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Category: Best Practices

Businesses Must Bridge the Skills Gap to Succeed With Quantum Computing

Quantum computing is poised to transform the way companies solve business problems. The technology needed to power this revolution is becoming a reality, with large developers like IBM making excellent progress toward building quantum-centric supercomputers. But while the technological barrier to deploying quantum is falling away, another obstacle remains: The lack of quantum skills in the labor market.

Last year, Classiq commissioned a survey of over 500 professionals familiar with quantum technology, which highlighted a range of possible benefits that quantum computing will unlock, including cost savings, new revenue streams and competitive advantages for companies that successfully deploy the technology. But the survey also highlighted that the shortage of qualified engineers and scientists who can program quantum computers remains one of the biggest barriers to increased quantum deployments.

This skills gap will not only prevent companies from leveraging the power of quantum computing but also lead to expensive competition for talent as businesses try to recruit the few quantum professionals available in the workforce. Why has this skills gap grown, and what can companies do about it?

Coding for the Quantum Computing Era

First, we need to understand what skills are missing that are contributing to a skills gap, as some readers may ask why existing programmers and computer engineers cannot meet this demand. Besides lacking the purely technical skills needed to code for quantum computing, such as creating quantum circuits and software, there is also a knowledge gap around how to solve business problems using a quantum computer.

For instance, while many programmers trained in classical computing could figure out a way to design a program for portfolio optimization, they likely have no idea how to do the same using a quantum computer. They are unlikely to know where to start, how to check their results or even how to run their code on a quantum device.

Coding for quantum computers is also more complicated than for classical computing. As each “qubit” can be one and zero simultaneously instead of either/or, writing the quantum code is more complex. Developers need to understand quantum concepts such as superpositions, so working in quantum requires new ways of thinking about problems and necessitates new ways of working.

This last issue is compounded by a technology gap, as quantum programming has not been abstracted away as it has for classical programming – it is mostly still at the assembly language level. At this level, writing a few dozen lines of quantum programming is achievable but time-consuming, so creating complex quantum programs of hundreds or thousands of lines of code is almost impossible.

Solving the Skills Gap

The first instinct for companies wishing to leverage quantum technology is to hire their way out of this problem, but there are simply not enough Ph.D.-trained quantum experts in the market, and there is likely to be stiff competition for the few quantum-trained graduates coming out of university. Given the shortage, companies must instead consider how to train and upskill their staff.

Convincing existing staff to take part in this training should not be an issue. Engineers and programmers are keen to learn – nearly 95% of respondents to our survey said they would like to be trained in quantum. Why is there such high demand? Not only is this new technology exciting, but many workers may hope to future-proof their jobs or increase their earning potential through quantum skills.

Upskilling your workforce does not have to be expensive. There is already a wide range of open source material, such as the Qiskit development kit, designed to help people explore this new technology. For companies, a straightforward solution is to give their engineers the time and space to learn about quantum for themselves. Simply giving your existing coders permission and resources to learn about this technology and how it can benefit your business could prove to be a profitable long-term investment.

The added advantage of upskilling your staff, rather than recruiting quantum Ph.D. graduates, is your existing workforce understands the sorts of problems your business needs to solve.

Plus, new products are emerging that can help solve the technology gap and make it easier to code for the quantum era. Sophisticated low-code software can abstract away some of the complexity involved in writing quantum coding, which in turn will speed up the process of learning.

But upskilling is only one thing to consider. Companies also need to invest in the resources and infrastructure around their emerging quantum talent. They need to build a team, identify use cases that can benefit from quantum technology and locate the right vendors and cloud providers that will allow them to test and simulate their quantum programs.

The quantum revolution remains several years away, but companies need to make sure they have the skills and infrastructure ready to take advantage of this technology or risk being left behind by quantum-prepared competitors.

January 15, 2024
5 Time-Saving and Problem-Solving Technologies for DevOps Teams

DevOps has transformed almost every aspect of software development, taking us from the dark ages of big-bang deployments every 12 months to the reality of releasing software multiple times per day. However, DevOps professionals have to overcome several hurdles to successfully implement and sustain the DevOps culture within the organization. Let’s review some of these challenges—and five technologies that can help.

Cultural and Organizational Differences

One persistent challenge for DevOps teams in a mature setting is navigating cultural and organizational differences. These differences often manifest in varied working styles, communication methods and priority management across teams. For instance, development teams might prioritize rapid innovation, while operations teams focus on stability and security, leading to conflicts. Managing these diverging priorities requires constant negotiation and alignment, which can be time-consuming and complex.

Furthermore, as organizations grow and incorporate diverse teams, including remote and international members, ensuring cohesive communication and understanding becomes even more critical. This diversity, while beneficial, brings challenges in terms of aligning on common goals and working practices, often leading to delays and misunderstandings that impede efficient workflow.

Complexity of Automation

In mature DevOps environments, the complexity of automation can become a significant day-to-day challenge. As the infrastructure and application landscape grows, the automation scripts and tools that were once efficient can become unwieldy and difficult to manage. Keeping these automation processes up-to-date with the constantly evolving technology stack is a never-ending task, requiring significant time and resources.

Additionally, the integration of new tools or updates to existing ones often introduces complexity, leading to potential errors and downtime. This complexity not only slows down the deployment process but also requires DevOps teams to invest time continuously in learning and adapting to new tools and methodologies. Ensuring that automation is both robust and flexible enough to handle a dynamic environment is a delicate balance that DevOps teams must constantly maintain.

Cross-Team Collaboration

Effective cross-team collaboration is vital for DevOps teams but can be challenging in day-to-day operations. The interdependence between various teams–such as development, operations, QA and security–requires a high level of coordination, often hindered by differing goals, tools and terminologies. Communication barriers can lead to misalignment on project objectives, causing delays and inefficiencies.

Moreover, in a fast-paced DevOps environment, keeping everyone on the same page regarding updates, changes and issues requires meticulous attention. This is compounded when teams are distributed geographically, as time zone differences and cultural nuances add another layer of complexity. Regular synchronization meetings, clear communication channels and shared tools are necessary, but managing these effectively without causing meeting fatigue or information overload is a continuous challenge.

Consistency and Standardization

Maintaining consistency and standardization across various projects and teams is a significant challenge for mature DevOps teams. As the number of services and microservices increases, ensuring that each adheres to the same set of standards for coding, security and deployment becomes increasingly difficult. Inconsistencies can lead to significant problems in integration, testing, and deployment, slowing down the entire delivery process.

Furthermore, as different teams may prefer different tools and practices, creating a standardized environment that accommodates everyone’s needs without compromising on efficiency or quality is a complex task. Regular audits, effective governance and a balance between flexibility and standardization are essential to manage this challenge, but they require constant effort and vigilance.

5 Lesser-Known Technologies That Can Help

While there is no one technology that can completely alleviate the above challenges, there are several tools that can help. Let’s review five technologies that can make the life of a DevOps team just a little bit easier.

Chaos Engineering Platforms

If you’re part of a DevOps team, you’re probably all too familiar with the panic that ensues when a system fails unexpectedly. Chaos engineering platforms aim to save you from such unforeseen surprises by embracing the philosophy of ‘breaking things on purpose.’

Chaos engineering involves intentionally introducing failures into systems to understand how they respond and to identify potential weaknesses. By doing so, DevOps teams can proactively find and fix issues before they manifest in production, thereby saving significant time and preventing potential downtime.

Platforms like Gremlin and Chaos Monkey offer controlled environments for running these chaos experiments, providing teams with valuable insights and recommendations. In the long term, this can not only save time but also instill a sense of confidence and resilience in your systems.

Application Dependency Mapping

Application dependency mapping (ADM) is a process that helps DevOps teams visualize and understand their applications and their dependencies. It is a crucial technology that aids in quickly diagnosing and rectifying application performance issues, thereby saving time.

ADM provides a holistic view of the application architecture, showing how different components interact with each other. This comprehensive visibility is invaluable during troubleshooting as teams can easily pinpoint problematic areas and rectify them without having to sift through mountains of code.

Furthermore, ADM is crucial when planning for application changes or upgrades. Understanding how changes in one component may affect others helps in preventing potential disruptions, leading to smoother and faster software delivery.

Policy-as-Code Solutions

Policy-as-code is a practice where policy definitions are codified and managed as code files within version control systems. This approach offers a host of benefits that can save time for DevOps teams.

Policy-as-code solutions enable automatic enforcement of policies, eliminating the need for manual checks. This significantly reduces the time taken to ensure compliance and allows teams to focus more on developing and delivering quality software.

Moreover, since policies are managed just like any other code, they can be versioned, tested and reviewed before being deployed. This allows for a more streamlined and error-free policy management process, saving valuable time and effort. Tools like Open Policy Agent and HashiCorp Sentinel are commonly used to implement policy-as-code in DevOps environments.

Service Mesh Technologies

Next on the list is service mesh, a dedicated infrastructure layer for managing service-to-service communication in a microservices architecture. Service mesh technologies offer a range of features that can save time for DevOps teams.

One of the primary benefits of a service mesh is its ability to offload common communication concerns from individual services to the mesh itself. This includes functionalities like load balancing, service discovery, and traffic management. By doing so, it saves DevOps teams the time and effort required to implement these functionalities within each service.

Additionally, service mesh technologies provide detailed observability of the service communication, making it easier for teams to monitor, troubleshoot and optimize their applications. Istio and Linkerd are two common open source options for implementing service mesh.

Codeless Test Automation Tools

Codeless test automation tools represent a significant shift in the way DevOps teams approach software testing, offering a more efficient and less time-consuming method. These tools allow the creation of automated tests without the need to write extensive code, making test automation accessible to team members who may not have deep coding expertise.

Using a graphical user interface, codeless test automation tools let users design tests by simply dragging and dropping elements into a workflow. This approach not only accelerates the test creation process but also simplifies the maintenance and updating of tests as applications evolve. Additionally, these tools often integrate seamlessly with various CI/CD pipelines, ensuring that automated tests can be easily incorporated into existing DevOps workflows.

With the speed at which technology is advancing, it’s crucial for DevOps teams to stay updated on the latest tools and technologies that can help them optimize their processes. The technologies listed in this article can significantly save time for DevOps team and make complex DevOps workflows a little smoother.

January 8, 2024
Best of 2023: Documentation as Code: A Game Changer for DevOps Teams?

As we close out 2023, we at devopsy.kinsta.cloud wanted to highlight the most popular articles of the year. Following is the latest in our series of the Best of 2023.

Documentation-as-code (DaC) is a growing trend in the world of software development. It is a philosophy that treats software documentation in the same way as software code. In other words, it means writing, storing, managing and delivering documentation through the same systems used for code. This approach is becoming an important part of DevOps culture, facilitating efficient and effective collaboration between development and operations teams.

The concept of documentation-as-code borrows practices from software development and applies them to the realm of documentation. Writing documentation-as-code allows us to use tools and processes that are already familiar to us from the software development process, such as version control, automated testing and continuous integration. This results in documentation that is more consistent, up-to-date and faster to produce, all while being stored in a format that is easily readable by both humans and machines.

Documentation-as-code enables a shift-left approach to documentation, where documentation is created and updated early and often. This shift results in more accurate, timely and relevant documentation since it is updated as the code changes. Furthermore, because documentation is treated as code, it becomes a part of the development process rather than an afterthought. This approach helps to bridge the gap between development and operations, making it easier for teams to deliver high-quality software at a faster pace.

5 Principles of Documentation-as-Code

Version Control

One of the key principles of documentation-as-code is version control. Just as with code, documentation changes over time. New features are added, old ones are deprecated and bugs are fixed. Therefore, it’s necessary to track these changes and have the possibility to revert to previous versions if needed. This is where version control comes into play.

Version control systems like Git are perfect for managing documentation. They allow you to track changes, create different versions of documents and merge changes from different contributors. This makes it easier to collaborate on documentation and ensures you always have a history of changes.

Plain Text Formats

Another principle of documentation-as-code is the use of plain text formats. Plain text formats, such as Markdown or reStructuredText, are easy to read and write, and they can be version-controlled and diffed, just like code. This makes it easy for anyone on the team to contribute to the documentation.

Plain text formats are also platform-independent, meaning they can be read and edited on any operating system. This is a huge advantage over proprietary formats like Word, which require specific editing software and may not be compatible across different platforms.

Furthermore, plain text formats can be easily converted to other formats like HTML or PDF for publishing. This means you can write your documentation once in a plain text format and then generate a variety of output formats for different audiences or purposes.

Collaboration and Peer Review

Documentation-as-code promotes collaboration and peer review. Because documentation is stored in a version control system and written in a plain text format, it’s easy for anyone on the team to contribute to the documentation. This means that developers, testers, operations staff and even customers can all contribute to the documentation, resulting in more comprehensive and accurate documentation.

In addition to facilitating collaboration, documentation-as-code also encourages peer review. Just like code, documentation can benefit from being reviewed by others. This can help to catch errors, improve clarity, and ensure consistency. Many version control systems have built-in tools for code review that can be used for documentation as well.

Automated Testing and Deployment

Just like code, documentation can and should be tested. Automated testing of documentation can help to catch errors like broken links, missing images or incorrect formatting. There are many tools available that can automatically test your documentation, and these can be integrated into your continuous integration and continuous deployment (CI/CD) pipeline, just like your code tests.

In addition to testing, documentation can also be automatically deployed. For example, when you push changes to your documentation repository, your CI/CD pipeline can automatically build and deploy the updated documentation to your website or documentation portal. This ensures that your documentation is always up-to-date and matches the current state of your software.

Single Source of Truth

The final principle of documentation-as-code is the concept of a single source of truth. This means that there should be one, and only one, definitive source of information for each piece of documentation. This source should be stored in a version control system, and all published versions of the documentation should be generated from this source.

Having a single source of truth ensures consistency across all versions of your documentation. It also makes it easier to manage and update your documentation, since you only have to update one source, rather than multiple copies.

Benefits of Documentation-as-Code for DevOps Teams

Improved Collaboration

In traditional setups, documentation is often seen as a secondary function; something that’s done after the actual coding. This approach often leads to bottlenecks and miscommunications, with developers and operations teams working in silos.

With DaC, documentation becomes part of the code itself. This means that everyone on the team has a stake in keeping it updated and accurate. It promotes shared responsibility and understanding, fostering a culture of collaboration.

Greater Consistency and Accuracy

Another key advantage of DaC is its contribution to consistency and accuracy. By treating documentation like code, we can use the same tools and practices used for code review and testing to validate our documentation.

This approach ensures that our documents adhere to specified standards and are free from errors. It also enables us to automate the process of checking for consistency in terms, formatting and structure, saving us from tedious manual work and reducing the chances of human error.

Moreover, since the documentation lives alongside the code, it’s easier to keep it in sync with the system’s current state. This ensures that our documentation always accurately reflects the system, eliminating the confusion and inefficiencies that come with outdated or inaccurate documents.

Streamlined Update and Maintenance Processes

DaC streamlines the processes of updating and maintaining documentation. Traditional documentation often becomes outdated quickly, requiring significant time and effort to update.

But with DaC, updates become part of the development process. Whenever code changes, the documentation changes with it. This removes the burden of having to update documentation separately and ensures that it’s always up to date.

DaC Best Practices for DevOps Teams

Here are a few best practices to help you effectively implement documentation-as-code in your DevOps pipeline.

Start Early

One of the best practices to implement DaC is to start early. Begin documenting as soon as you start coding. This ensures that the documentation grows organically with the code and reduces the likelihood of important details being forgotten or overlooked.

Use Lightweight Markup Languages

When implementing DaC, it’s advisable to use lightweight markup languages like Markdown or AsciiDoc. These languages are easy to learn and use, and they allow you to write documentation that’s readable both in its raw form and when rendered to HTML or other formats.

Documentation Reviews and Feedback Loop

Just like code reviews, documentation reviews should be a regular part of your development process. This ensures that the documentation is accurate, comprehensive, and adheres to your team’s standards. Also, incorporate a feedback loop for your documentation. This allows your team to continuously improve the documentation based on feedback from users and stakeholders.

Modularity and Reusability

Lastly, design your documentation for modularity and reusability. Break it down into small, manageable pieces that can be easily updated and reused across different parts of the application.

Conclusion

Documentation-as-code is an invaluable tool for DevOps teams. It improves collaboration, ensures consistency and accuracy, facilitates version control and streamlines the update and maintenance processes. By adhering to the best practices outlined in this blog post, you can harness the full potential of DaC and significantly enhance your team’s efficiency and effectiveness.

January 5, 2024
Best of 2023: 5 Microservices Design Patterns Every DevOps Team Should Know

As we close out 2023, we at devopsy.kinsta.cloud 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.

January 2, 2024
Key Cost Optimization Strategies for Multi-Cloud Environments
In today’s ever-changing technology landscape, businesses are realizing the advantages of multi-cloud environments. These environments allow organizations to tap into various cloud platforms and use their services as required. This change signifies a shift away from relying on just one cloud provider. Businesses now embrace the flexibility and adaptability that come with using multiple clouds simultaneously.

In complex multi-cloud environments, it is crucial to manage costs effectively. It is important to strategically optimize costs while considering your organization’s specific needs. By utilizing tools, implementing processes and continuously monitoring costs, you can save money while still reaping the benefits offered by different cloud providers.

What is Multi-Cloud?

A multi-cloud environment describes a setup in which companies or individuals utilize cloud computing platforms or providers to fulfill their computing requirements. In a multi-cloud setup, organizations have the ability to integrate both public clouds (such as Amazon Web Services, Microsoft Azure or Google Cloud Platform) and private clouds (on-premises or hosted private clouds) to establish a diverse and adaptable computing infrastructure. Rather than relying on one cloud provider, multi-cloud strategies distribute workloads across several cloud environments. Rather than depending on a single cloud provider, a multi-cloud strategy involves distributing workloads across various cloud environments.

Benefits and Challenges

Multi-cloud environments offer organizations numerous benefits, including flexibility, reliability, scalability and cost optimization. When organizations distribute their workloads across various cloud platforms, they reduce the chances of downtime or service disruptions due to a single provider failure. This guarantees uninterrupted availability and minimizes any potential impact on business operations.

It is quite challenging to manage a multi-cloud environment for several reasons. Organizations must tackle challenges related to governance, cost management, integration and compatibility. To overcome these obstacles effectively, it is important to implement a well-defined strategy with efficient management tools.

Since each cloud provider comes with its own set of tools, APIs and policies, ensuring consistent governance across different cloud platforms is challenging. Since different cloud providers have differing pricing structures, it is quite difficult to monitor and optimize costs in a multi-cloud environment.

Analyzing and understanding each provider’s cost model is necessary when tracking usage patterns across different platforms. Integrating services from different cloud providers into a cohesive system architecture can be challenging due to compatibility issues. Careful planning and expertise are needed to achieve seamless integration without conflicts or performance problems.

The Importance of Cost Optimization in Multi-Cloud Environments

In today’s evolving technology landscape, businesses need to make the most of various cloud environments to fully utilize their cloud capabilities. However, great power entails great responsibility and cost optimization is a critical aspect that should not be overlooked.

As businesses expand their cloud presence across multiple providers, optimizing costs becomes necessary to maximize ROI and stay competitive in today’s fast-paced market. In a multi-cloud environment, organizations typically use several cloud platforms simultaneously, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), to reduce expenses and maximize efficiency by comparing the services, features, and pricing models offered by each of the providers.

Multi-cloud environments offer opportunities for cost optimization by allowing organizations to choose the cost solutions tailored to their specific needs. With access to pricing models and options offered by providers businesses can optimize their expenditure based on workload requirements.

Optimizing Costs in Multi-Cloud Environments

To optimize costs in multi-cloud setups, one must carefully analyze and manage expense factors across providers and services. It involves understanding cost structures, pricing models and usage patterns while considering workload requirements to choose cost-effective options for different tasks. Achieving these financial benefits requires efficient resource allocation and minimizing unnecessary expenses.

This section discusses the key strategies for optimizing costs in a multi-cloud environment.

Automating Resource Management

You can optimize costs in a multi-cloud environment by automating the management of resources. Efficiently optimizing costs in cloud environments relies heavily on automating resource management. This enables organizations to monitor usage levels and resources dynamically as required, effectively implement cost-saving policies and ensure optimal utilization of cloud services without the need for human intervention.

Organizations can automate their processes and tasks to streamline their operations, thereby saving time and reducing the risk of overspending on unnecessary resources. To effectively handle resources, it is crucial to have monitoring tools that offer real-time observations on usage patterns and performance metrics. These tools enable businesses to identify underutilized resources, which can then be automatically shut down or adjusted in size as required.

Automation also involves implementing automatic resource allocation capabilities based on demand fluctuations. By scaling up or down depending on workload requirements, organizations can optimize costs by ensuring they only pay for the resources they truly need at any given time. By implementing these automated policies, organizations are able to enforce cost optimization practices. For example, by establishing guidelines for assigning metadata tags to resources, organizations gain visibility and control over expenditure across various cloud platforms.

Additionally, scheduling non-production environments to automatically shut down during off-business hours can significantly reduce costs. By turning off development and testing environments when they’re not in use, companies can avoid paying for resources during periods of low activity.

Analyzing Costs in Multi-Cloud Environments

While analyzing costs in a multi-cloud environment is challenging, it is a crucial strategy for your organization to make the most of its resources and avoid unnecessary expenses. It’s important to understand usage patterns and expenditures to optimize costs effectively. Start by gaining visibility into spending across all clouds. Use cost management tools provided by the cloud platforms or consider third-party solutions that consolidate data. This helps track costs at a detailed level and identify areas of high expenses. Analyze usage patterns to find instances that can be resized or turned off when not needed. By adjusting resources based on demand, overprovisioning can be avoided and unnecessary costs reduced.

Here are the key steps to analyze costs in a multi-cloud environment:
- Identify and categorize workloads
- Track usage patterns and cost
- Analyze the usage patterns of your workloads
- Automate cost optimization
- Incorporate cost analysis into your decision-making process
- Review cost optimization strategy regularly
Use Spot Instances

In multi-cloud environments, using spot instances is crucial for cost optimization. These instances are available from cloud providers at a much lower price than on-demand or reserved instances, leading to substantial cost savings for organizations. With spot instances, you can take advantage of unused or idle capacity on cloud providers’ infrastructure, resulting in substantial cost savings. Being strategic with spot instances provides economical access to compute resources. Cloud providers offer discounted spare resources in the absence of regular customer demand, making it favorable compared to on-demand or reserved instances. Use on-demand or reserved instances for critical tasks while employing spot instances for less critical workloads. To optimize costs, you should leverage on-demand, spot and reserved instances, i.e., a mix of all of them.

Right-Sizing and Efficient Resource Allocation

Rightsizing is a key strategy for cost optimization that reduces costs and enhances performance in multi-cloud environments, leading to substantial savings. One of the most important steps in this regard is capturing comprehensive data on resource utilization (CPU, memory, storage and network utilization). By identifying over-provisioned or underutilized instances, organizations can optimize resource allocation. Automated tools simplify this process by monitoring and recommending adjustments. Consolidating workloads into fewer instances and leveraging virtualization technologies or containerization platforms further improves performance and scalability. Right-sizing and resource allocation are two key techniques that can help optimize costs in a multi-cloud setup.

Here are the key strategies at a glance:
- Analyzing workload requirements
- Selecting the appropriate instance types
- Regularly review and adjust resource allocations based on actual usage patterns
- Implementing auto-scaling and load-balancing mechanisms
- Utilizing resource tagging and monitoring tools for cost visibility
- Right-sizing resources on demand
- Automated scaling strategies
Conclusion

By implementing key strategies such as using reserved instances or savings plans, automating resource management, implementing rightsizing techniques, leveraging spot instances, optimizing data transfer and storage, applying automation and orchestration principles, practicing financial governance and budget management and keeping an eye on future optimizations, businesses can effectively control their cloud spending and ensure they are making the most of their multi-cloud investments while minimizing costs.

However, it’s important to acknowledge that cost optimization in cloud environments comes with its own set of challenges. Managing costs can be quite challenging for cloud environments despite their flexibility and scalability. From dealing with pricing models to facing a lack of visibility across clouds, there are obstacles that need to be overcome.

With careful planning and implementation combined with ongoing monitoring and review processes, organizations can mitigate the challenges and truly harness the power of multi-cloud. Your organization should adhere to the key strategies outlined in this article to take advantage of the benefits of multi-cloud while ensuring optimal costs in their cloud spending.
December 7, 2023
How Generative AI Can Streamline Code Documentation

Let’s be honest—it’s not always that software documentation matches production environments, let alone that documentation exists at all. Too often, developers are pushed to deliver code faster and faster, and documentation often falls under the wayside as a second priority. As such, obscure functions and code libraries often become undocumented and difficult for new developers to comprehend.

Generative AI is having an impact on many areas within the software development life cycle. And documentation is another surefire area that artificial intelligence is set to revolutionize. By running large language models (LLMs) over preexisting codebases, AI could document legacy technologies and potentially even generate documentation in lockstep with new projects.

Many obstacles inhibit documentation practices across modern engineering environments. Below, we’ll check in with the experts to see how AI could advance the status quo. We’ll consider some techniques for using generative AI for documentation, outline the potential benefits and discuss some of the caveats to watch out for.

The Blockers to Current Documentation Practices

Various obstacles within the software development pipeline can hinder a documentation-driven culture. A significant roadblock, until recently, has been upkeeping documentation manually.

“It is almost impossible for organizations to keep the current state of their IT application portfolio documented manually,” said Miten Marfatia, founder and CEO, EvolveWare. Newer agile documentation generation tools can fix this by creating and updating metadata for IT applications automatically, yet they still require some manual intervention, he explained.

In addition to manual documentation standards, other inhibitors can present themselves, such as balancing support for pre-existing documentation for old projects with documentation for new projects. “There is an eternal tradeoff between creating documentation for new features and revising or optimizing documentation for existing features,” explained Sanjay Sarathy, vice president, marketing, Cloudinary. “As companies add more products or existing products become richer, the tradeoff calculus becomes more complex and harder to get right.”

According to Igor Jablokov, CEO and founder of Pryon, organizations suffer from a lack of knowledge visibility, which can hinder development and even become a compounded risk within a highly regulated industry. “Friction abounds—it takes so much effort to document things, and tribal knowledge of all types is seldom properly recorded,” he said. “So, at times, organizations have to restart projects because they don’t know how an existing component works.”

Streamlining Documentation With Generative AI

As one can see, there are many drawbacks to the current state of documentation habits. However, generative AI is set to greatly advance the current manual and error-prone documentation standards. It could help address documentation policies for projects that have been sitting in the queue for too long while also encouraging new developer experiences to emerge.

“With generative AI modeling techniques, the documentation process would become progressively seamless and automated whether an application is being documented for the first time or its documentation is being updated,” said Marfatia. “Generative AI will best apply to documentation and transformation of legacy code.”

“Generative AI can certainly be used to streamline documentation processes,” said Sarathy. “It’s particularly promising and valuable in that it can help accelerate the process of keeping legacy content fresh and accurate.”

While it might not replace manual reviews entirely, generative AI could be used to flag old content, update code samples and help address gaps in the full scope of documentation support, said Sarathy. He likens this to the 80/20 rule: AI can help teams optimize high-trafficked pages while keeping up with the other 80% lower-priority applications.

Last but not least, generative AI presents a new opportunity to create novel experiences with old documentation, bringing some new life back to how projects are shared and understood. “By transforming previous static documents into interactive experiences, an organization can discover contradictions, gaps, and overlapping content,” said Jablokov. “They can unify multimodal assets from audio, images, text and videos that may contain critical regulatory information or lessons learned.”

Techniques for Using AI For Documentation

First and foremost, generative AI is set to benefit the documentation and transformation of legacy code the most since large volumes of data and code are already available for the model to ingest to infer understanding of similar code. So, it might behoove adopters to first trial AI-generated documentation in this scenario.

But what would the actual process look like? Well, speaking generically, it would first involve training an LLM on a large data corpus, which will likely include an enterprise’s software development artifacts like code, documentation, functional specifications, technical specifications, test cases, user stories and a variety of other supporting materials, explained Keith Cox, managing director of application modernization & migration at Deloitte. “Generative AI can be used to utilize foundational knowledge for generating future state system artifacts,” he said. “We can use these foundational legacy elements to create high-value LLMs for the enterprise.”

With this in hand, developers could prototype a desired functionality. But taking this a step further, if generative AI was used to generate a data model, it could also help spur design-first approaches for greenfield development. “Ultimately, generative AI could revolutionize the entire development life cycle,” said Cox, “First by accelerating the generation of design specifications and then potentially for some application scenarios, replacing the end-to-end cycle of application development.”

Yet, for generative documentation to be effective, it will rely on a valuable data corpus and bespoke capabilities that extend beyond off-the-shelf models. “The highest value generative AI models will be those that are specialized with large amounts of samples, targeted to specific industries, market segments or applications of technology,” said Cox.

Benefits of Using AI For Documentation

Generative AI is set to improve documentation and, in effect, speed up future development. LLMs that generate the documentation for legacy applications could be useful for generating functionality requirements for modern applications, too, said Marfatia. Ultimately, generative AI could revolutionize the entire development lifecycle.

In addition to development efficiency gains, the end benefits to the business could easily be improved user experiences that result in higher revenue and profitability. “The content produced by the brand can be updated more regularly to ensure greater accuracy and thoroughness,” said Sarathy. “Better, more accurate content leads to fewer support tickets and faster resolutions.”

Another interesting outcome of adopting AI for documentation generation is helping teams contend with multiple cloud providers and an increasingly diverse array of technology stacks.

“Instead of bouncing between myriad provider sites to support their internal development teams, they could unify all support documentation within a singular experience,” said Jablokov. “This allows them to reduce the risk of errant signals when they interact with the public web, as well as see underperforming vendors in a coordinated way. As a bonus, they could blend in their own internal documentation, so they have a cohesive view of what technologies are used for what workflows.”

That said, AI-driven documentation generation (and all generative AI endeavors, for that matter) should be undertaken cautiously, especially when it comes to the security concerns of the organization and not ingesting intellectual property without permission, said Marfatia.

Final Thoughts

It’s good to document your code—not only for learnability but for security reasons, too. By encouraging a more regular, automated regiment for documentation, technical leaders could enhance service discoverability, reduce zombie endpoints, increase the reusability of internal services, enable partner-sharing capabilities and reap other benefits.

Generative AI presents an interesting opportunity to localize and document preexisting shadow IT, making it more visible to engineers. And it could guide the documentation of greenfield development too. Yet, of course, these models rely on a large data corpus and take effort to implement. As such, generative AI shouldn’t be trumpeted as the end-all cure for every scenario or software development woe.

December 5, 2023
DevOps Best Practices for Faster and More Reliable Software Delivery

Imagine a scenario where teams creating the software and delivery aren’t just passing information but sitting together, brainstorming and solving problems in real time. That’s the core of DevOps. It’s not a one-click software solution, but teams working together to provide a reliable solution for seamless and faster software delivery.

Let’s take an example of an app or software update; users would expect it to work seamlessly. The secret here for that seamless experience is often a well-structured DevOps strategy. DevOps isn’t just about speeding things up, it’s about balancing the need for speed with the need for stability.

According to research, 99% of organizations witnessed a positive impact after implementing DevOps in their business delivery processes. They’re deploying updates far more frequently, their failure recovery is lightning-fast, and they see fewer issues when they launch new features.

Using DevOps for Efficient Software Delivery

DevOps is crucial for organizations looking to resolve the complexities of modern software delivery. It bridges the gap between ‘code complete’ and ‘code in production,’ ensuring that software isn’t just created but delivered swiftly and effectively to the end-user. This approach not only accelerates time-to-market but also enhances product quality and customer satisfaction.

By adopting continuous integration and continuous delivery (CI/CD), automation, and constant feedback, DevOps empowers teams to respond to market changes with agility and confidence. It’s about balancing processes, people and technology that work together to unlock higher efficiency, innovation and success.

Implementing Continuous Integration and Continuous Deployment (CI/CD)

Continuous integration and continuous deployment (CI/CD) are core practices in the DevOps approach, designed to streamline and automate the steps in getting software from development to deployment. CI/CD establishes a framework for development teams that supports frequent code changes while maintaining system stability and high-quality output. This method depends on automation to detect problems early, reduce manual errors and speed up the delivery process, ensuring that new features, updates and fixes are available to users quickly and reliably. Teams should follow several best practices:

• Commit to Version Control Rigorously: Every piece of code, from application to configuration scripts, should be version-controlled. It ensures that any changes can be tracked, rolled back or branched out at any point, providing a solid foundation for collaborative development and deployment.
• Automate the Build for Consistency: Automation is the key to CI/CD. By automating the build process, one can ensure that the software can be reliably built at any time. This automation includes compiling code, running database migrations, and executing any necessary scripts to move from source code to a working program.
• Incorporate Comprehensive Automated Testing: A robust suite of automated tests, including unit, integration, acceptance, and regression tests, should be run against every build to catch bugs early. Automated tests act as a safety net that helps maintain code quality throughout the rapid pace of DevOps cycles.
• Replicate Production in Staging: A staging environment replicates your production environment and is crucial for pre-deployment testing. It should mimic production as closely as possible to surface any environment-specific issues that could otherwise cause unexpected behavior after release.

• Ensure Quick and Safe Rollbacks: The ability to roll back to a previous state quickly is essential. This safety measure minimizes downtime by swiftly reversing failed deployments or critical issues without going through a prolonged troubleshooting process during peak hours.
• Monitor Relentlessly Post-Deployment: Continuous monitoring of performance, user experience, and error rates is crucial for catching issues not identified during testing. This real-time data feeds back into the development cycle, helping to improve the next iteration of the software.

Security Practices in DevOps (DevSecOps)

Security is a fundamental element that must be implemented in software development and operations. Integrating security into the DevOps lifecycle means shifting from a traditional security approach to a DevSecOps where security is embedded in the development process, from design to deployment.

To ensure that security is not sidelined in the DevOps cycle, here are some best practices:
• Embed Security Early and Often: Integrate security tools and practices at the earliest stages of the development cycle. This includes code analysis, threat modeling and security requirements gathering.
• Automate Security Processes: Use automation to scan for vulnerabilities, configuration issues and compliance violations. Integrate automated security tools within the CI/CD pipeline to run automatically with every build.
• Practice Infrastructure as Code (IaC): Manage and provision infrastructure through code, which is version-controlled and reviewed for security like application code.
• Stay Compliant: Keep track of relevant regulatory standards and ensure practices meet or exceed these requirements. Regularly review and audit processes to maintain compliance.
• Threat Modeling and Risk Assessment: Regularly perform threat modeling to anticipate potential security issues and conduct risk assessments to prioritize security efforts.
• Continuous Monitoring and Response: Implement real-time monitoring to detect and respond to security incidents promptly. Have an incident response plan that can be executed immediately when necessary.

Cloud Integration in DevOps

Cloud services offer on-demand resources that can be quickly equipped and scaled to meet the needs of any development project. Integration of cloud services with DevOps provides scalable, flexible and efficient resources that align with the rapid and iterative nature of modern software development. Cloud capabilities allow DevOps methodologies to succeed, providing always available resources that adapt dynamically to varying workload demands.

Maximizing the cloud’s potential within DevOps requires adherence to several best practices:
• Firm Infrastructure: Systems should be replaced rather than altered to maintain consistency. Utilizing pre-configured images for servers and containers enables quick, reliable rollouts.
• Configuration Management: Keeping configurations distinct from application code simplifies the management and scalability of the environment, promoting clear operational boundaries.
• Security and Compliance: Security practices must be integrated into cloud operations. Tools provided for identity management, data encryption, and regular compliance audits are critical for maintaining a secure environment.
• Cost Management and Optimization: Active monitoring and reporting on cloud expenditures are essential. Resources should be optimized based on demand, and reserved instances should be considered for predictable workloads to manage costs effectively.
• Continuous Monitoring and Improvement: Persistent monitoring of cloud resources and applications is crucial. Insights from monitoring guide informed decisions on enhancements, assuring availability and optimal performance.
• Disaster Recovery and High Availability: Cloud infrastructure should be architected to withstand failures. Strategies like multi-region deployment and automated failovers guarantee continuous application availability and resilience.
• Performance Tuning: Ongoing evaluation of cloud resources and workloads is necessary for performance optimization. Features like scaling and load balancing are necessary to meet performance benchmarks.

The Role of Testing in DevOps

Testing in DevOps is a practice that integrates into every stage of software development and deployment. Continuous Testing within DevOps ensures that every change made to the codebase is automatically tested, reducing bugs and improving quality. This approach helps maintain a steady pace of delivery while protecting the reliability and stability of the software. Adopting strategies that enhance the efficiency and effectiveness of testing processes is critical for DevOps’ success.

Here are some strategies to achieve this:
• Automate Testing Wherever Possible: Automation is critical to maintaining the speed and consistency required in DevOps. Prioritize the automation of regression, integration, and performance testing to catch defects quickly.
• Leverage Service Virtualization: Use service virtualization to simulate components that are unavailable or still under development to allow testing to proceed without delays.
• Parallelize Test Execution: Run tests in parallel where possible to reduce the time required for the testing process. Cloud-based solutions can be particularly effective for parallel test execution.
• Shift-Left Testing: Integrate testing early in the development cycle to identify and fix issues sooner, which is more cost-effective and reduces time to market.
• Incorporate Testing into the Build Process: Embed testing into the build and deployment pipeline to ensure every build gets tested and issues get identified before they reach production.
• Maintain Test Data Management: Implement tools and practices for managing test data effectively, ensuring that tests can access up-to-date and relevant data sets.
• Regularly Review and Update Test Suites: Keep test suites up to date with application changes to ensure they remain relevant and effective at identifying new and existing defects.

Conclusion

DevOps will become even more ingrained in the software delivery process, with advancements in AI and machine learning likely introducing unprecedented levels of automation and predictive analytics. As technology evolves, integrating DevOps practices into current business models will be crucial. For many organizations, this means seeking expert DevOps consultation to navigate the complexities of modern software delivery and to tailor these practices to their unique challenges and goals. As DevOps evolves, it will continue to pave the way for faster, more reliable and more secure software delivery that keeps pace with the demands of the changing digital landscape.

November 30, 2023
Can Unlocking Joy Unlock Developer Productivity?

Software development has become more complex in the last 10 years with the introduction of the cloud, containerization and microservices – not to mention increased attention to security and governance. All of which falls on developers to manage.

On the surface, this seems like a sensible way to keep engineering organizations lean. But it comes with a price. The increased cognitive load combined with less time for actual coding turns what should be an exciting job into a series of chores.

As CTO of Atlassian, my goal is to build a world-class engineering organization. I want our team of 5,000+ developers to be productive, sure. But more than that, I want them to experience the joy of creation and a sense of pride in their work.

So, my leadership team and I made a strategic bet. We wagered that unlocking developer joy would, in turn, unlock productivity. It’s paying off so far, and I believe it’s an approach that can work for any engineering organization.

Where is the Love?

To be clear, the goal isn’t joy for its own sake. It’s joy in the service of improving productivity across the engineering department. Because it’s not as if developers are inherently lazy, they want to perform at a world-class level. So what’s getting in their way?

Unsurprisingly, our devs knew exactly what was slowing them down: Brittle systems in need of some major TLC, lack of time to fix them and fundamental best practices that had fallen by the wayside. It was papercut after papercut, sapping the energy from even our most enthusiastic engineers.

With that in mind, we looked for ways to remove friction so our devs could stay in their flow state longer. We ultimately arrived at a three-part framework that has proved successful. In concert, we defined baseline metrics for overall developer satisfaction, the time between commit and deploy, pull request cycle time, self-serve documentation and self-serve dependency maintenance. You can’t improve what you don’t measure, after all.

Part One: Provide Awesome Tools

Even before we finalized our baseline metrics, we knew that cycle times for deployments and pull requests were too long. Other issues we identified stemmed from macro-level changes across the industry. Like the fact that increasingly a lot of software is assembled from existing components, we believe this trend will only increase over time.

Most SaaS products, Atlassian’s included, comprise hundreds of microservices, libraries and other components woven together with fresh code. If you need to get up to speed on an unfamiliar microservice, it can take hours to locate the documentation or wait for a colleague several time zones away to come online.

We built Compass to solve this. It removes friction by establishing a self-serve catalog of all software components, including who owns each one, changelogs and health-check metrics. Other work in this area includes introducing new automation in the pull request process, migrating to new test libraries, and external service mocking to enable testing against upstream services within a local dev environment.

We’ve set incremental goals with milestones every few months to maintain a sense of progress and momentum. As of now, we’re tracking toward a 90% reduction in the time it takes for changes in shared services to make their way into individual products.

Part Two: Empower Teams to be the Change They Seek

Our developers were well aware of the issues slowing them down but didn’t feel they had permission to spend time fixing them. So my leadership team, with support from Atlassian’s co-founders, told our development teams to use 10% of their time removing speed bumps.

For example, before our developer joy initiative, less than 12% of the Confluence team’s pull requests reached production within 48 hours. Through a combination of restructuring their repositories, improving test efficiency, and deployment pipeline automation, they bumped that number up to 31% in just one quarter.

Similarly, the Trello team eliminated 24,000 lines of obsolete code and adjusted their branching model to reduce code freezes. Another team increased their deployment frequency by 300% by eliminating manual approvals for low-risk changes.

These are just a few wins we’ve scored by granting teams more latitude and asking them to explicitly set time aside to fix things.

Part Three: Foster an Amazing Engineering Culture

We want Atlassian Engineering to be known for clean code, an ownership mindset when it comes to quality and strong, agile practices.

On the coding side, I thought back to my days of working on the Windows NT operating system. The code was rich with comments and supporting documentation that let me understand it on the very first read. That’s not the case with all of Atlassian’s code, so we’re in the process of modernizing our code base and documentation.

We’re also building a framework for engineering standards. This includes guidance that helps engineers write the right tests at the right time, as well as pulling considerations like security and compliance upstream. We want secure and compliant code from the start so we have fewer rewrites on nearly complete features.

An amazing engineering culture also creates space for developers to learn, experiment and share. To that end, we’ve run hackathons focused on developer productivity and started a monthly internal newsletter showcasing the progress we’ve made. We’re also strengthening our “demo culture” with a weekly showcase where teams present projects they’re working on, exchange ideas and generally catalyze innovation.

Continuous Improvement for the Win

Developer joy and its side effect, developer productivity, is now part of the everyday conversation within Atlassian engineering. It’s also a company-level OKR, complete with funding and a central team that takes on department-wide projects and then rolls them out to the teams.

A year in, our teams are noticeably stronger, moving faster and have more room to test out new tech. (We’re especially excited about our experiments with AI tools like coding assistants and all the opportunities to build on top of our AI platform.) And we can see this in the data. At the start of this initiative, only 50% of our developers reported being satisfied with their work environment. Now, our internal surveys show that number hovers around 70%.

A code base is like a human body; if you take care of it, it’ll take care of you. If you neglect the upkeep, however, eventually, it’s going to break in very painful ways. This isn’t just a project – it’s a lifestyle. There will always be a next step to take. I couldn’t be happier that we’re on the path toward greater developer joy and have come this far in such a short time.

November 27, 2023
FinOps Foundation Previews FOCUS to Help Optimize Cloud Costs

The FinOps Foundation has made available a preview of version 1.0 of a FinOps Open Cost and Usage Specification (FOCUS) that promises to normalize cost and usage data to make it simpler to optimize cloud computing costs.

Udam Dewaraja, chairperson for the FOCUS project, said normalizing schema and terminology for cost and usage data in a vendor-neutral way will make it simpler for IT teams to understand the true cost of cloud computing services and enable them to make comparisons based on value more easily.

The most recent release of the specification adds support for both software-as-a-service (SaaS) applications along with open source data converter implementations and data validators. It also enables use cases for discount analysis, unit pricing, allowing rate transparency and detailed usage analysis while enabling the overlying business context information using tags and resource metadata.

FOCUS 1.0 will also include a SQL-compatible library, with more than forty commonly performed FinOps use case examples tied to FOCUS data outputs curated by experienced FinOps practitioners.

The FOCUS project has the support of Amazon Web Services (AWS), Microsoft, Google and Oracle along with organizations such as CapitalOne, Walmart, Workday and Goldman Sachs that consume cloud services.

Today, many organizations find it challenging to assess the true cost of cloud computing services. That lack of transparency often results in fewer workloads being moved to the cloud simply because no one is certain what their monthly billing cost might be, noted Dewaraja.

Many organizations today engage consultants to analyze existing and potential cloud computing costs that can vary widely from one month to the next. FOCUS should make it simpler for IT leaders to make informed cost decisions as application workloads are being deployed in highly dynamic IT environments, noted Dewaraja. Ultimately, the goal is to bake cost analysis capabilities directly into DevOps workflows, he added.

It’s not clear how quickly organizations are embracing FinOps as a best practice for containing cloud computing costs, but in the face of ongoing economic headwinds, pressure on IT teams to reduce the total cost of IT is rising. In many cases, developers have been allowed to provision cloud resources on demand with minimal supervision, resulting in spiraling costs as more workloads are deployed in cloud computing environments.

As IT organizations increasingly employ multiple cloud platforms to run different classes of workloads, understanding the true cost of choosing to use one cloud over another is becoming more important. At the same time, IT organizations have a greater appreciation for the need to move workloads between platforms that might enable them to reduce costs. Just because an application workload was created in the cloud doesn’t mean that workload should be deployed there. The challenge IT organizations arguably face going forward will be integrating streams of financial data into DevOps processes to make more economically informed decisions about how to allocate their cloud resources in real-time.

November 22, 2023
A Practical Guide to Mitigating DevOps Backlog

Every company aspires to get the first-mover advantage in its respective industry. This strategy allows companies to establish a robust brand presence and offer strong product offerings in the market. However, at times, this may also lead early-stage startups to prioritize pushing product features to market without adequate consideration toward stabilizing and maintaining the product. The above-mentioned approach may help win business and keep customers satisfied; achieving excellence in DevOps still remains an elusive goal. From operational issues to the lack of focus on streamlining DevOps, several factors add up to the already existing pile of DevOps backlog.

According to the Puppet DevOps report 2023, nearly 80% of organizations remain in the middle of their DevOps journey, experiencing varying levels of success within the team level but not across the entire organization. And, one of the areas that could hinder an organization’s success is the accumulating pile of DevOps backlog.

What is DevOps Backlog?

In typical software development teams, the primary focus is often on crafting functional and groundbreaking solutions, which is understandable given the constant drive to meet strict deadlines and deliver exceptional products. However, this relentless cycle of prioritizing outputs can inadvertently relegate the development process to a lower priority, resulting in the accumulation of tasks and issues contributing to the DevOps backlog.

DevOps Backlog consists of user stories, bug fixes, technical tasks, or any other work required in supporting and sustaining the development, deployment and maintenance of a product. It is dynamic and changes with time as and when new tickets get added to the list.

Key Factors Fuelling DevOps Backlog

Managing a substantial DevOps backlog can be one of the most complex challenges for companies to overcome. Within the CI/CD pipeline and the collaboration of multiple teams, intricate system architectures and more, several potential drivers contribute to the growth of the DevOps backlog. Let’s explore some of them:

Relying on a Small DevOps Team: In many cases, DevOps teams consist of a limited number of specialists who act as the lynchpin, holding various processes together. Larger development teams often rely on these smaller DevOps teams, resulting in dependencies that lead to blocked pipelines and delayed handovers, further adding to the DevOps backlog.

Architecture Changes: As development progresses and new services are introduced, architectures naturally evolve and change. This constant evolution necessitates new tooling and infrastructure requirements. Adapting to these changes and aligning new tools and frameworks with the evolving architecture contribute to the pile of the DevOps backlog.

Adapting to Changing Business Requirements: Business requirements are dynamic and evolve over time, adding complexity to the DevOps backlog. For instance, if your organization operates as a software-as-a-service (SaaS) provider or in the healthcare industry, you may need to establish new environments tailored to specific countries, regions, or regulatory frameworks. Managing these new environments and ensuring compliance with relevant regulations adds to the workload of the DevOps team and adds to the DevOps backlog stockpile.

Ticket Overflow: In many scenarios, developers do not have direct access to DevOps toolchains, resulting in an abundance of developer tickets that need to be processed by the DevOps team. This flood of tickets significantly reduces the bandwidth and efficiency of the DevOps team, impeding their progress in addressing the backlog.

Mitigating DevOps Backlog: Prioritizing Essential Strategies

While collaborating with companies to establish DevOps standardization, we initially assumed that the DevOps backlog challenge was limited to a select few. However, we were surprised to discover that even well-established companies in the later stages of product development struggle with significant DevOps backlogs without strategic interventions. Our conversations with over 200 organizations convinced us that the DevOps backlog is a prevalent issue faced by most companies today.

A Lack of Focus on Proactive Approach

The symbiotic relationship between business growth and technological advancements is undeniable. As time progresses, the architecture inevitably becomes more intricate, resulting in a growing number of tasks and challenges within the DevOps backlog. The complexities surrounding DevOps resonate with numerous companies, yet the proactive approach to addressing them remains an issue.

At our organization, we firmly believe that discussions revolving around DevOps stories are vital for organizations aiming to tackle this critical issue head-on. Now, let’s delve into the top 10 stories that software development teams can incorporate into their backlog to strike a balance between prioritizing software development and implementing essential process improvements.

1. Automating Provisioning and Configuration of Resources and Infrastructure
Proper provisioning and configuration of resources and infrastructure are vital for software development and deployment teams. By automating these processes and leveraging the appropriate toolchains and metrics, teams can streamline resource management, fostering consistent and efficient deployment practices. This, in turn, leads to enhanced productivity and improved overall development efficiency.

2. Application Life Cycle Management
Application life cycle management encompasses the comprehensive management of an application throughout its entire life cycle, from development to deployment and beyond. Teams that engage in discussions surrounding these stories take the necessary steps to streamline the entire life cycle. By leveraging toolchains and implementing proficient workflows, these teams achieve heightened productivity and maintain consistency across the application’s lifespan.

3. Release Management and Strategies
Stories related to release management and strategies go beyond mere release scheduling. They encompass crucial aspects such as zero downtime deployments and the implementation of effective rollback strategies. By prioritizing these stories, development teams can ensure smooth and seamless deployments, minimizing disruptions for end-users and optimizing the user experience.

4. Observability
Observability is the key to implementing effective monitoring practices for applications. This involves setting up alerts, capturing relevant metrics and configuring dashboards to track application performance and resource utilization. Prioritizing observability empowers teams to proactively identify and resolve any issues within the DevOps pipeline, ensuring a stable and reliable environment for continuous improvement.

5. Database Management
Database management entails activities such as upgrading and migrating databases, implementing scheduled backups and defining restoration policies. A robust approach to database management ensures the stability and reliability of the data infrastructure, mitigating the risks of data loss or downtime. By prioritizing this story, teams can safeguard their valuable data assets and maintain operational continuity.

6. Access and Permissions
Secure access and permissions are essential for developers across different environments, including testing, staging and production. This story emphasizes the importance of maintaining a transparent audit trail, identifying and rectifying permission leaks and ensuring compliance with security standards. Prioritizing access and permissions not only promotes a secure development environment but also minimizes the risk of data breaches or unauthorized access.

7. Environment Creation and Management
Establishing and managing environments is a complex and time-consuming process that requires collaboration among various stakeholders. From development to quality assurance (QA), pre-production and production environments, meticulous configuration and provisioning are paramount. By addressing this crucial need, teams can ensure well-prepared environments, minimizing discrepancies that may arise during the development and testing stages.

8. Security and Compliance
Security and compliance are fundamental elements that should be considered when designing and implementing an architecture. Adhering to the standards and regulations defined by the geographical countries of deployment is crucial. Neglecting this aspect could result in substantial fines, vulnerabilities and the need for extensive damage control in the future, wasting valuable resources. Therefore, prioritizing security and compliance is imperative for long-term success.

9. Cloud Cost Visibility and Optimization
The dynamic nature of cloud computing necessitates efficient monitoring and optimization of costs, striking a delicate balance between frugality and system performance. Teams must possess comprehensive visibility into cloud cost usage to identify areas of potential savings. By scrutinizing resource allocation, assessing utilization patterns and implementing cost management strategies, organizations can minimize expenditure without compromising the functionality and stability of their applications.

10. Exploring New Tools and Frameworks
It is imperative for organizations to continually explore new tools and frameworks that help elevate software development practices. By implementing zero-trust networks and network segmentation, teams can fortify their security measures, bolster network resilience, and proactively mitigate potential risks. By remaining at the forefront of technological advancements, teams harness the power of these transformative tools and frameworks to optimize overall efficiency and propel innovation to new heights.

Intervene Now to Tackle DevOps Backlog

Prioritizing product features over process can lead to complex issues in DevOps backlog. Solving this problem requires a major shift in mindset and a proactive approach toward mitigating DevOps backlog. Whether your DevOps backlog is in the initial stage or has reached a later stage where it is getting more complex, the time to intervene is now.

November 6, 2023