Tag: devops
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Navigating the Multi-Cloud Ecosystem
Multi-cloud infrastructure, an approach where organizations utilize services from multiple cloud providers, is rapidly gaining traction in the tech world.
In this article, we will unpack the concept of multi-cloud infrastructure, examining how it enables businesses to diversify their cloud ecosystems for enhanced performance, greater flexibility and improved risk management.
Understanding the Multi-Cloud Ecosystem
Multi-cloud environments represent a strategic approach in cloud computing where businesses utilize services from multiple cloud providers to meet diverse operational needs.
This concept differs from hybrid cloud, which typically blends both private and public cloud infrastructures, aiming to offer a balance between the two under a single unified environment.
In contrast, a multi-cloud strategy involves using distinct cloud services – for instance, one provider for storage, another for computing power and another for analytics – without necessarily integrating them into one cohesive system. This approach allows organizations to leverage the unique strengths and offerings of various cloud providers, thereby optimizing their IT infrastructure for efficiency, cost-effectiveness and enhanced performance.
The Advantages of a Multi-Cloud Approach
Adopting a multi-cloud approach brings several key benefits, the biggest among them being enhanced resilience, flexibility and the avoidance of vendor lock-in.
By distributing resources and workloads across multiple cloud environments, businesses can mitigate risks associated with relying on a single cloud provider, such as service outages or data loss. This diversity ensures continuous operation and robust disaster recovery capabilities.
Additionally, a multi-cloud strategy empowers organizations with the flexibility to select the best services that each cloud provider offers, tailoring their IT infrastructure to specific needs and operational demands. This tailored approach enables businesses to optimize both performance and cost. Different providers may offer competitive pricing for specific services, allowing companies to choose the most cost-effective solutions for their requirements.
Moreover, by avoiding vendor lock-in, organizations retain the freedom to switch providers as needs evolve or as newer, more advantageous services become available. This strategic utilization of multiple clouds ultimately leads to a more efficient, agile and economically sound IT infrastructure.
Key Challenges in Multi-Cloud
Multi-cloud management comes with its own set of challenges, particularly in terms of complexity, data security and compliance. DevOps teams often grapple with the complexity of managing disparate cloud services, each with unique features and interfaces, making seamless integration and consistent operation a demanding task.
Data security is another critical concern, as safeguarding sensitive information across multiple platforms requires robust and adaptable security measures. Additionally, ensuring compliance with varying regulations across different cloud environments adds another layer of complexity.
These challenges necessitate that DevOps teams are technically proficient and adept at strategic planning and coordination to manage a multi-cloud ecosystem effectively.
Best Practices in Multi-Cloud Deployment and Management
When managing and deploying within a multi-cloud ecosystem, DevOps teams can greatly benefit from the following five best practices:
1. Strategic Cloud Service Selection: Carefully assess and choose cloud services based on specific workload requirements, performance metrics and cost-effectiveness to ensure optimal deployment.
2. Centralized Monitoring and Management: Utilize centralized tools for monitoring and managing different cloud environments to maintain consistency and control and to streamline operations.
3. Consistent Security Posture: Apply uniform security policies and practices across all cloud platforms to protect data integrity and comply with regulatory standards.
4. Automation: Automate repetitive and routine tasks across different clouds for efficient resource utilization, reduced errors, and faster deployment cycles.
5. Cost Management and Optimization: Continuously monitor and manage cloud spending across platforms to avoid cost overruns, ensure efficient use of resources and follow cloud cost optimization best practices.Building a Multi-Cloud Strategy: A DevOps Perspective
From a DevOps perspective, building a comprehensive multi-cloud strategy involves aligning cloud infrastructure with business objectives and core DevOps principles.
The first step is clearly defining business goals and identifying how different cloud environments can support these objectives. This should be followed by selecting cloud services that best match the specific needs of each workload, considering factors like performance, cost and compliance requirements. Integrating continuous integration and continuous deployment (CI/CD) processes is crucial in a multi-cloud setup.
This integration ensures software can be developed, tested and deployed rapidly and reliably across different cloud platforms. The strategy should also include implementing robust automation and monitoring tools to maintain consistency and efficiency across the cloud.
By focusing on these elements, a DevOps team can develop a multi-cloud strategy that not only aligns with the organization’s goals but also adheres to the agile and efficient ethos of DevOps methodologies, ensuring seamless operation and scalability in the multi-cloud environment.
Wrapping Up
In conclusion, effectively navigating the multi-cloud ecosystem is crucial for DevOps success, as it offers unparalleled flexibility, scalability and resilience in managing diverse cloud resources. DevOps professionals are encouraged to embrace multi-cloud strategies as they enhance operational agility and open avenues for innovation and improved service delivery.
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New Year, New ‘Tude
There are multiple types of debt in an organization. In IT alone, you can have technical, ops and (my favorite) attitude.
We work in a high-stress field with a lot of moving parts and an enterprise depending upon us to keep things running. Even if you are in the product development org at a software company, you literally make the product; people are more dependent on you doing it right most of the time and being super proactive when things go wrong.
All of this creates stress that we tend to either internalize or leave to find a new place to get our stress.
Internalizing makes for jaded IT folks. Or worse, cynical ones. Vacations are part of the salve to relieve that stress, and we just got a couple of weeks off. Or at least most of us did, some of you all are about to, and others will in February. But it is all the same.
To continue improving, we need to leave the baggage behind. “Blameless IT” improved how others judge devs and operators that make mistakes, but we’ve done little to improve when devs and operators see problems that they cannot fix and it festers.
When you return to work, take a deep breath, consider what you can do something about versus what you cannot. Then, take action on the things you can and ignore the things you cannot. Improvement is what matters; improving a specific broken process isn’t. Of course, it will eat at you if you pay a ton of attention to something you cannot change. I have always taught those working for me (and my kids, for that matter) to change what you can and not dwell on what you can’t—precisely because you can’t.
If the thing you can’t change is a show-stopper and makes your life miserable, then the thing you can change is your job. Start looking.
Between Agile, DevOps and new deployment methods, we have come a long way in technology, but we also have created a ton of work for ourselves. This stuff doesn’t implement itself. Focus on that work and on how you can make things better – for the org, for IT, for fellow developers or operators. And do that. Always improving and not dwelling will make life happier for you and generally more productive for your team.
I’m excited for a ton of things that 2024 is bringing, and a couple of things that aren’t necessarily ‘2024 new,’ but that I’m still figuring out. Let’s do new stuff with a smile, because we know we’re making things better/faster/more reliable. And implement that feature your users all want and your team hasn’t done yet, while you’re at it. Users are the reason we do this, in the end, be they internal or external.
We made it through 2023 with some great improvements—no doubt you did, too. Now, it’s off to 2024 to do more. Keep rolling out features, protecting the systems, deploying to new targets, etc. And forget about that stupid annoyance. (Until you can change it—then crush it like a bug). Meanwhile, thank you from all those that don’t realize they add to your stress every day.
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The Age of Platform Engineering: What to Expect in 2024
Platform engineering has been gaining traction over the last few years, with some likening it to the next iteration of DevOps. Gartner has called it one of the top strategic trends of 2024 – and has predicted that 80% of large software engineering organizations will establish platform engineering teams by 2026. But what’s driving the momentum?
I believe it’s a result of maturity in the adoption of the cloud by enterprises of all sizes and shapes. As more and more enterprises started using the cloud and embraced DevOps as a path towards quickly taking features to the market, there was a clear need to rethink how the underlying infrastructure and platforms were used for enterprise applications. Platform engineering emerged as the new path to building and managing the underlying application infrastructure.
It’s been a growth year for platform engineering, but in today’s era of hybrid-cloud-meets-generative-AI, we see new trends emerging in this space. So, what’s next? Here is what we expect to see as we head into 2024.
Product-Centric Funding Model
Traditionally, IT was seen as a gatekeeper and cost center. With the cloud, developers gained more leverage in procuring the infrastructure they wanted for their applications. Enterprises realized the value of modernizing their IT operations to meet this modern need and wanted to transform their cloud operations to be part of the innovation team. They see platform engineering as the transformational approach that aligns cloud operations with developers and business users. However, platform engineering requires a product-centric funding model instead of project-centric models in the past, as a product-centric model doesn’t have a start and end date like projects and aligns closely with the product lifecycle with ongoing funding. Enterprises need to recognize this shift from the status quo widely, and we expect this realization to come for businesses in 2024.
Platform Engineering as an Extension to DevOps
As enterprises embraced DevOps to accelerate their application development, they still used the traditional gatekeeping models with cloud operations. The embrace of traditional guardrails in the cloud created friction and slowed innovation. With its “as code” model, platform engineering is well suited for reducing friction and accelerating innovation. We expect organizations to embrace platform engineering as the natural extension of DevOps to operate at the speed of business.
Generative AI Capabilities
Generative AI is at the peak of the hype cycle as we close out 2023, and in 2024, we will see some enterprise-ready use cases come to life. Generative AI will augment platform engineering with code assistants to create infrastructure-as-code, governance-as-code and security-as-code to ensure smooth Day 2 operations. We will also see chatbots leveraging generative AI to help SREs and other platform engineering pros with documentation, infrastructure playbooks and other relevant use cases.
2024 is a critical year for organizations as they fend off the economic forces and embrace technological advancements to compete in the global market. Most CXOs will see platform engineering as the top strategic trend to focus on because it brings productivity and cost efficiencies to their organization. This is only the beginning, and the coming decade will be defined by platform engineering, laying a firm foundation for AI-driven innovation in enterprises.
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Five Great DevOps Job Opportunities
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:
LinkedIn
Fox Tech
New York, New York
Senior DevOps Engineer
$121,500 to $170,000Dice.com
Chuwa America Corp
Austin, Texas
Adobe Experience Manager (AEM) Developer – DevOps
$120,000 to $140,000Indeed.com
The Travelers Companies
Hartford, Connecticut
DevOps Engineer
$115,700 to $190,900SimplyHired.com
Great Minds
Remote
Senior DevOps Engineer
$114,000 to $141,000CareerBuilder.com
Caresoft
Concord, New Hampshire
DevOps Engineer
$100,000 to $150,000 -
Choosing the Right Programming Language
In software engineering, selecting a programming language is a pivotal decision for any organization. The appropriate language contributes to a streamlined development workflow and yields a resilient software solution that facilitates swift and efficient debugging, documentation, enhancement and maintenance. On the other hand, selecting an unsuitable language can set a project on a course for failure from the onset. Given the multitude of available options and the magnitude of this decision, how can programmers ensure they make the most judicious language choice?
A comprehensive and effective decision-making process begins with a thorough understanding of the current landscape, historical context and the evolutionary trajectory of programming languages. It is also imperative to critically appreciate the benchmarks employed by computer scientists and the industry’s best practices in language selection. Programmers who invest time to enhance their expertise in these domains will mitigate the risk of selecting a language deficient in essential features or one poised for obsolescence, thereby safeguarding the resulting product’s longevity, relevance and success.
The evolution of programming languages
Charles Babbage is credited with creating the first computer between 1833 and 1871. He spent nearly 40 years perfecting his invention, which ultimately could perform simple calculations. While Babbage is recognized for building the initial computer, in 1883, Ada Lovelace played a pivotal role by devising the programming language for Babbage’s invention. Today, 140 years later, more than 500 distinct programming languages facilitate human-computer interaction. The continuous adaptation and innovation of programming languages have paralleled the dynamic evolution of technology.
Contemporary programming languages, such as Swift, Kotlin and Go, exhibit more user-friendly syntax and enhanced ease of use than their predecessors, including SQL, MATLAB and C. Recent languages often integrate features that were unavailable just a few years ago. As such, programmers have the ability to select from a diverse array of languages, identifying the most suitable for their project that facilitates the efficient completion of tasks. For example, Swift is used in Mac and iOS platforms, C# is tailored for the .NET Framework and JavaScript, renowned for constructing the internet, is a testament to the varied roles these languages play in diverse technological domains.
Selecting the right programming language
With vast options of programming languages available, one of the greatest challenges is determining which is best suited for a particular project. Comparing the various features and syntaxes of languages can be complicated. Based on recent industry trends, Table 1 details the key programming languages in popular areas/domains.
Area/Domain
Programming Languages
Web development
JavaScript, Java, HTML5, Ruby, TypeScript
Mobile development
Kotlin, Swift, Objective-C, ++, C#
Data science
Python, R, SQL, Scala, Julia, MATLAB
Artificial intelligence and machine learning
Python, R, Java, Lisp, Prolog, JavaScript
Cloud computing
Java, Python, JavaScript, Golang, .Net Core
Cybersecurity
Perl, C++, SQL, Ruby, Rust, Assembly
Table 1: Key programming languages by domain
Scientifically benchmarking programming languages
One of the most efficient and effective ways to differentiate programming languages is through benchmarking, a process through which computer scientists analyze and compare programming languages. For instance, they may compare the performance of different programming languages directly against each other. They may also assess performance differences between different compilers or runtimes of the same language with the same source code. In addition, they may assess real server environments such as hosted cloud VMs or Docker/Podman and their effects on the performance of various programming languages.
Numerous renowned computer scientists have begun benchmarking programming languages, making it easier to tell them apart and select the best language for a particular task. The Computer Language Benchmarks Game is a popular free software project that compares how popular programming languages implement simple algorithms. Programmers can view results and better comprehend each language, its speed and optimal use cases. Additionally, several reputable websites, such as Programming Language and compiler-Benchmarks and Kostya-benchmark, provide computer scientists with side-by-side comparisons of different programming languages, taking into account various performance issues.
Ranking of programming languages by popularity (2023)
When selecting a programming language, some of the factors for programmers to consider include the targeted platform, production time, language elasticity, community/support, the programmer’s experience with the language, and the language’s current popularity. Popularity is an important factor because even though more than 500 programming languages are available, most of the industry relies on just 10, making those the most likely to remain in use. Table 2 highlights the 10 most popular programming languages based on the PYPL Index, which assesses how frequently language tutorials are searched for on Google and Stack Overflow’s Developer Survey 2023.
Ranking
PYPL Ranking
September 2023
Stack Overflow’s
Developer Survey 2023
#1
Python
JavaScript
#2
Java
HTML/CSS
#3
JavaScript
Python
#4
C#
SQL
#5
C/C++
TypeScript
#6
PHP
Bash/Shell
#7
R
Java
#8
TypeScript
C#
#9
Swift
C++
#10
Objective-C
C
Table 2: 2023 10 most popular programming languages
When making the final programming language selection, it is essential to remember that no programming language will be perfect for any particular project. As such, programmers need to allow for some flexibility in the selection process. Determine what is most important, such as speed, performance or fewer lines of code, and then sacrifice other areas to ensure a solution that best fits the identified priorities.
The future of programming languages
Today’s programming world is centered around artificial intelligence (AI) and machine learning (ML). As such, languages like Python, currently a driving force behind AI, will likely grow in popularity. Moving forward, programmers can expect an increase in the popularity of JavaScript, which dominates web browsers; Java, which is prevalent in the business world; Kotlin, which is widely used in apps and C++, which is used frequently in system-level programming and game development.
As technology evolves, programming languages—both existing and yet-to-be-developed—will play a pivotal role in shaping the future of a digital landscape. Their role in incorporating new tech features, expanding ML, augmenting AI and furthering the internet-of-things (IoT) will become increasingly critical.
As new languages appear and evolve, old languages will fade away. To that end, Analytics Insight recently published a list of 10 programming languages, including currently well-used languages like Visual Basic and COBOL, which it expects to dramatically decrease in popularity and use by 2030. For this reason, it is essential for developers and decision-makers to stay up to date on the ever-evolving world of programming languages. As existing languages are retired and new ones enter the marketplace, it is imperative that professionals learn promising new languages and continually upskill. Those who commit to ongoing training and education will simplify the programming language selection process, increase their chances of running a successful project, and solidify their position in an increasingly competitive marketplace. Those who don’t risk going extinct alongside once-popular programming languages.
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Best of 2023: 5 Microservices Design Patterns Every DevOps Team Should Know
As we close out 2023, we at DevOps.com wanted to highlight the most popular articles of the year. Following is the latest in our series of the Best of 2023.
Microservices have revolutionized the world of application development, breaking down large, monolithic systems into smaller, more manageable components. The architectural style, characterized by independent, loosely-coupled services, brings numerous advantages from scalability and modularity to increased flexibility.
How can DevOps teams best utilize this approach for maximum efficiency? The answer lies in understanding and effectively employing microservices design patterns. In this article, we will delve into the five key microservices design patterns that every DevOps team should know: The API gateway pattern, database per service pattern, circuit breaker pattern, event-driven pattern and the saga pattern. We’ll explore what these patterns are, the benefits they bring, their challenges and touch on how to choose the optimal microservices design patterns for your project.
What Are Microservices?
Microservices is an architectural style that structures an application as a collection of small, loosely coupled and independently deployable services. Each of these services corresponds to a specific business functionality and can be developed, deployed and scaled independently.
The idea behind microservices is to break down a large, monolithic application into a collection of smaller, more manageable pieces. Each microservice is a separate component that can be developed, tested, deployed, scaled and updated independently of all other microservices. This approach offers numerous advantages, such as increased modularity, flexibility and scalability, making it highly popular among organizations seeking to improve their application’s performance and maintainability.
Contrary to the monolithic architecture, where all components of the application are interconnected and interdependent, in a microservices architecture, each service is independent and communicates with others via well-defined APIs and protocols. This independence allows for using different technologies and languages for different services that best fit each service’s requirements.
Benefits of Microservices in DevOps
Microservices architecture has emerged as a game-changer in the DevOps landscape. Let’s delve into some of the key benefits of integrating microservices into your DevOps practices.
Independent Deployments
One of the biggest advantages of microservices is that they can be deployed independently. This means that changes can be made to a single service without affecting the entire application. In a monolithic architecture, even a small change requires redeploying the whole application, which is both time-consuming and risky. However, with microservices, teams can update, adjust or even completely rewrite a service without disrupting the overall application’s functionality. This facilitates continuous delivery and deployment, key aspects of DevOps culture.
Fault Isolation
Another major benefit of microservices is enhanced fault isolation. In a monolithic architecture, a failure in one component can bring down the entire application. However, in a microservices architecture, if one service fails, the others continue to function normally. This isolated failure can be dealt with without impacting the overall application performance. Therefore, microservices contribute significantly to the stability and resilience of an application.
Enhanced Scalability
Microservices also offer superior scalability. Since each microservice is a separate entity, it can be scaled independently based on demand. If a particular functionality experiences high demand, only the corresponding service needs to be scaled up rather than the entire application. This targeted scaling is not only more efficient but also cost-effective, making microservices a preferred choice for businesses experiencing variable loads.
Importance of Design Patterns in Microservices Architecture
When it comes to microservices, one size does not fit all. Different applications have different requirements, and the design of your microservices architecture should accommodate these specific needs. That’s where design patterns come into play. Let’s examine the importance of these patterns in the context of microservices.
Scalability
Scalability is one of the critical factors to consider while designing a microservices architecture. The ability to handle increased loads by adding more instances of services is a core advantage of microservices. However, this requires careful design to ensure that services can be easily duplicated and distributed. Design patterns like the replicated service instance and sharded services patterns help achieve this scalability.
Reducing Complexity
Design patterns play a vital role in reducing the complexity associated with microservices architecture. Breaking down an application into microservices can lead to a proliferation of services, which can be challenging to manage. However, with the right design patterns, such as aggregator or API gateway, you can simplify service management and improve communication between services.
Distributed Data Management
Microservices often rely on distributed data management, which can be complex. Each microservice has its own separate database to ensure loose coupling and independence. However, managing transactions and ensuring data consistency across services can be challenging. Design patterns like saga and event sourcing can help manage distributed data effectively.
Enhancing Communication
In a microservices architecture, services need to communicate with each other to function correctly. This inter-service communication is usually done via APIs, but it can become complicated as the number of services increases. Design patterns like client-side load balancer and circuit breaker can help streamline this communication and ensure that services can interact efficiently.
5 Key Microservices Design Patterns
The API Gateway Pattern
In a microservices architecture, each service exposes a set of fine-grained APIs. Managing these APIs individually can be a daunting task, especially when your application consists of dozens or even hundreds of microservices. That’s where the API Gateway pattern comes into play.
The API gateway serves as a single entry point for all client requests. It routes requests to the appropriate microservice and subsequently aggregates the responses. It also handles cross-cutting concerns like authentication, monitoring and rate limiting. Furthermore, it provides a unified API that is easier to consume by the client, shielding them from the complexity of the microservices architecture.
However, the API Gateway pattern is not without its challenges. It can become a bottleneck if not properly designed and scaled. Also, it’s a single point of failure unless highly available. Despite these challenges, with careful design choices and good operational practices, the API Gateway pattern can greatly simplify client interaction with microservices.
Database Per Service Pattern
In a monolithic application, all modules typically share a single database. While this approach might seem convenient, it leads to a tight coupling between modules, making it hard to scale and maintain the application. The Database Per Service pattern provides an elegant solution to this problem.
In this pattern, each microservice owns its database, ensuring loose coupling and high cohesion. This allows each microservice to use a database type that is best suited to its needs. Furthermore, it enables independent scaling and evolution of each microservice.
However, implementing the Database Per Service pattern can be challenging. It involves dealing with distributed data management issues, like ensuring data consistency across services. Despite these challenges, the Database Per Service pattern is a powerful tool for achieving data isolation and autonomy in a microservices architecture.
The Circuit Breaker Pattern
In a microservices architecture, services often rely on each other. If a service fails or becomes slow, it can impact all the dependent services, leading to a cascading failure. The Circuit Breaker pattern aims to prevent this scenario.
With the Circuit Breaker pattern, you can prevent a network or service failure from cascading to other services. When a failure is detected, the circuit breaker trips and prevents further calls to the failing service. It then periodically attempts to call the service, and if successful, it closes the circuit and lets the calls go through.
This pattern helps maintain service performance and avoid timeouts during a failure. However, it requires careful tuning to balance between responsiveness and sensitivity to failures. Despite the complexities, the Circuit Breaker pattern is a key pattern for building resilient microservices.
The Event-Driven Pattern
In a microservices architecture, maintaining data consistency among services can be challenging. The Event-Driven pattern provides a solution to this issue.
In the Event-Driven pattern, services publish events when their state changes. Other services subscribe to these events and update their state accordingly. This way, each service can maintain its consistency without the need for synchronous communication.
This pattern enhances the decoupling between services and improves performance by enabling asynchronous communication. However, it can also make the system more complex and harder to understand due to the indirect nature of the interactions between services. Nevertheless, the Event-Driven pattern is a powerful tool for ensuring data consistency in a microservices architecture.
The Saga Pattern
In a microservices architecture, implementing business transactions that span multiple services can be a big challenge. The Saga pattern provides a solution to this problem.
A saga is a sequence of local transactions where each transaction updates data within a single service. If a local transaction fails, the saga executes compensating transactions to undo the impact of the preceding transactions.
While the Saga pattern can effectively manage distributed transactions, it also adds complexity to the system. It requires careful design and coordination between services. Despite these challenges, the Saga pattern is a critical tool for managing complex business transactions in a microservices architecture.
Conclusion
In conclusion, understanding and applying these five key microservices design patterns can help you design more scalable, reliable and maintainable applications. However, it’s important to remember that each pattern comes with its trade-offs and should be applied judiciously based on the specific needs of your application. As you dive deeper into the world of microservices, you’ll realize that these patterns are essential building blocks for developing robust and resilient applications.
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Five Great DevOps Job Opportunities
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:
CareerBuilder.com
Belcan Federal
Anaheim, California
Lead DevOps Engineer
$140,000 to $170,000Dice.com
Trinity IT Services
Remote
DevOps Lead
$115,000 to $165,000SimplyHired.com
EMMES Corp
Rockville, Maryland
DevOps Engineer
$115,000 to $145,000Indeed.com
VyncaCare
San Francisco, California
DevOps Engineer
$110,000 to $140,000LinkedIn
L3Harris Technologies
Colorado Springs, Colorado
Senior DevOps/AWS Specialist
$91,500 to $170,500 -
DevOps 2024: DevOps Isn’t Dead; Long Live DevOps
Well, it has been quite the year in general and quite the year as it relates to DevOps. As I sit here on the cusp of the 10th year of publishing DevOps.com, I look back and recognize how much DevOps has grown, evolved, matured and shifted over time. As was the case 10 years ago, there are those who say DevOps does not work or insist that DevOps is dead. My response:
“They stab it with their steely knives
But they just can’t kill the beast.”
~ Eagles, Hotel CaliforniaNo, it seems you can’t kill DevOps. Much like Obi-Wan Kenobi being struck down by Darth Vader, DevOps, it seems, is more powerful than ever. It has seeped into the very fabric of IT.
While cloud native has become the dominant form of computing, the fact is you really would not have cloud-native stacks without DevOps fundamentals enabling them. As cloud-native continues to grow, so does DevOps.
One of the interesting things about DevOps is the fact that it really has become an enterprise toolset. While initially scaling DevOps across an organization was challenging (and still is for many), it has become much more common to see large enterprises—or ‘horses,’ as Gene Kim would call them—adopting DevOps methodologies across significant parts of their IT teams.
Speaking of Gene Kim, some have pointed to his recent announcement that he’s changing the name of his DevOps Enterprise Summit (DOES) to Enterprise Technology Leadership Summit as evidence of DevOps’ demise. We will be interviewing Gene on Techstrong.TV the first week in January 2024 and will address this with him, so stay tuned. But without giving out too many spoilers, Gene felt that DOES had expanded beyond just DevOps to encompass a general technology discussion (hey, AI will be in there for sure) aimed at technology leaders (C-suite). But rest assured that Gene Kim does not think DevOps is dead.
Looking ahead to 2024, I see DevOps making broad inroads into the SMB and mid-enterprise market. The DevOps toolset and methodology have matured to the point where it can be adopted and used successfully in IT environments not as resource-rich or process-driven as large enterprises generally are.
Another continuing story in DevOps is DevSecOps. Security and/or quality continues to be a key driver for DevOps pipelines, with the software supply chain and SBOM security still driving lots of adoption and adaptation.
Did I mention AI? You can’t discuss much today without mentioning it, or so it seems. AI is a catalyst for automation. Generally, in software development and deployment, where there is automation, you guessed it—there is DevOps.
But here is one out of left field for you: ITSM. It seems like IT service management is making a big comeback (some say it never left). Today’s ITSM incorporates many DevOps fundamentals to increase scale and agility. On this front, stay tuned; in partnership with PeopleCert, the folks behind ITIL, Prince 2 and the DevOps Institute, we will roll out a series of virtual events in 2024, tying all three of these key technologies together.
So, there’s lots going on in the DevOps world for 2024. You can count on us here at Techstrong Group to keep you in the know.
On that note, this will close out our coverage for 2023. To all of our readers, watchers, listeners, friends, sponsors and contributors, a Merry Christmas, Happy New Year, and here is to a healthy and prosperous 2024. -
All Your IT Team Wants This Holiday Season is a Break!
The holiday season is all about giving. As organizations increasingly look to IT as they move toward new digital tools and processes, now is the perfect time to give back to IT teams tirelessly working to keep the modern enterprise online. Whether your system performance has been naughty or nice this year, there’s no denying that tech professionals have earned our appreciation, respect—and the tools to set them up for success in 2024.
For IT teams limited in both time and resources, simply maintaining systems can feel as impossible as squeezing themselves down a chimney or delivering gifts to millions of homes in a single night. On top of that, instead of being greeted with milk and cookies, they’re inundated with endless performance issues, support requests and alerts—leaving little time left over for the important work of innovating.
They say the best gifts are the ones you can’t wrap. That holds true for IT teams, too. This year, bring your organization the gift of a simpler, speedier, more rewarding workload. If your team is dreaming of a tech-savvy future, here are some enterprise software solutions to make their lives easier that they won’t want to re-gift:
Enjoy the View With Observability
Everyone loves to cozy up at home during a winter snowstorm, but with the widespread migration to combined remote, on-premises and distributed hybrid environments, the daily monitoring journey for today’s IT teams is more akin to trekking blindly through a blizzard.
Observability tools are metaphorical snowshoes and goggles that can help them not only weather the storm but see clearly from the mountaintop. Observability is the answer to the modern enterprise’s struggle to gain full visibility into their organization’s apps, networks, databases and infrastructure—something nearly half of IT professionals lack, according to SolarWinds research. IT teams will be able to rest easier at night with visions of sugarplums, rather than outages or anomalies, dancing in their heads.
Even better, integrating artificial intelligence (AI) capabilities into observability solutions to collect and provide data on what’s not performing as expected and why will help your teams take a proactive approach to solving issues.
Lend a Helping Hand With AIOps
AI isn’t just the shiny new toy of the tech world. Organizations using AI for IT operations (AIOps) can give the gift of support to their overworked IT teams by automating some of the time-consuming and mundane tasks that stand between them and a focus on innovation.
Adding AIOps to observability can provide IT teams with maximum visibility into the state of their digital ecosystems through automated discovery and dependency mapping. Additionally, your teams can gain the ability to easily track inbound connections linked across the organization’s application stack and storage volumes with auto-instrumented views.
Today, it simply isn’t feasible for humans alone to manage modern IT environments without intelligent automation. Think of AIOps as a workshop of elves operating in the background to ensure workloads and processes are streamlined and moving as efficiently as possible. With AIOps in place to analyze data and streamline workloads and processes, IT teams are relieved of some pressure—and can focus on accelerating your digital transformation rather than just maintaining it.
Give the Gift of Time
Finally, although you can’t outright give the gift of time to your IT team, you can still arm them with tools that save time year-round. In 2024, more organizations will embrace IT service management (ITSM) to improve their IT outcomes, service delivery and employee satisfaction.
SolarWinds research found that enterprises using AI-powered ITSM tools were able to reduce system downtime by 21% and decrease time spent resolving incident and service requests by 23%. In fact, the average SolarWinds customer reported savings of 23 hours weekly due to reduced ticket volume—nearly the equivalent of adding an employee to the team.
This holiday season, the best gifts you can give your IT teams are the actionable, predictive intelligence and tools they need to optimize performance, cut down on complications and quickly resolve issues. (As a bonus, it will certainly make you more popular than the guy from The 12 Days of Christmas who thought it was a good idea to gift his partner geese, milkmaids and leaping lords.)