can be cumbersome and prone to human error.
• AI Solution: AI can manage the process of digitally signing artifacts, ensuring that only validated, authorized code is signed and reducing the potential for human error.
• AI Tool Example: While no specific AI tool exists for this task, automated DevOps tools such as JFrog Artifactory or Sonatype Nexus could be combined with AI technology to automate the signing process, ensuring that only validated, authorized code is signed.
The transition from traditional continuous integration (CI) to AI-engineered CI presents several challenges:
Integration with Existing Systems: When incorporating AI into an existing CI toolchain, ensuring seamless integration can be a challenge. Organizations need to verify that AI tools can work effectively with current systems and don’t disrupt existing workflows.
Skillset and Understanding: Adopting AI requires a certain level of understanding of how AI works, including its strengths and weaknesses. Teams need to be trained on how to use AI tools effectively and interpret their outputs.
Data Privacy and Security: AI tools often rely on analyzing large amounts of data, which can include sensitive or proprietary information. Organizations need to ensure that using these tools doesn’t compromise their data security and privacy.
Cost: Implementing AI tools can be costly, both in terms of purchasing and deploying the tools and in the potential need for additional hardware resources. Organizations need to consider the return on investment (ROI) and determine if the benefits of using AI outweigh the costs.
Tool Maturity: Many AI tools in the DevOps space are relatively new and still evolving. Organizations need to consider the maturity and stability of the tools they’re planning to use.
Over-Reliance on AI: While AI can greatly enhance CI processes, over-reliance on AI without proper understanding can lead to misinterpretations and errors. It’s important to balance AI usage with human oversight and expertise.
Transitioning to AI-engineered CI toolchains can have several significant benefits for organizations:
Improved Efficiency: AI can automate various aspects of the CI process, such as code review, testing and environment configuration. This can greatly reduce the time taken for these tasks, speeding up the overall CI process and allowing teams to deliver software more quickly.
Increased Quality: AI tools can perform in-depth code analysis and identify potential issues with a level of detail that might be missed by humans. This can lead to improved code quality, fewer bugs, and more stable software.
Predictive Analytics: AI can analyze historical data to predict outcomes, such as the likelihood of a build failure. This can help teams proactively address issues, reducing the risk of failures and improving the reliability of the CI process.
Improved Decision Making: AI can provide valuable insights based on data analysis, helping teams make more informed decisions. This can relate to things like identifying the most suitable release candidate or deciding when and how to scale resources.
Consistency and Standardization: AI-driven CI can ensure consistent application of rules and standards, as there’s less reliance on individual developers’ judgment. This can lead to a more standardized, reliable development process.
Overall, AI has the potential to greatly improve the efficiency, accuracy and effectiveness of DevOps continuous integration practices. However, it’s also important to note that these solutions require careful management and oversight to ensure they’re working as intended and improving rather than hindering processes. Organizations should start with small, manageable projects to gain an understanding and experience with AI before scaling up. They should also invest in training and support to help their teams adapt to these new tools.
In several projects of late, I have noted a trend in otherwise rockstar IT departments: They identify an area that they wish to implement or approve and then set out to address it. So, “We need a new CI tool,” spawns a project to choose and implement a new CI tool, for example.
But there are so many details that these projects are missing from the start that should have been put right into the charter/project kickoff documents.
Let’s start with, “What CI tools do we currently have in place?” This is the 2020s. How is it possible we are still starting these projects as if there was no solution in the virtual building already doing the job? We need to start from, “Here are the tools that can do this job that are currently running in our environment.” Only then should we move on to, “Here are other options to consider.” But I will humbly suggest that if there is a single tool that can solve your issues, it should be used–and if, of all of the tools running in the organization, none of them can solve all of your issues, then a new product should be chosen that can.
The same is true for things like alerting. When I am analyzing a given vendor’s solutions, I ask about how many places they can raise issues–it is important that a vendor asking you to purchase their product can report and alert to whereever the organization needs it to. So that could mean Jira or ServiceNow, Slack or email, centralized logging—all of the above and sometimes more (SIEM for security events, for example) need to be supported. But when a given organization is implementing? Choose a reporting method.
The most common is, “We do Slack, but we also do email. And, of course, we coordinate though issue management like Jira …” Stop. Seriously. This is not about, “Oh, they made it easy, so we just implemented every option.” This is about, “We need to keep track of conversations and resolutions.” If two people are discussing an issue over Slack while a separate team is discussing over email, you’re failing to communicate. Even if there is linkage to ServiceNow or Jira and that is where conversations should happen, we all know that final resolution—but not troubleshooting steps—is what ends up there. So offer one robust and widely available alerting mechanism and let people run with it.
That type of redundancy is all over IT. Organizations that would not implement per-team email solutions (not servers, solutions) will happily allow for per-team DevOps toolchains. Why? What, exactly, is different? The purpose of a mail server is to deliver mail. Choosing which mail vendor/service to run with is based upon how a given product does so and what additional services come with it. How is that different than, “The purpose of the DevOps toolchain is to deliver applications, and selection is based upon what vendor/product best serves the way our organization does so?” There isn’t a difference; we imagine one. We latch on to one feature that we must have to keep our current favorite, and the compromise is that the next team over can keep their tools, too, because they must have some other feature.
Evaluate what the organization as a whole needs, identify tricky bits like must-have features and determine if that really is a requirement. If it is, ask why it isn’t a requirement for every project not using that tool.
And keep moving those apps along! You are building and deploying more apps more securely than ever before. Continue to sanitize the build and delivery process, and continue to crank out apps that make the business hum. But don’t make it part of your job to support fifteen tools that all do the same job. Streamline and improve. And keep rocking it.
DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.
Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.
Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.
The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.
We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:
DuckDuckGo
Remote
Senior DevOps Engineer
$170,000
CareerBuilder.com
Credit Acceptance Corp.
Remote
Staff DevOps Engineer
$145,000 to $150,000
Indeed.com
NASDAQ
New York, New York
DevOps Engineer
$104,400 to $174,000
SimplyHired
U.S. Bitcoin
Remote
Software DevOps Engineer
$100,000 to $130,000
Dice.com
ITBMS
Remote
DevOps OpenShift Engineer
$80,000 to $100,000
A CloudBolt Software survey found that while the majority (98%) of respondents worked for organizations with a formal FinOps team (82%) or are actively considering adding one (16%), realizing value from FinOps is slow. More than half of existing FinOps teams (54%) were formed just 12 months ago or less, the survey also found.
The trouble is, only one in of 500 respondents claimed to achieve any material value from FinOps to date. More than three-quarters of respondents (76%) said they will have to wait two to three years to see real FinOps-driven results.
The survey polled 500 executives, engineers and developers at companies with over 5,000 employees in the U.S., Canada, United Kingdom and Australia. It was conducted by Wakefield Research on behalf of CloudBolt Software, a provider of a platform for managing distributed computing environments.
A total of 45% of respondents also questioned FinOps efficacy in the real world. Specifically, 15% flagged FinOps as being “theoretically easy but harder in practice,” with another 13% regarding it as “much ado about nothing.” Another 10% said it is “nothing more than a suggested framework,” while 7% labeled FinOps as “a necessary evil.”
Nevertheless, a full 89% also described FinOps as the silver bullet for reining in the complexity of cloud cost management, with 68% noting that FinOps is among their organization’s most strategic priorities for 2023. The average size of a FinOps team is now 4.1 people, with the “director level” being the senior-most leader on the team 53% of the time, the survey noted.
A total of 71% noted that achieving this year’s IT goals without a FinOps practice would pose a significant challenge, with 74% reporting that FinOps is now on par with ITOps, DevOps, SecOps and other IT disciplines.
In addition, 71% of respondents said funding for FinOps initiatives increased in 2023, with 58% reporting key performance indicators (KPIs) and metrics to the C-suite or board of directors.
CloudBolt Software CTO Kyle Campos said it’s clear that when it comes to reining in costs, large enterprises are struggling to effectively manage IT across multiple silos. In many cases, organizations were investing heavily in IT before and during the COVID-19 pandemic to drive digital business transformation initiatives without many cost controls being put in place, he added.
Now that organizations are encountering economic headwinds, many chief financial officers are now asking IT leaders to be a lot more efficient, noted Campos.
Developers, in particular, have for years been provisioning cloud infrastructure resources with little to no oversight. Unfortunately, developers are prone to leaving the proverbial lights on by, for example, leaving application development environments running over a weekend or simply forgetting that a virtual machine has been left to run for weeks—sometimes even months.
Developers are also not especially good at alerting IT leaders of possible spikes in consumption due to, for example, a data-intensive workload being constructed. No finance team likes to be surprised by a monthly bill for a cloud service that is twice as much as the previous month.
The one thing that is certain is that time is of the essence. FinOps, at its core, may not necessarily be a new idea, but IT organizations that fail to practice it may soon find themselves being held more accountable than any time in recent memory.
