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Category: Doin’ DevOps

Navigating the Maze of SSDLC Models: A DevOps-Centric Analysis

In the rapidly evolving world of software development, the integration of security into the software development life cycle (SDLC) is no longer a luxury but a necessity. The concept of secure SDLC (SSDLC) has become pivotal in ensuring the creation of software that is not only functional and efficient but also secure from the ground up. With a plethora of SSDLC models available, such as Microsoft’s security development lifecycle (SDL), the OpenWeb application security project’s software assurance maturity model (OWASP SAMM), and others, it’s crucial for organizations, especially those embracing DevOps, to understand their distinct characteristics. This article provides an insightful comparison of these models, highlighting their strengths, weaknesses and applicability to various project types, underpinned by relevant statistics and research findings.

Microsoft’s Security Development Lifecycle (SDL)

Microsoft’s SDL, introduced in the early 2000s, is a security assurance process designed to reduce software vulnerabilities and address compliance requirements. A notable aspect of SDL is its emphasis on security training for developers, a practice that has been shown to reduce the introduction of security flaws by up to 30%. The SDL framework includes security-focused activities such as threat modeling, code reviews and security testing throughout the SDLC.

Strengths: SDL’s structured approach is well-suited for large-scale, complex software projects. Its comprehensive documentation and detailed guidelines are beneficial for organizations seeking a thorough security framework.

Weaknesses: The extensive nature of SDL can make it cumbersome for smaller projects or teams that adopt rapid development cycles typical in DevOps environments. Its prescriptive nature may also stifle agility.

OWASP’s Software Assurance Maturity Model (SAMM)

OWASP SAMM offers a flexible and prescriptive approach to integrating security into software development, making it particularly attractive for organizations adopting DevOps methodologies. SAMM focuses on continuous improvement and can be tailored to an organization’s specific needs.

Strengths: SAMM’s flexibility allows it to be easily integrated into various development processes, including Agile and DevOps. It supports incremental implementation, a critical factor given that 60% of companies are adopting some form of DevOps practices.

Weaknesses: Its less prescriptive nature can be a double-edged sword, potentially leading to inconsistencies in implementation. Organizations without a strong security background may find it challenging to interpret SAMM’s guidelines effectively.

Comparing SDL and SAMM in a DevOps Context

When viewed through a DevOps lens, SDL and SAMM cater to different organizational needs. SDL’s structured approach is ideal for large enterprises with well-established development and security processes. However, its rigidity may clash with the dynamic, iterative nature of DevOps. In contrast, SAMM, with its adaptability and emphasis on continuous improvement, aligns more closely with the principles of DevOps. It allows for faster integration and adaptation in a fast-paced development environment.

Other Notable SSDLC Models

Beyond SDL and SAMM, other models like the V-Model, Agile SDL and BSIMM (building security in maturity model) also offer unique perspectives. For instance, the V-Model’s linear and straightforward approach is well-suited for projects where requirements are well-defined and unlikely to change, a scenario less common in DevOps-focused organizations. Agile SDL integrates security practices within Agile methodologies, making it a viable option for teams that balance rapid development with security considerations. BSIMM, based on real-world data from over 100 organizations, provides a benchmark for companies to measure their secure SDLC practices against industry standards.

Tailoring SSDLC to Project Needs

A one-size-fits-all approach does not work in the realm of SSDLC. The choice of model depends largely on the organization’s size, the nature of the project, compliance requirements and the existing development methodology. For instance, one survey indicated that 45% of small to medium-sized enterprises prefer flexible models like SAMM due to their limited resources and need for agility.

The Role of Automation and Tooling in SSDLC

Irrespective of the chosen model, the integration of automation and tooling plays a pivotal role in the successful implementation of SSDLC in a DevOps environment. Automated security testing tools, for example, can identify vulnerabilities early in the development cycle, a practice that has been shown to reduce the cost of fixing security issues significantly.

Evolving Security Challenges in the DevOps Era

As the software development landscape continuously evolves, so do the security challenges, especially in the context of DevOps. DevOps practices, which focus on integrating development and operations to enhance agility and efficiency, also bring unique security considerations. The rise of microservices architectures and containerization, for instance, has introduced complexities in managing security at scale. Data suggests up to 70% of organizations using microservices have faced security incidents due to misconfigurations.

In this context, SSDLC models must evolve to address these new challenges. For example, incorporating security into container orchestration tools and ensuring secure service mesh configurations are becoming increasingly important. Furthermore, the integration of security into continuous integration/continuous deployment (CI/CD) pipelines, a hallmark of DevOps, demands that SSDLC models be adaptable enough to automate security checks without impeding the speed of deployment.

Bridging the Gap Between Security and Development

A critical aspect of SSDLC in a DevOps environment is bridging the gap between security and development teams. Traditionally, these teams have operated in silos, often leading to security being considered late in the development process. This misalignment can be costly. Research has reported that identifying and fixing security vulnerabilities post-deployment can be 100 times more expensive than addressing them during the design phase.

To mitigate this, modern SSDLC models are increasingly advocating for a ‘shift-left’ approach, where security considerations are introduced earlier in the development process. This approach not only reduces the cost and effort associated with addressing security issues but also fosters a culture where security and development goals are aligned. Tools like automated security scanning and threat modeling are instrumental in this regard, enabling developers to identify and rectify security flaws early.

The integration of SSDLC into DevOps is not just about selecting the right model but also about adapting to the evolving landscape of software development and cybersecurity. By understanding and addressing the unique challenges posed by DevOps practices, organizations can create a more secure and efficient development environment. Whether adopting Microsoft’s SDL for its structured approach or OWASP’s SAMM for its flexibility, the key lies in tailoring the SSDLC model to fit the organization’s specific needs, culture and project requirements. As we move forward, the convergence of security and development will continue to be a cornerstone in the journey towards creating resilient, secure software.

Conclusion

The landscape of SSDLC models is diverse, each with its strengths and weaknesses. For organizations embracing DevOps, the choice of SSDLC model must align with their culture of rapid development and continuous delivery. Microsoft’s SDL offers a robust and structured approach, ideal for complex, large-scale projects. OWASP’s SAMM, on the other hand, provides the flexibility and adaptability needed in a fast-paced DevOps environment. Ultimately, the decision should be guided by the organization’s specific needs, project characteristics and security objectives, ensuring that security is ingrained in the software from inception to deployment and beyond

March 15, 2024
Machine Learning in Predictive Testing for DevOps Environments

In today’s fast-paced technological world, DevOps has become an integral part of software development. It emphasizes collaboration, automation, continuous integration (CI) and continuous delivery (CD) to improve the speed and quality of software deployment. Predictive testing is a key component in this landscape, where machine learning (ML) plays a pivotal role.

By leveraging ML algorithms, predictive testing can forecast potential issues, automate test processes and optimize testing strategies, thus enhancing the efficiency and reliability of DevOps practices.

Machine Learning: Transforming Testing Strategies

ML has revolutionized traditional testing methods by introducing adaptive algorithms capable of learning from data. This shift allows for dynamic test case generation, real-time analysis and predictive analytics.

A study by Gartner predicts that by 2025, AI-powered testing will have reduced the time required for test generation and execution by 70%. Such advancements signify a major leap in testing efficiency, paving the way for more sophisticated and reliable software solutions.

Predictive Analytics and Risk-Based Testing

Predictive analytics in testing involves analyzing historical data to anticipate future problems. This approach prioritizes testing efforts based on the likelihood and impact of potential failures.

For instance, a report by Capgemini highlights that predictive analytics can improve defect detection rates by up to 45%. DevOps teams can allocate resources more effectively by focusing on high-risk areas, ensuring that critical issues are addressed promptly.

Optimizing Test Automation with ML

ML algorithms excel in optimizing test automation strategies. These algorithms can adapt and improve test scripts by continuously learning from new data, reducing manual intervention.

According to a survey by the State of DevOps, organizations that adopt ML for test automation achieve a 45% higher change success rate. This improvement accelerates the development cycle and reduces the chances of errors slipping into production.

Continuous Feedback and Learning

One of the cornerstones of DevOps is continuous feedback and improvement. ML facilitates this by constantly analyzing testing outcomes to refine and enhance testing strategies.

A recent study by Forrester found that continuous testing with ML integration can reduce the feedback cycle by up to 80%. This rapid feedback mechanism allows teams to identify and rectify issues swiftly, ensuring a robust and reliable software delivery pipeline.

AI in Software Testing: A Game-Changer

The integration of AI in software testing is a game-changer for DevOps environments. AI algorithms can analyze vast amounts of data from various stages of the development process to identify patterns and anomalies. This capability enhances the ability to predict failures and automate responses.

An IBM study reveals that AI-enhanced testing can increase defect detection rates by up to 30%, significantly improving the overall quality of software products.

Enhancing Collaboration and Communication with ML in DevOps

The synergy of ML with DevOps extends beyond just testing; it significantly improves collaboration and communication within teams. ML-driven analytics tools can monitor and analyze communication patterns, identifying bottlenecks and areas for improvement. For instance, using natural language processing, ML can categorize and prioritize issues based on urgency and relevance, streamlining the decision-making process. This results in a more cohesive and efficient workflow, where teams can anticipate challenges and coordinate efforts more effectively.

Research by McKinsey suggests that teams leveraging AI and ML in their communication strategies see a 20% increase in project delivery speed.

Predictive Maintenance in Software Deployment

Predictive maintenance, a concept borrowed from the manufacturing industry, is now gaining traction in DevOps. By applying ML algorithms to monitor the health of software systems, DevOps teams can predict and prevent potential system failures before they occur. This approach shifts the focus from reactive to proactive maintenance, minimizing downtime and improving user satisfaction. For example, ML can analyze logs and performance metrics to identify patterns indicative of future issues.

Ethical Considerations and Best Practices in ML-Driven DevOps

As ML continues to permeate DevOps environments, ethical considerations and best practices must be addressed. It is crucial to ensure that ML algorithms are transparent, unbiased, and aligned with ethical standards. DevOps teams must be aware of data privacy concerns and the potential risks associated with automated decision-making. Establishing guidelines for ethical ML use in DevOps is essential for maintaining trust and integrity in the software development process. Industry leaders like the IEEE have begun to draft ethical guidelines for AI and ML in software development, emphasizing the importance of responsible and ethical AI deployment in DevOps.

The Future of DevOps: AI-Driven Testing

Looking ahead, the role of ML in predictive testing is set to grow exponentially. As algorithms become more sophisticated, they will offer deeper insights and more accurate predictions, further streamlining the DevOps process. The integration of AI and ML in testing is not just a trend but a fundamental shift in how we approach software quality and reliability in DevOps environments.

As we progress, the convergence of AI and ML with DevOps practices will continue to evolve, unlocking new possibilities and setting higher standards for software development and deployment.

March 12, 2024
What is RISC-V and Why Has it Become Important for Java?

RISC stands for reduced instruction set computer, and V points to its fifth release in 2015. RISC-V is the new processor architecture to watch out for. Arm-based processors, which stayed among the most popular ones in recent years, may capture a significant share of the semiconductor market, and this is why.

Arm and RISC-V are a type of processor architecture. Another well-known processor architecture type is x86.

Arm and RISC-V chips are used by various industries, from tablets to servers, but initially, they targeted the embedded market. Their design specifics allow them to offer sufficient performance while keeping power consumption low.

RISC-V is a novel open source instruction set architecture (ISA) based on the reduced instruction set computer (RISC) model. The project started at the University of California, Berkeley and was taken over by the RISC-V Foundation in 2015.

Using the x86 and arm processors for hardware development implies high fees because you need to pay royalties to the providers of processor IP, making production expensive and delivering bureaucratic complications. The RISC-V project brought liberation in this sense, creating a new model based on the open-source approach, allowing users to use these processors free of charge. In short, RISC-V is getting more and more popular thanks to its improved design and open-source approach to its development.

RISC-V consists of a set of basic instructions. These instructions act as a communication bridge between hardware and software. RISC-V includes 47 instructions, and additional extensions can be added in a modular way. Thanks to that, RISC-V is smaller compared to x86 or ARM.

In contrast to other popular processors, RISC-V is open source, and therefore, anyone can design, manufacture and sell RISC-V chips and software. As such, RISC-V established new opportunities for modern computing devices, from mobile phones to supercomputers and even microchips for the space industry. Space industry technologies are very challenging as they operate in very different environmental factors. Consequently, the recent NASA announcement on selecting SiFive RISC-V as the core CPU for the high-performance spaceflight computing (HPSC) processor is a great example of the power of RISC-V architecture.

RISC-V belongs to so-called “disruptive technologies.” It removes drawbacks of older systems, eliminating complexity together with licensing fees,, as a result delivering new levels of software flexibility and performance and reducing costs of application development.

According to the RISC-V market report by SHD Group, RISC-V SoCs are forecasted to reach $92.7 billion by 2030, representing a 47% annual growth rate. So, we are still in the early stages of adaptation. Considering that RISC-V can be used as a key ingredient for anything from a smartphone chip to advanced processors for artificial intelligence, its market share could grow very quickly.

It should be noted that Asia is a step ahead in using RISC-V architecture today, with Huawei and Alibaba leading the game. But we also see increasing involvement from companies such as Google, IBM and others. At the same time, the early adoption of RISC-V has already caused a political debate on the microchip trade policy between the U.S. and China.

Java on RISC V

Java has always been at the top of the list for enterprise coding languages, and it is also one of the most widespread languages for embedded systems.

However, applying Java to embedded systems requires adapting it to their technical requirements, which is why Oracle and several OpenJDK vendors have created a special edition of Java for embedded systems. Java runtime for embedded should be compiled into the native ISA for the target hardware, supported by highly tailored, relatively small runtime packages, able to execute on multiple processors if needed for capacity expansion.

RISC-V presents a new type of hardware and needs an adapted version of Java too. So, the Java community initiated the RISC-V port, which was successfully integrated into JDK 19.

However, even with a port in place, you still need to build and test the JDK binary correctly to use it with RISC-V. Today, many OpenJDK vendors and Linux distributions look to offer packages for RISC-V, and gradually, most of the vendors will be delivering JDK packages adapted to RISC-V. But it is great to be among the first. BellSoft released Liberica JDK builds for Linux on RISC-V for JDK 21 LTS. BellSoft commits to supporting version 21 until 2032, providing users with all the benefits of an open source Java runtime from a leading OpenJDK contributor. This includes quarterly CPU and PSU updates and additional solutions for Java development. The RISC-V support in Liberica JDK comes with a Standard JDK flavor, offering three VMs: Server VM, Client VM and Minimal VM designed specifically for systems with lower performance so that your Java applications start up faster and utilize less memory.

OpenJDK and RISC-V are both open source projects; Liberica JDK is free for personal and commercial use. The BellSoft team is glad to continue contributing to the secure and easy development process of Java applications and allowing early access to evolving technologies.

RISC-V is changing the hardware manufacturing market and requires the closest attention from software developers too. RISC-V delivers a sustainable approach to development, removing unnecessary barriers, providing new levels of flexibility and scalability and, as a result, reducing the cost of chip production. RISC-V combined with correct application development practice can completely change the tech future.

March 8, 2024
Database Observability Extends DORA Metrics and More to Database DevOps

Extending DevOps to the database helps teams keep up with application deployments by shifting left and streamlining previously slow, manual stages in the workﬂow. Implementing change management automation and governance represents a major step in completing the “last mile” of your CI/CD pipeline. But to fully embrace DevOps and its beneﬁts, you not only need to automate and integrate but continuously optimize your processes.

DORA’s four key metrics provide a compass to application and DevOps teams for continuous optimization of the development pipeline. Measured and represented in analytics and monitoring platforms, these insights drive a faster, more eﬃcient and more innovative application development process.

How can the same spirit and quality of optimization extend to the database deployment workﬂow?

Organizations are leveraging database observability to apply DORA metrics to change management and use those insights to:

● Accelerate the pipeline

● Improve security

● Reduce errors and remediation

● Anticipate and prevent issues

● Detect and avoid database drift

Much like other elements of DevOps, it’s possible to bring visibility to the database change process thanks to the components that power workﬂow automation. Database observability unlocks DORA metrics along with other indicators that matter to your DevOps, application, database and IT teams.

The Role of Database Observability in DevOps

Database observability is the capacity to understand a system’s current state and optimize CI/CD pipeline performance, security, and eﬃciency by analyzing its outputs, logs and workﬂow data. These measurements inform insights for continuous improvement and proactive problem management, closing the gap between application development and database management in traditional DevOps practices.

Just like a DevOps observability dashboard provides visibility into the performance of application development workﬂows, a database observability dashboard showcases change management workﬂow measurements. It can answer questions like, “What is the current state of our environments?” or, more precisely, “Which changes have been deployed? Which of those succeeded or failed? What is the context surrounding those changes – and what can we learn from this information that can help make the pipeline better?”

Teams embrace database observability so they can:

● Streamline database operations by identifying bottlenecks and improving efficiency

● Enhance auditing capabilities through transparent and traceable database change logs

● Foster a culture of continuous improvement by leveraging data-driven insights for database management

● Align with application pipeline acceleration efforts to achieve overall DevOps agility and maturity

● Reduce the frequency and risk of deployment failures and operational downtime

Database observability enables you to shift left, proactively detecting issues and improving integrity earlier, before errors occur in production or further down the pipeline. It supports a high-performing data infrastructure with the ability to detect problems before they endanger your production system and identify opportunities for process improvement that make way for the team to focus on progress and innovation.

DORA Metrics for Database Change Management Optimization

With visibility into your systems, you can use actionable insights to inform decisions for the continuous improvement of your CI/CD pipeline. Think of DORA’s metrics (deployment frequency, lead time for changes, change failure rate, and time to restore service) in the database context, then expand to answer your optimization questions.

Identifying metrics is a critical step in the database observability initiative. Observability insights will help answer, “How do we improve these metrics?” and “What does improvement look like?” By capturing and analyzing database change logs over longer periods, teams can see trends and patterns previously obscured to them by lack of visibility.

Breaking down deployment frequency, for instance, can help highlight the metrics by database, application, and team to ﬁnd high and low performers or discover ineﬃciencies in data connections. Similarly, ﬁltering and slicing observability data to create baselines, comparisons and aspirations enables teams to use the data they’re already collecting to look inward and optimize the process itself. This can answer questions like:

● Which deployment pipelines are least eﬃcient and why? How can these be optimized, unblocked and accelerated?

● How often are database rollbacks occurring – and how is that affecting reliability?

● What kinds of errors are most common and costly – and how can they be avoided?

● Which users and systems are accessing which databases – and how can they be more secure?

In addition, database observability provides security and compliance enhancements by enabling trend analysis, reducing remediation time and anticipating potential issues before they occur. It can also identify drift in the intended state of your environment with detailed reports to ﬁnd the problem fast.

Achieving database observability has typically been an uphill battle – as one of the last areas of the overall application pipeline to be automated, database change management often relies on manual processes that take hours or days, not seconds. However, automated database change management workﬂow data provides all the ingredients for a successful database observability dashboard, not to mention the capability for self-service deployments and built-in policy checks.

In the context of full-pipeline visibility and continuous optimization, database observability brings much-needed clarity and granularity to teams embracing DevOps at every stage of their workﬂows.

March 7, 2024
The Ultimate DORA DevOps Guide: Boost Your Dev Efficiency With DORA Metrics

Imagine having a powerful tool that measures your software team’s efficiency, identifies areas for improvement and unlocks the secrets to achieving speed and stability in software development – that tool is DORA metrics.

DORA metrics offer valuable insights into the effectiveness and productivity of your team. By implementing these metrics, you can enhance your dev practices and improve outcomes.

In this blog, we will delve into the importance of DORA metrics for your team and explore how they can positively impact your software team’s processes. Join us as we navigate the significance of these metrics and uncover their potential to drive success in your team’s endeavors.

What are DORA Metrics?

Software teams use DORA metrics in an organization to help improve their efficiency and, as a result, enhance the effectiveness of company deliverables. It is the industry standard for evaluating dev teams and allows them to scale.

The metrics include deployment frequency, lead time for changes, mean time to recovery, and change failure rate. They have been identified after six years of research and surveys by the DORA (DevOps Research and Assessments) team.

To achieve success with DORA metrics, it is crucial to understand them and learn the importance of each metric. Here are the four key DORA metrics

The Four DORA Metrics

1. Deployment Frequency: Boosting Agility

Organizations need to prioritize code deployment frequency to achieve success and deliver value to end users. However, it’s worth noting that what constitutes a successful deployment frequency may vary from organization to organization.

Teams that underperform may only deploy monthly or once every few months, whereas high-performing teams deploy more frequently. It’s crucial to continuously develop and improve to ensure faster delivery and consistent feedback. If a team needs to catch up, implementing more automated processes to test and validate new code can help reduce recovery time from errors.

Why is deployment frequency important?

● Continuous delivery enables faster software changes and quicker response to market demands.
● Frequent deployments provide valuable user feedback for improving software efficiently.
● Deploy smaller releases frequently to minimize risk. This approach reduces the impact of potential failures and makes it easier to isolate issues. Taking small steps ensures better control and avoids risking everything.
● Frequent deployments support agile development by enabling quick adaptation to market changes and facilitating continuous learning for faster innovation.
● Frequent deployments promote collaboration between teams, leading to better outcomes and more successful projects.

Use Case:

In a dynamic market, agility is paramount. Deployment frequency measures how frequently code is deployed. Infrequent deployments can cause you to lag behind competitors. Increasing deployment frequency facilitates more frequent rollouts, hence, meeting customer demands effectively.

2. Lead Time for Changes: Streamline Development

The time it takes to implement changes and deploy them to production directly impacts their experience, and this is the lead time for changes.

If we notice longer lead times, which can take weeks, it may indicate that you need to improve the development or deployment pipeline. However, if you can achieve lead times of around 15 minutes, you can be sure of an efficient process. It’s essential to monitor delivery cycles closely and continuously work towards streamlining the process to deliver the best experience for customers.

Why is the lead time for changes important?

● Short lead times in software development are crucial for success in today’s business environment. By delivering changes rapidly, organizations can seize new opportunities, stay ahead of competitors and generate more revenue.
● Short lead times help organizations gather feedback and validate assumptions quickly, leading to informed decision-making and aligning software development with customer needs. Being customer-centric is critical for success in today’s competitive world, and feedback loops play a vital role in achieving this.
● By reducing lead time, organizations gain agility and adaptability, allowing them to swiftly respond to market changes, embrace new technologies and meet evolving business needs. Shorter lead times enable experimentation, learning, and continuous improvement, empowering organizations to stay competitive in dynamic environments.
● Reducing lead time demands collaborative teamwork, breaking silos, fostering shared ownership and improving communication, coordination and efficiency.

Use Case:

Picture your software development team tasked with a critical security patch. Measuring lead time for changes helps pinpoint the duration from code commit to deployment. If it goes for a long run, bottlenecks in your CI/CD pipeline or testing processes might surface. Streamlining these areas ensures rapid responses to urgent tasks.

3. Change Failure Rate: Ensuring stability

The change failure rate measures the code quality released to production during software deployments. Achieving a lower failure rate than 0-15% for high-performing dev teams is a compelling goal that drives continuous improvement in skills and processes. Establishing failure boundaries tailored to your organization’s needs and committing to reducing the failure rate is essential. By doing so, you enhance your software solutions and deliver exceptional user experiences.

Why is change failure rate important?

● It enhances user experience and builds trust by reducing failures; we elevate satisfaction and cultivate lasting positive relationships.
● It protects your business from financial risks, and you avoid revenue loss, customer churn, and brand damage by reducing failures.
● Reduce change failures to allocate resources effectively and focus on delivering new features.

Use Case:

Stability is pivotal in software deployment. The change failure rate measures the percentage of changes that fail. A high failure rate could signify inadequate testing or insufficient quality control. Enhancing testing protocols, refining code reviews and ensuring thorough documentation can reduce the failure rate, enhancing overall stability.

4. Mean Time to Recover (MTTR): Minimizing Downtime

Mean time to recover (MTTR) measures the time to recover a system or service after an incident or failure in production. It evaluates the efficiency of incident response and recovery processes. Optimizing MTTR aims to minimize downtime by resolving incidents through production changes. The goal is to build robust systems that can detect, diagnose, and rectify problems. Organizations ensure minimal disruption and work towards continuous improvement in incident resolution.

Why is mean time to recover important?

● Minimizing MTTR enhances user satisfaction by reducing downtime and resolution times.
● Reducing MTTR mitigates the negative impacts of downtime on business operations, including financial losses, missed opportunities and reputational damage.
● Helps meet service level agreements (SLAs) that are vital for upholding client trust and fulfilling contractual commitments.

Use Case:

Downtime can be detrimental, impacting revenue and customer trust. MTTR measures the time taken to recover from a failure. A high MTTR indicates inefficiencies in issue identification and resolution. Investing in automation, refining monitoring systems and bolstering incident response protocols minimizes downtime, ensuring uninterrupted services.

Key Use Cases

Development Cycle Efficiency:

Metrics: Lead time for changes and deployment frequency, high deployment frequency, swift lead time:

Teams with rapid deployment frequency and short lead time exhibit agile development practices. These efficient processes lead to quick feature releases and bug fixes, ensuring dynamic software development aligned with market demands and ultimately enhancing customer satisfaction.

Low deployment frequency despite swift lead time:

A short lead time coupled with infrequent deployments signals potential bottlenecks. Identifying these bottlenecks is vital. Streamlining deployment processes in line with development speed is essential for a software development process.

Code review excellence:

Metrics: Comments per PR and change failure rate, few comments per PR, low change failure rate:

Low comments and minimal deployment failures signify high-quality initial code submissions. This scenario highlights exceptional collaboration and communication within the team, resulting in stable deployments and satisfied end-users.

Abundant comments per PR, minimal change failure rate:

Teams with numerous comments per PR and a few deployment issues showcase meticulous review processes. Investigating these instances ensures review comments align with deployment stability concerns, ensuring constructive feedback leads to refined code.

Developer Responsiveness:

Metrics: Commits after PR review and deployment frequency, frequent commits after PR review, high deployment frequency:

Rapid post-review commits and a high deployment frequency reflect agile responsiveness to feedback. This iterative approach, driven by quick feedback incorporation, yields reliable releases, fostering customer trust and satisfaction.

Sparse commits after PR review, high deployment frequency:

Despite few post-review commits, high deployment frequency signals comprehensive pre-submission feedback integration. Emphasizing thorough code reviews assures stable deployments, showcasing the team’s commitment to quality.

Quality Deployments:

Metrics: Change failure rate and mean time to recovery (MTTR), low change failure rate, swift MTTR:

Low deployment failures and a short recovery time exemplify quality deployments and efficient incident response. Robust testing and a prepared incident response strategy minimize downtime, ensuring high-quality releases and exceptional user experiences.

High change failure rate, rapid MTTR:

A high failure rate alongside swift recovery signifies a team adept at identifying and rectifying deployment issues promptly. Rapid responses minimize impact, allowing quick recovery and valuable learning from failures, strengthening the team’s resilience.

Code Collaboration Efficiency:

Metrics: comments per PR and commits after PR is raised for review

In collaborative software development, optimizing code collaboration efficiency is paramount. By analyzing comments per PR (reflecting review depth) alongside commits.

After PR is raised for review, teams gain crucial insights into their code review processes.

High comments per PR, low post-review commits:

Thorough reviews with limited code revisions post-feedback indicate a need for iterative development. Encouraging developers to iterate fosters a culture of continuous improvement, driving efficiency and learning.

Low comments per PR, high post-review commits:

Few comments during reviews paired with significant post-review commits highlight the necessity for robust initial reviews. Proactive engagement during the initial phase reduces revisions later, expediting the development cycle.

Impact of PR Size on Deployment:

Metrics: Large PR size and deployment frequency

The size of pull requests (PRs) profoundly influences deployment timelines. Correlating large PR size with deployment frequency enables teams to gauge the effect of extensive code changes on release cycles.

High deployment frequency despite large PR size:

Maintaining a high deployment frequency with substantial PRs underscores effective testing and automation. Acknowledge this efficiency while monitoring potential code intricacies, ensuring stability amid complexity.

Low deployment frequency with large PR size:

Infrequent deployments with large PRs might signal challenges in testing or review processes. Dividing large tasks into manageable portions accelerates deployments, addressing potential bottlenecks effectively.

PR Size and Code Quality:

Metrics: Large PR size and change failure rate

PR size significantly influences code quality and stability. Analyzing Large PR size alongside change failure rate allows engineering leaders to assess the link between PR complexity and deployment stability.

High change failure rate with large PR size:

Frequent deployment failures with extensive PRs indicate the need for rigorous testing and validation. Encourage breaking down large changes into testable units, bolstering stability and confidence in deployments.

Low change failure rate despite large PR size:

A minimal failure rate with substantial PRs signifies robust testing practices. Focus on clear team communication to ensure everyone comprehends the implications of significant code changes, sustaining a stable development environment.

Leveraging these correlations empowers engineering teams to make informed, data-driven decisions — a great way to drive business outcomes— optimizing workflows, and boosting overall efficiency. These insights chart a course for continuous improvement, nurturing a culture of collaboration, quality and agility in software development endeavors.

Help Your Team With DORA Metrics!

In the ever-evolving world of software development, harnessing the power of DORA metrics is a game-changer. By leveraging them, your software team can achieve remarkable results. These metrics are vital to enhancing user satisfaction, mitigating financial risks, meeting service-level agreements and delivering exceptional software solutions.

March 7, 2024
Platform Engineering: The 2024 Game-Changer in Tech

Platform engineering, a key driver in the ever-evolving technological landscape, is poised for significant momentum in 2024. This article explores the reasons behind its rising popularity, the types of organizations that stand to benefit, potential drawbacks and the crucial role of test data management (TDM) in this context.

What’s Driving Demand for Platform Engineering?

Platform engineering is anticipated to gain significant momentum in 2024 as it offers crucial advantages in accelerating business value, reducing cognitive loads and enhancing the efficiency of application development and management processes. According to Gartner, by 2026, about 80% of software engineering organizations will establish platform teams as internal providers of reusable services, components and tools for application delivery. This trend underscores the increasing adoption and importance of platform engineering in the tech world.

The growing popularity of platform engineering can be attributed to several factors:

• Developer productivity: Enhancing developer productivity is a key driver, accounting for approximately 21% of the reasons for its popularity.
• CI/CD pipeline implementation: About 20% of the popularity is driven by the need for efficient continuous integration and continuous delivery pipelines.
• Standardization of tools and processes: Another 20% comes from the necessity to standardize tools and processes.
• Security enhancements: Security improvements contribute 20% to its popularity.
• Infrastructure-as-code (IaC): Finally, 19% is driven by the adoption of infrastructure-as-code (IaC) methodologies.

In addition, platform engineering addresses the inefficiencies caused by the decentralization of tools and processes in DevOps, especially as organizations scale. This approach provides a more organized and centralized framework, which is essential in managing the complexities of modern software development environments.

As highlighted by Gartner, platform engineering’s potential to offer a frictionless self-service experience to customers and business partners is a significant factor in its rising popularity. This adaptability is crucial in an era where technology and market demands are rapidly evolving, requiring businesses to remain agile and competitive.

These factors collectively make platform engineering a strategic approach for software engineering organizations, particularly looking forward to the year 2024 and beyond.

Key Benefits of Platform Engineering

While it isn’t a fit for every organization, there are some shared attributes of companies that are most likely to benefit from it.

If your organization is seeking to:

• Reduce tool sprawl by standardizing tools and services
• Improve productivity through automation, leading to faster development cycles
• Establish governance frameworks for software development
• Increase scalability through technologies like containerization and orchestration
• Enhance security by integrating automated security scanning tools into the CI/CD pipeline,

then platform engineering may be right for you. These benefits are especially relevant for businesses facing complex software development cycles and those in need of robust, scalable, and secure solutions.

Drawbacks of Platform Engineering

However, platform engineering is not without its challenges. Increased complexity in infrastructure can be daunting for small-scale teams. High dependency on the platform may lead to difficulties in switching technologies or providers. Additionally, compatibility issues with existing development tools and technologies can slow down processes.

The Crucial Role of Test Data Management (TDM)

In a platform engineering environment, test data management (TDM) becomes a pivotal component. TDM’s importance lies in its role in agile development, ensuring test accuracy and reliability, thus enhancing the effectiveness of the testing process. Agile methodologies, which divide projects into smaller sprints, rely heavily on TDM for providing high-quality, compliant test data in a continuous integration and deployment model.

TDM addresses the challenge of managing data fragmented across various applications, environments and compliance levels. An entity-based TDM approach enables testers to provision test data on demand, streamlining the testing process and reducing the risk of data breaches. Ultimately, TDM practices significantly reduce time, cost, and risk in software delivery, making it a vital component in modern platform engineering strategies.

As technology continues to evolve rapidly, the role of platform engineering, bolstered by effective TDM, becomes increasingly crucial. Companies that do not adapt to these changes by integrating components like TDM into their platform engineering strategies will risk falling behind in the competitive tech market. Therefore, embracing these innovations is essential for survival and success in today’s fast-paced technological landscape.

March 5, 2024
Low Code: The Key to Accelerating Custom App Dev for Moderate Developers

Custom app development has become a crucial skill in an increasingly digitized world. Many moderate developers face challenges while creating unique and functional applications. This is where low-code platforms come into play, offering a solution to accelerate app development and compensate for those shortcomings.

Who are Moderate Developers?

While every developer is constantly evolving and growing in experience, like any professional, they can be put in three baskets. The first is entry-level developers learning on the job who may assist with debugging, essential documentation, code repository maintenance and some simple feature requests. At the other end are very experienced developers who lead projects, architect solutions, oversee the development of applications and take full ownership of delivering killer features in a new application.

Moderate developers are those in the middle, representing a large swath of professionals with the foundational expertise to implement most app designs and feature requests. With more experience than beginners but yet to be qualified to serve as seniors on a team, they have a shared set of challenges.

Understanding the Challenges of Moderate Developers

Moderate developers often need help with complex application development demands and skill limitations. They are faced with creating high-quality, functional and unique applications, which can be daunting in light of the rapid pace of technological change and user expectations.

These moderately skilled developers often face common challenges such as:
● Limited Expertise: Cultivating an in-depth knowledge of programming languages and development environments is an ongoing process.
● Time Constraints: Developing an application from scratch requires significant time. Dedicating effort to a single project can constrain developers and their organizations.
● Scalability Issues: Growing and expanding applications increase the need for scaling and can create complexity. Making changes and adding new features can drag down an organization’s ability to focus on new projects.
● Security Concerns: Moderate developers may not be aware of an application’s security risks, jeopardizing the protection of a company.

Low-Code Platforms Offer Hope

Low-code platforms have been created to meet the needs of moderately skilled developers and competitively focused organizations. The use of tested drag-and-drop elements offers a secure and powerful way to overcome these challenges.

Creating practical and powerful applications is available through low-code platforms by connecting ready-made templates. This process rapidly creates applications for use as mobile and web-based applications.

Non-IT professionals can overcome their lack of time and knowledge through low-code platforms. Short-staffed organizations in their IT department can still provide a powerful connection for their services. The team can quickly get to work meeting its primary mission.

Visual Modeling is the Secret Power of Low-Code Platforms

Traditional application development involves writing lines of code, testing and managing by an experienced IT staff. Low-code platforms use pre-tested and connected visual components to build and implement successful and secure applications rapidly.

Low-code developers can see the application they are building from the beginning of the process to its launch. The drag-and-drop aspect of these platforms is more intuitive and user-friendly. Using proven templates offered by these platforms can also help reduce errors.

Duplicating Success

Assembling tested and ready-made components boosts the chance of an application’s success. Pre-configured applications are a way to rapidly launch applications with the confidence of a proven track record.

Low-code platforms offer users the confidence to trust the components while looking straight at their ongoing results from a clear visual perspective. More time and attention can be paid to an application’s unique functionalities rather than the complexity and drudgery of coding.

Team Building and Collaboration Opportunities

The visual nature of low-code platforms allows the entire team to play an active role in an application’s development and keeps everyone speaking the same development language.

Built-in collaboration tools facilitate feedback loops, revision tracking, user stories and messaging, making it easier for teams to work together in the same office or distributed worldwide. Everyone can own the solution while having a positive impact upon its release.

Ensuring Scalability

Low-code platforms offer more than a “one and done” opportunity for growth and success. Robust and friendly customer-facing connections await users of low-code platforms who take advantage of their power. They can continue to generate further service opportunities and sales after the initial launch.

Proven internal processing networks can be extended with the same low-code platforms that started this robust development. Low-code connections can be efficiently expanded using earlier low-code work.

Ever-changing business needs can be readily addressed with existing low-code platforms. They can continue to offer ready-made templates for internal and external successes that fit an enterprise’s needs.

The cloud-based nature of low-code platforms ensures a flexible and continuous service of the applications. This helps ensure reliability and responsiveness to a customer’s ongoing needs.

Choosing the Right Low-Code Platform

Identifying the best low-code software development platform is based on several key factors that developers must consider:
● Ease of use
● Scalability
● Integration capabilities
● Ability to customize applications

Decision-makers must also consider their team’s expertise when choosing a low-code platform. Trial versions or free tiers can be beneficial for understanding the team’s expertise and a platform’s capabilities to meet an organization’s needs.

The Future of Low-Code Platforms

As businesses continue to navigate the complexities of digital transformation, analysts predict a bright future for low-code platforms. The trend will be continued enterprise adoption to ensure faster development and specific business needs. It’s also clear that low-code platforms will only partially replace traditional software development. But by democratizing app development and fostering a more robust culture of innovation, low-code platforms are positioned to significantly shape the future of business in the digital era.

March 4, 2024
Navigating the Rise of AI-Generated Code in Software Development

The year 2023 saw the rise of many technologies and innovations, but none quite as prominent as generative AI. Trained on vast amounts of data, these models are designed to “generate” the next sequence of values based on inputs and create novel content for various purposes. The emergence of generative AI has forced industries to rethink how they operate and where adjustments should be made. A recent study from ISG indicates that financial services are the leading industry for generative AI adoption, making up 24% of total use cases. This is followed by manufacturing at 14%, healthcare and pharma at 12% and business services at 11%.

More recently, organizations have started contemplating generative AI’s impact on software development and its ability to effectively generate code. AI-enabled tools will have a major impact on software development, increasing the volume and velocity of code delivery and innovation. However, there are ethical and practical guardrails that need to be considered.

Key Capabilities of Generative AI in the SDLC

The advantage of AI tools lies in their ability to enhance the software development workflow by automating repetitive tasks. Mundane and time-consuming boilerplate code generation or routine code reviews can now be handled by the same machines that we are writing the programs for. This not only accelerates the software development lifecycle (SDLC) but liberates developers from monotonous tasks, allowing them to focus on more creative projects that require human insight.

For developers, the strength of AI-enabled tools lies in their ability to expedite the development process. By harnessing machine learning (ML) algorithms, AI can analyze vast datasets, find patterns and generate simple code quickly, often requiring numerous iterations to get it right — hopefully! But at its core, the benefit of AI-enabled tools stems from the premise that it’s easier to refine a set of initial ideas than it is to generate them. In other words, generative AI models create the “sparks” that a good developer can turn into a full creative process.

Challenges, Concerns and Ethical Considerations

As the adage goes, there are two sides to every coin. While AI tools hold the power to provide immense benefits to developers, they also introduce an array of risks that cannot be overlooked:

● Challenges in understanding context and user requirements: Despite its sophistication, AI struggles to grasp the intricacies of specific project contexts and the diverse needs of end-users. The contextual understanding that human developers possess, gleaned from experience and intuition, is still a facet where current AI encounters hurdles.
● Potential security risks: The automated nature of AI-generated code introduces potential security risks that demand careful consideration. While AI algorithms strive for efficiency, they may inadvertently generate code with vulnerabilities. Addressing security concerns requires ongoing vigilance, thorough testing, and the incorporation of robust cybersecurity measures to ensure the integrity of the developed software.
● Adapting to evolving technological environments: The dynamic nature of technology presents another challenge for AI-generated code, as rapid changes in programming languages, frameworks and methodologies require an adaptive approach. AI models may struggle to seamlessly transition to new technological advancements, potentially causing compatibility issues and requiring continuous updates to maintain relevance.

It’s important to note that developers will not be the only ones affected by AI’s integration into the SDLC. Security teams are barely keeping pace now, and in many organizations, they’ve become the bottleneck. Increased developer velocity will put security teams under even more pressure—the volume of scans, the range of vulnerabilities and the patches that need to be applied are all guaranteed to skyrocket.

Automation Alone is Not Enough; We Still Need Human Developers

While the introduction of AI marks a significant milestone in the evolution of software development, it’s essential to recognize the nuanced interplay between automation and human ability. Rather than replacing human expertise altogether, generative AI is poised to augment and complement the skills of developers. Human creativity, critical thinking and the ability to navigate complex problems will continue to be invaluable assets in the development process — true potential only appears when collaborating seamlessly with human developers, blending the efficiency of automation with the creativity and problem-solving skills inherent to human intelligence.

Looking towards the future of software development, the trajectory of generative AI presents intriguing possibilities. It has become increasingly clear that the rise of AI-generated code is not a story of replacement but one of enhancement. While AI brings undeniable efficiency and automation to certain aspects of coding, the role of human developers is still irreplaceable. The understanding of context, the intuition required for creative problem-solving, and the adaptability to evolving technological environments are hallmarks of human expertise. These are facets that, as of now, AI struggles to replicate.

As we embrace the future of AI-generated code, let us not forget the essence of our craft. The evolution of technology is inevitable, but it is the human touch, ingenuity and ethical foundation that will define the software landscape of tomorrow.

Image courtesy of Josh Calabrese via Unsplash

March 1, 2024
404: Role Not Found – Generative AI’s Impact on DevOps Jobs
Many DevOps professionals are wondering if generative AI will be a friend or foe, even as DevOps remains at the forefront when it comes to onboarding new technologies. Our DevOps communities have been enabling and fostering the collaborative cultural shift and evolving roles and responsibilities of software professionals. In this article, we explore the impact of generative AI in key DevOps roles and explain how to prepare ourselves for the shift.

Let us start by highlighting the basic roles and their broad areas of responsibility so we begin with a common vocabulary.

Overview of DevOps Roles

DevOps roles are evolving quickly, but PagerDuty outlined six main DevOps roles for modern organizations.
1. The DevOps Evangelist: Responsible for delivering changes, collaborating with dev and ops teams and working toward optimizing flow, enabling feedback and fostering experimentation.
2. The Code Release Manager: Responsible for creating cadence between delivery and deployment of code. This role often requires deep technical expertise to run and maintain the releases.
3. The Automation Architect: Responsible for designing and implementing automated pipelines and reducing manual toil.
4. The Experience Assurance Expert: Responsible for user experience, enabling feedback loops and ensuring superior experience, assessing feature performance and improving end-product usability.
5. The Software Developer & Tester: Developers and testers are responsible for the design and development of the software product. Primary responsibilities are writing code, testing and fixing bugs.
6. The Security and Compliance Engineer: Responsible for the overall security and compliance of the software product. Collaborating with all the above roles to ensure standards and regulations are implemented.
All roles above are expected to work across the value chain to maximize their impact. Note that there are many other roles, including leadership and executive roles, which we have not covered in this article. Next, let’s assess the impact of generative AI on some of these roles.

Adding Generative AI to Every Role

Before diving into the business value of adding generative AI to job roles, let’s build a common vocabulary, starting with defining generative AI. According to Gartner, generative AI can learn from existing artifacts to generate new, realistic artifacts (at scale) that reflect the characteristics of the training data but don’t repeat it. It can produce novel content such as images, video, music, speech, text, software code and product designs.

Automated code generation: AI-augmented code generation tools serve as companions for developers and testers. As an example, GitHub Copilot, developed by GitHub in collaboration with OpenAI, primarily offers predictive code generation and supports multiple languages. Tabnine offers deep learning capabilities and supports around 20 programming languages.

As these and other tools evolve, software developers’ time may be freed up to focus on higher-level, more creative, and innovative thinking and business value-driven development. Some pundits say these code generation tools may be a stopgap in the race to hire developers. It may also result in generative AI-based moderators, a new job role entirely. These moderators would help with exporting or reusing existing code rather than trying to reinvent it.

AI-based release management: Generative AI can also help with release management; from a simplified approach for generating release notes to boosting the productivity of the feature release to setting a precise release cadence, generative AI-based tools are coming to our rescue. For example, Digital.ai introduced predictive decision-making, threat insights and many more features to orchestrate releases. Another example is K8sGPT, a tool for scanning your Kubernetes clusters, diagnosing and triaging issues in simple English. More integration of generative AI is on the way for DevOps tools. Release managers’ roles are at a pivotal point and will evolve with time and new skills to manage real-time releases, integration with several channels of special edition software, classic upgrades and core software releases. They will need to address different customer segments with managed subscription-based, open source versions and take a human-centric approach to software releases.

Security and compliance engineers: DevSecOps firm Jfrog is integrating generative AI capabilities into its Xray and Artifactory solutions. It will automatically detect security flaws, license violations and more early and often. Snyk surveyed more than 400 IT professionals and found the use of artificial intelligence (AI) to write code is creating a security paradox. Generative AI also can be used to simulate malware attacks and identify vulnerabilities. However, this is a double-edged sword as it also can be used to enhance the portfolio of bad actors. That means that security and compliance engineers must increase their focus on regulatory, assessment, audits and validation of generative AI outcomes. Specialist knowledge, decision-making, and ethical evaluation are needed for a strategic approach toward next-generation cybersecurity capabilities.

The automation architect: An automation architect is responsible for decomposing complex tasks and automating them through reusable code. These tasks become more sustainable, with less rework and delays. As an automation architect, generative AI is another tool in the toolset that helps improve efficiency and productivity. Generative AI can help automation architects to scale and allow self-service options for DevOps teams and automate redundant tasks.

Generative AI’s Impact on Key Job Roles

As discussed in the previous section, there are many jobs that will be transformed by generative AI’s influence. Many companies will view generative AI-enhanced tools as a pot of gold where most of the work that requires building templates, infrastructure provisioning, log analysis, reuse or refactoring code can be done with the help of generative AI-enabled tools.
- Short-term skills gap: Software-related skills gaps are everywhere, and organizations need to go beyond reskilling to solve the problem. The role of leaders is key for successfully transitioning into the new era of workforce modeling. DevOps professionals should also look at strategies to improve our productivity by investing in capabilities and tools to fill the skills gap.
- Mid-term talent augmentation: Role disruption is most likely to happen due to generative AI; research and a more structured approach are needed to address the upcoming change. DevOps teams must focus on communication patterns, onboarding of human-machine collaborative models and ensuring performance of the new work models. Copyright, intellectual property (IP), licensing and the use of open source technology will be essential ingredients of mid-term talent augmentation with generative AI.
- Long-term, flexible workforce: Widespread and massive onboarding of generative AI in the long term will require a pragmatic approach to flexible workforce modeling. Task-based frameworks will emerge to guide the flexible orchestration of workforce, roles or even jobs or tasks. Task-based frameworks may mature into capability-driven roles that evolve into a flexible workforce where humans and machines together will deliver the desired business outcomes.
With rapid advancement in generative AI technology, companies will be forced to design new ways of working to keep pace with these changing dynamics. DevOps professionals should see this as an opportunity to get ahead of the curve, assess how generative AI technologies can boost productivity and enhance it with new tools and capabilities.
February 26, 2024
Blueprints for a Secure, Future-Proof Hybrid Cloud

For today’s organizations, hybrid environments are a given. Whether for legacy reasons, regulatory requirements, or other specific needs, some of an enterprise’s critical data is likely to reside on, or in some cases, revert to, on-premises. However, other equally crucial data might be more efficiently and cost-effectively suited for storing and accessing in the cloud. Hence, hybrid.

Despite their logic and many benefits, hybrid environments bring new levels of complexity. And they bring greater vulnerability, with ever more devious cyber threats probing the multitude of network access points. More than 80% of breaches now involve data stored in hybrid environments, according to IBM’s Cost of a Data Breach 2023 Report.

That’s why, to avoid data loss and maintain a comprehensive level of protection across the entire environment, organizations need a blueprint for adopting and evolving their hybrid systems. The right solution enables data protection across workloads, providing the hybrid enterprise with the flexibility to protect data wherever it is today — and wherever it might be in the future.

A growing multitude of organizations in various industries have successfully implemented blueprints of this sort, built on the following foundational considerations:

Cost-optimization: A solution that can span a hybrid environment — SaaS, software and on-premises — and reduce infrastructure to manage and save administrative time.

Air-gapped security: Backup of the most critical data in cloud locations that can’t be accessed through whatever network or networks might be the focus of a cyberattack.

Fast recovery: Flexible, performance-optimized storage combined with the ability to send an air-gapped copy to the cloud for ransomware protection.

Broad coverage: An integrated set of software, appliance and SaaS offerings that span hybrid environments — and can evolve with the organization.

Simple management: Single-pane-of-glass dashboards for monitoring the hybrid environment while reducing management overhead.

Reliability for peace of mind: Leveraging industry-leading, trusted platform solutions rather than unproven point products.

Now’s the time to see it in action.

Ensuring an Insurer’s Data Backup

In Israel, Harel Group is the country’s top insurance and financial services provider, managing a large volume of data and investments. As such, a critical priority for the company is to ensure all that data is 100% backed up and can be restored without any issues.

Harel Group were using a secure solution to reliably protect more than 1.5PB of data in its on-premises environment. More recently, though, it has sought the flexibility and efficiencies of moving part of its infrastructure to the cloud. However, stringent industry regulatory requirements meant Harel could store sensitive data in the public cloud only if it could be backed up and stored as efficiently, securely and reliably as the on-premises data. What they needed was a SaaS solution.

“A SaaS solution takes care of data availability, but not the backup operation of the data,” said David Ben-Eli, system IT infrastructure manager for Harel Group. “We knew from day one that in taking on Microsoft 365, we would need a SaaS solution with good restore functionality that satisfies our business model and strategy.”

And so, Harel designed a blueprint based on a fully managed SaaS platform. The initial stage involved protecting approximately 1,000 of its employees’ mailboxes, which will soon cover more than 5,000 mailboxes.

The SaaS solution also enabled Harel to move its on-premises storage securely to the cloud, which has the dual advantage of reducing infrastructure costs and air-gapping its backups to mitigate the risk of ransomware attacks. “We have peace of mind that our data is secure,” Ben-Eli said. And the entire cost of the SaaS solution “is less than the cost of hiring more people.”

Backing up and protecting a company’s data in a hybrid environment is challenging enough. To be a vendor providing those capabilities to other companies comes with additional challenges.

A Vendor Puts its Data in Order

Evolutio, based in Madrid, is a leading enabler of cloud services that provides more than 5,600 kilometers of deployed networks with 6,100 virtual machines across three data centers. Its goal is to support customers’ digital transformations and foster their agility, flexibility and innovation capacity to optimize their business value.

To meet Evolutio’s needs, Alain Rodriguez, head of services management office, had to find an easy-to-use solution that consumed minimal resources to manage backups and security for Microsoft 365. His blueprint aimed to protect critical data before anything could go wrong.

So, Evolutio chose a single platform to back up and restore data for internal systems. By implementing a fully hosted SaaS-based solution, Evolutio no longer needs to worry about purchasing and maintaining equipment yearly. There is also no upfront investment required and no additional costs for support from the vendor. As a result, Evolutio’s business can grow on demand.

Resolving a Global Data Sprawl

Woodward, Inc. is an American designer, manufacturer and service provider of control systems and components for aircraft engines, industrial engines and power systems. The company has been growing significantly across dozens of global networking sites, with expansion that quadrupled the size of data and backed-up servers.

Virtual machine sprawl ate into company resources while complicating backup operations requiring standardized procedures. Slow legacy servers and apps made it impossible to meet recovery point objective (RPO) and recovery time objective (RTO) targets. And the absence of a coherent, comprehensive disaster recovery plan worsened things.

Topping this off was increased backup storage costs via disk and tape and a need for central management or cross-department visibility into data protection and backup policy compliance. There was no blueprint, in other words.

After trying unsuccessfully to address its needs with various disparate solutions, Woodward decided to replace everything with a comprehensive cloud-based backup and recovery platform. The company switched all global sites to this solution. As a result, Woodward has reduced the overhead of managing their data protection, can consistently enforce backup, restore and retention policies and has unprecedented comprehensive visibility and reporting across the end-to-end environment.

“I used to cringe when I received a restore request since backups would frequently fail,’’ said Loren Leveque, senior manager of IT and system networking. With the cloud-based platform, he said, “I am ready to support the backup, restore and archive needs of our company.”

Supporting Dozens of Manufacturing Plants

Linamar Corporation is a diversified global manufacturing company and world leader in designing, developing and producing highly engineered products. Because the Canadian company supports 61 plants globally, a cloud approach became necessary to streamline efficiency in backup and recovery.

Linamar had recently shifted from Microsoft Exchange on-premises to cloud-based Microsoft 365 for its 7,000+ users worldwide, and the company’s tape-based backup system didn’t support it. The built-in Microsoft 365 data protection was a start, but it couldn’t handle the rapidly expanding demands imposed by Linamar’s global network.

By moving to a cloud-based backup and recovery platform supporting Microsoft 365, Linamar saved the costs and administrative time previously needed for managing tapes. And backup is no longer a full-time job – it’s more like “when you need it, set it up,” said Kristian Smith, Linamar’s manager of technical services and support. Restores can now be handled 100% within the team and take hours to execute, not weeks.

Bringing it All Together

The examples might be as varied and numerous as enterprises worldwide with crucial data to protect. But these and any other success stories all have at least one thing in common: They took a blueprint approach to ensuring that their hybrid environment is secure and sufficiently future-proof.

February 23, 2024