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Tag: cloud migration

Smoothing the Never-Ending Road to Modernization
In IT, it can sometimes feel like the finish line keeps getting farther away, no matter how fast we run.

Cloud migration reshaped how organizations think about infrastructure, offering new scalability and agility, while creating years of intensive migration work that continues to this day. Then, application modernization shifted the conversation from where workloads run to how effectively applications can leverage modern architectures and services. Suddenly, IT teams found themselves modernizing both their applications and their environments simultaneously.

Today, AI is creating yet another wave of modernization efforts, with organizations seeking ways to connect agents to existing applications and data.

If it feels as though there’s always another modernization initiative around the corner, that’s because there generally is. In reality, IT has no finish line. After all, the future doesn’t wait for organizations to finish the modernization initiatives of the past.

Technologies evolve, business needs change, and it’s up to the IT shop to ensure their environments and applications keep pace.

We’ll never reach an end state where environments and applications are finally, permanently “modern.” But that doesn’t mean IT professionals need to experience life as one mad scramble after another.

There’s a better way.

The Move to Continuous Modernization

We tend to think of IT modernization in terms of major projects such as huge cloud migrations or enterprise-wide application refreshes and upgrade campaigns. But really, much of modernization consists of smaller, recurring tasks like updating libraries, remediating vulnerabilities, and keeping applications aligned with internal development standards.

Treating all this work as a series of manual projects can eat up significant engineering time and leave IT teams feeling they’re never able to get ahead. But by automating many of these tasks, organizations can free up capacity for real innovation. And especially critical at the current moment: they can make legacy applications and data ready for agents without a rewrite, and without waiting for every last workload to move.

That’s the idea behind AWS Transform – continuous modernization, a new capability of AWS Transform for continuous, autonomous technical debt analysis and remediation. While AWS Transform already helps enterprises migrate to the cloud, modernize applications, and streamline software maintenance, this new agentic capability gives organizations broad visibility into the state of their codebase across thousands of repositories. It provides detailed dependency mapping to help prioritize findings, and code changes are delivered as pull requests for developer review.

Out of the box, the offering includes policies for detecting end-of-life dependencies, deprecated frameworks, and other common sources of technical debt. IT teams can also extend these capabilities with remediation patterns specific to their organization, including approved libraries, internal coding standards, or tech debt policies that their platform teams already enforce.

The goal is to help organizations continuously reduce technical debt and keep their frameworks and codebases AI-ready, without requiring developers to spend all their time managing legacy code in the name of modernization.

Continuous Modernization in the Real World

Only weeks after this new offering was announced, organizations are already seeing the impact of continuous modernization. Tasks that once required substantial manual effort now happen much more quickly, at a scale that would have been impossible before.

Quantiphi, an AI-first digital engineering and services company based in Massachusetts, ran continuous modernization across a large portfolio, compressing a multi-week assessment into days. The company analyzed more than 500 repositories, uncovering more than 3,000 technical debt findings in under a week. By automating technical debt discovery and uncovering actionable remediation insights, company leaders said, they were able to reduce their assessment effort by more than 60%.

Tech Mahindra, a multinational consulting and engineering company based in India, saw even more dramatic time savings, reducing assessment time across 25 enterprise repositories by 80%, from 40 hours down to just eight. Sanjeev Agarwal, the company’s global head of AWS business, said company leaders see this as the “beginning of a transformative shift” in how organizations approach IT modernization. “AWS Transform’s scanning and analysis engine is solid, and the structured output allows our teams to validate findings quickly and build prioritized remediation plans with confidence,” he said.

And U.S. healthtech company Netsmart used AWS Transform – continuous modernization to help five teams accelerate projects that were estimated to take between three months and a year. Some were completed as quickly as two weeks. “Dependency mapping has historically been a tedious, complex and ongoing effort,” said Pepper Pancoast, Senior Director of Engineering. “This new capability helps automate dependency mapping on a recurring basis, giving us greater visibility into our codebase health at scale.”

Learn More

On October 22 at 1 p.m. Eastern, AWS leaders will join Futurum Group CTO Advisor, Alastair Cooke, for a webinar discussion titled “Continuous Modernization: Why Modernization Never Really Finishes.”

Together, we’ll explore what changes when organizations stop considering modernization as a project with a finish line, and instead embrace the tools and practices needed to continuously modernize across their IT environments.

Attendees can expect to learn how to:
- Turn manual, reactive software maintenance into continuous modernization.
- Unlock engineering resources and reduce maintenance costs.
- Gain a broader, more transparent view across their codebases.
- Reduce tech debt at scale and ensure their codebase is AI-ready.
Register here to join us.
September 24, 2026
When DevOps Meets the Cloud: A Real-World Transformation Story
DevOps and cloud adoption are often described as guaranteed upgrades for modern software teams. In practice, the transition is rarely smooth or predictable. It involves rethinking habits that have existed for years, questioning assumptions, and learning through a mix of progress and setbacks. This article shares a real-world DevOps and cloud transformation journey from a growing product team, focusing on what actually changed, what went wrong, and what ultimately delivered value.

Rather than presenting an idealized success narrative, this case study reflects the realities teams face when modernizing software delivery in a live production environment.

The Situation Before Change

At the start, the team’s workflow was familiar and functional, but it struggled under increasing pressure. The product was growing steadily, feature requests were frequent, and expectations around reliability were rising. However, the systems and processes supporting development had not evolved at the same pace.

Releases required manual coordination across teams. Environmental differences caused unpredictable behavior. When incidents occurred, diagnosing the root cause often depended on individual experience rather than clear data.

Some recurring challenges included:
- Deployments that required manual intervention and late-night fixes
- Differences between development, testing, and production environments
- Limited visibility into system performance during failures
- Slow recovery times when something went wrong
- Growing tension between speed and stability
As user demand increased, it became clear that continuing with the same approach would only amplify these issues.

Why DevOps and Cloud Became Necessary

The decision to adopt DevOps practices alongside cloud infrastructure was driven by necessity rather than trend-following. The existing setup worked only when the system was small. As complexity increased, the lack of automation and standardization became a liability.

Cloud infrastructure offered scalability and flexibility, but without structured processes, it risked becoming another source of inconsistency. DevOps principles provided a way to align development and operations, reduce manual work, and make system behavior more predictable.

The team set a few guiding objectives:
- Reduce dependence on manual deployments
- Improve release reliability without slowing development
- Shorten recovery time when failures occur
- Enable engineers to focus on product work instead of operational firefighting
Rather than attempting a full overhaul, the team chose to implement changes incrementally.

Early Steps in the Transformation

Standardizing Infrastructure

One of the first improvements involved standardizing infrastructure across environments. Configuration differences had been a frequent source of bugs, especially issues that appeared only after deployment.

By defining infrastructure consistently, the team reduced environment-related surprises. This change alone eliminated a class of problems that previously consumed hours of debugging.

Automating Build and Deployment Pipelines

Next, the team introduced automated build and deployment pipelines. Each code change followed the same path, from build to testing to deployment. This replaced informal, manual steps with repeatable workflows.

Automation did not immediately make releases flawless, but it made failures easier to trace and resolve. Knowing that every deployment followed the same process reduced uncertainty and guesswork.

Treating Infrastructure as a Shared Responsibility

Infrastructure changes were no longer handled in isolation. They were reviewed, discussed, and versioned alongside application code. This encouraged collaboration and reduced dependency on a few individuals with specialized knowledge.

Challenges That Slowed Progress

Despite early wins, the transition was not without friction.

One mistake was introducing automation faster than monitoring capabilities. When something failed, it was not always obvious why. Automated systems moved quickly, but visibility lagged.

Another challenge was human adaptation. Tools changed rapidly, but habits did not. Some team members trusted the new processes immediately, while others remained cautious. Building confidence required time, documentation, and consistent outcomes.

There was also an initial tendency to over-engineer solutions. In hindsight, simpler workflows would have delivered value faster. The team adjusted by focusing on stability before optimization.

Strengthening Observability and Feedback

As the system matured, monitoring and observability became a priority. Logs, metrics, and alerts were refined to provide clearer insight into system behavior.

This shift changed how incidents were handled. Instead of reacting based on assumptions, teams relied on data. Faster detection and clearer context reduced the stress associated with outages and enabled quicker recovery.

Importantly, failures became learning opportunities rather than blame-driven events.

Post-incident reviews focused on process improvement instead of individual mistakes.

Tangible Improvements Over Time

After the DevOps and cloud practices stabilized, the impact became more consistent and measurable.
- Releases became more frequent and predictable
- Deployment-related incidents decreased noticeably
- Mean time to recovery improved due to better visibility and rollback strategies
- Engineers spent less time on repetitive operational tasks
- Collaboration between teams became more natural and less reactive
One of the most valuable outcomes was psychological. Deployments no longer felt risky. Confidence in the system allowed teams to move faster without increasing stress.

Evaluating the Return on Investment

Rather than relying on abstract benefits, the team evaluated ROI using practical indicators.

They tracked:
- Time from code completion to production deployment
- Frequency of deployments per release cycle
- Time required to recover from failures
- Reduction in manual operational work
Over time, these indicators showed steady improvement. The investment in DevOps and cloud tooling paid off not through immediate gains, but through reduced friction and fewer disruptions.

Lessons Learned Along the Way

Several insights emerged that may help other teams considering a similar transition:
- DevOps success depends more on culture than tools
- Automation must be paired with observability
- Cloud platforms amplify both good and bad processes
- Incremental change is more sustainable than large-scale rewrites
- Metrics provide clarity, but interpretation matters
Most importantly, DevOps is not a one-time project. It requires continuous refinement and a willingness to adapt.

Advice for Teams Starting Their Own Journey

For organizations planning a DevOps and cloud transformation, a few principles can guide early decisions:
- Start with pain points, not tools
- Standardize environments before optimizing workflows
- Introduce automation gradually and measure its impact
- Invest in communication and shared ownership
- Treat failures as feedback, not setbacks
These steps help ensure that DevOps practices improve outcomes rather than introduce new complexity.

Closing Reflection

This real-world DevOps and cloud transformation did not succeed because of perfect planning or cutting-edge tools. It succeeded because the team embraced learning, adjusted based on real outcomes, and remained focused on reducing friction in everyday work.

For teams facing similar challenges, the greatest value lies in approaching DevOps as an evolving practice rather than a finished destination. With patience, measurement, and collaboration, DevOps and cloud adoption can deliver lasting improvements in both software delivery and team experience.
February 20, 2026
An Experience-Based Guide to Choosing the Right DevOps Provider in 2026
To stay competitive, companies need to deliver software updates and new features rapidly without compromising reliability and security. However, maintaining a specialized in-house team can be costly due to talent shortages, high salaries and fluctuating workloads. Whether through outsourcing, outstaffing or managed service models, partnering with a trusted DevOps provider offers a flexible way to fill talent gaps and scale expertise without the burden of maintaining a large in-house team.

This article outlines a four-phase approach for selecting and evaluating a DevOps partner to ensure alignment with your business objectives.

PHASE 1: The ‘Why’ (Internal Preparation)

Begin your search by evaluating your project needs. Assess the current state of your software systems, delivery processes and areas for improvement. This will help you define clear requirements and avoid misunderstandings as you align your project vision with that of the service provider.

1.1 Define Your Needs and Goals

Identify the challenges you want to solve through DevOps adoption and create a clear, organized list of goals. This list will become the foundation for selecting and evaluating potential partners. Here are a few examples of such goals:
- Increase developer productivity by establishing or optimizing a continuous integration/continuous deployment (CI/CD) pipeline.
- Implement infrastructure as code (IaC) to automate and standardize cloud environment management.
- Reduce deployment and release times while minimizing errors from manual processes.
- Improve system monitoring, alerting and logging to enhance visibility.
- Strengthen infrastructure security and compliance controls.
Define measurable success criteria (for example, reduce deployment time by 40% in six months or achieve 99.9% uptime on production). Providers perform better when expectations are transparent and quantifiable.

1.2 Align Stakeholders Early

Before engaging external DevOps partners, make sure all key stakeholders in domains such as engineering, operations, finance and security agree on priorities and success metrics. Misalignment often causes friction later when the provider’s goals do not fully match business expectations.

Max Petrichenko, team lead at IT Craft DevOps development company, notes:

“A clear understanding of your long-term goals and challenges is the foundation of a successful DevOps implementation. If your team is overloaded or lacks a comprehensive understanding of existing bottlenecks, a third-party system audit can provide valuable insights. It provides an outside perspective on your infrastructure, highlights hidden risks and helps refine your requirements before engaging an external DevOps partner.”

1.3 Assess Your Current Capabilities

Review your available resources and evaluate your in-house development team’s skills and capacities, as they closely collaborate with the outsourced DevOps team. Focus on identifying:
- Gaps in workflows that DevOps can help resolve.
- How much workload do your developers already carry?
- Existing DevOps-related skills and knowledge within the team.
- How satisfied are developers with current processes?
1.4 Determine Project Scope and Duration

Evaluate the scope and expected duration of your collaboration. Depending on your goals, you might need a short-term engagement to handle specific tasks, such as a cloud migration or a long-term strategic partner who acts as an extension of your in-house team, managing infrastructure maintenance and monitoring.

Understanding your project scope, key outcomes and timeline will help you determine how many DevOps engineers you need and what type of engagement model best fits your goals.

PHASE 2: The ‘Who’ (Initial Vetting)

Once your internal preparations are complete, proceed to evaluate potential service providers. Review the available candidates carefully and create a shortlist of those who meet your key requirements.

2.1 Experience and Expertise

A solid track record is the clearest indicator of a service provider’s expertise. Look for a partner that specializes in DevOps within your industry, not a general software development company. Their familiarity with your industry’s unique challenges can be a significant advantage.

Review case studies and client testimonials from similar projects, paying attention to experience with businesses of your size. Additionally, don’t hesitate to ask for references to verify their claims.

Ask how the provider measures DevOps’s success internally. Mature vendors often track metrics such as deployment frequency, change failure rate, lead time to change and mean time to recovery (MTTR)—known as DORA metrics. A team that is fluent in DORA indicates a metrics-driven mindset, not just tool familiarity.

2.2 Technical Proficiency and Tooling

Review the service provider’s technical stack. They should have proven expertise with core DevOps tools such as Docker, Kubernetes, Jenkins, Terraform or Grafana, ensuring they can support automation, orchestration and monitoring across your environment.

However, a reliable service provider shouldn’t be limited to a fixed set of tools. Instead, they should be skilled in a wide range of DevOps technologies and flexible enough to choose the ones that best fit your project’s specific needs and challenges.

Check whether the provider supports multi-cloud and hybrid environments. Various companies face tool sprawl across AWS, Azure and on-prem infrastructure. A capable partner should demonstrate how they unify observability and automation across different platforms. Ask for a real example of how they used automation to reduce operational costs or deployment errors in a similar environment.

2.3 Communication and Cultural Fit

The outsourced DevOps team should function as a seamless extension of your in-house team. Prioritize providers that promote transparency, proactive communication and flexibility in adapting their workflows to align with your existing processes.

A reliable service provider should ensure smooth communication and consistent project updates. They need to integrate with the project management tools used by your internal team and hold regular touchpoints such as daily check-ins and weekly planning meetings to keep everyone aligned.

Beyond English fluency, evaluate time-zone overlap, responsiveness during emergencies and clarity in reporting. A mature vendor provides structured communication rituals such as weekly status summaries, quarterly retrospectives and incident reports, following the DevOps blameless post-mortem model.

PHASE: 3 The ‘How’ (Deeper Evaluation)

After shortlisting and vetting potential providers, proceed to the technical and operational evaluation stage. The goal here is to verify their claims and gain a clear understanding of how they apply DevOps practices in real projects.

3.1 Technical Assessment and Methodology

Take a close look at how the provider applies DevOps in real projects. Ask them to walk you through a typical CI/CD pipeline and explain how they handle quality, security and process automation. Ideally, the provider can walk you through an end-to-end workflow, from estimation to production, explaining each step and the reasoning behind every decision.

Ask whether the provider performs infrastructure security reviews as part of CI/CD implementation. DevSecOps readiness, including vulnerability scanning, compliance checks and secret management, has become mandatory. Moreover, evaluate if they use automated testing frameworks and observability tools to detect performance regressions early.

3.2 Team Structure and Skill Set

Request to review the curriculum vitae (CVs) of the specialists assigned to your project and schedule interviews to discuss their experience, previous roles and upcoming responsibilities. Additionally, assess the provider’s replacement procedures and resource backup plans to ensure business continuity in the event of a key DevOps expert leaving the team.

3.3 Test the Partnership with a Trial Project

Begin with a small paid project to evaluate the DevOps team in a real-world setting. This allows you to observe their technical skills, communication style and overall approach to collaboration. Pay attention to how they respond to feedback and manage revisions, as this will reveal their flexibility and commitment to delivering quality results.

If possible, structure your trial project to simulate a real bottleneck or risk scenario, such as a sudden traffic spike or a failed deployment. Measure how the team collaborates under pressure. A strong DevOps partner demonstrates not only technical skill but also composure and clear communication when issues occur.

PHASE 4: The ‘What’ (Finalizing the Agreement)

After vetting and selecting a particular service provider from your list, focus on preparing a clear and comprehensive contract.

4.1 Pricing Models and Transparency

Whatever model you choose, whether fixed price or a dedicated team, ensure the pricing structure is fully transparent. Ask about potential risks of cost overruns and how they are handled. Confirm that the provider clearly outlines all expenses from the start to avoid unexpected charges later.

Request a detailed cost breakdown for each deliverable or sprint to prevent hidden charges. Clarify ownership of cloud accounts, licenses and infrastructure expenses. Moreover, establish a clear exit strategy—what happens to your infrastructure scripts, credentials and data if the contract concludes.

4.2 Clarify Terms and Protect Your Interests

Clearly document all deliverables in the contract and include key performance metrics in the service level agreement. These may cover uptime, mean time to recovery, lead time for changes and other relevant indicators. Review carefully the section outlining what happens if the provider fails to meet commitments and how accountability is ensured.

Beyond the SLA, include a continuous improvement clause. This ensures the provider regularly proposes process enhancements (for example, improving build time or deployment frequency) rather than just maintaining the status quo.

Review all contract terms with your legal team to ensure your interests are fully protected. The agreement should clearly define intellectual property rights, roles and responsibilities, and include provisions that help prevent misunderstandings or future disputes.

Final Thoughts: Continuous Partnership Evaluation

Even after signing, vendor management doesn’t end. Review your DevOps provider’s performance quarterly using key performance indicators (KPIs) such as:
- Deployment frequency and speed improvements
- Incident response efficiency
- Infrastructure cost optimization results
- Innovation contributions (for example, automation improvements or new tool adoption)
This ongoing evaluation keeps the relationship strategic, not transactional.
October 29, 2025
Dispelling the Cloud Security Myths and Accelerating Migration
Cloud migration is no longer a question of if—it’s a matter of when. But the mere mention of it can cause IT security leaders to conjure up images of data breaches, compliance nightmares and loss of access control. As businesses aggressively continue to harness the scalability, flexibility and cost-effectiveness of the cloud, the pressure on every application to make the leap mounts. But here’s the controversial truth: Most security fears about the cloud are, by now, completely unfounded.

Persistent waves of misconceptions continue to sow doubt, holding back migrations and leaving applications unnecessarily stuck in outdated infrastructure. So how do you separate fact from fiction in the cloud security debate?

On Sept. 24, during our DevSecOps Virtual Summit, we addressed these concerns in the session “Dispelling Cloud Security Myths in Migration.” Let’s take a deeper dive into some of the most common myths around cloud security—and, more importantly, what you can do to ensure secure, seamless transitions to the cloud.

The Top Cloud Security Myths Keeping You Up at Night

In our session, one thing became crystal clear: We’ve been looking at cloud security all wrong. When it comes to cloud migration, security is often cited as the biggest barrier, but many of the fears around cloud security are rooted in outdated ideas.

Myth 1: The Cloud is Less Secure than On-Premises Solutions

For years, we’ve clung to the notion that our on-premises systems are inherently more secure than the cloud. This is one of the biggest misconceptions organizations face. The idea that data is inherently safer when kept on physical servers within an organization’s walls is outdated. In reality, cloud providers invest billions in their security infrastructure, and many cloud services are equipped with more advanced security measures than most on-premises environments could ever match.

This doesn’t mean that migrating to the cloud automatically makes your data secure. The essential difference here is that security in the cloud is a shared responsibility. Cloud providers offer a secure foundation, but it’s up to your organization to build on that foundation with strong policies around access, data protection and compliance.

Myth 2: Once You’re in the Cloud, You’re Immune to Cyberattacks

The cloud can be more secure, but it isn’t invincible. A common belief is that once your systems and data are in the cloud, you’re automatically protected from threats. But no system—cloud-based or otherwise—is immune to cyberattacks. What matters is how prepared you are to respond to those threats.

Cloud security requires continuous monitoring and updating. A “set it and forget it” mindset won’t work in the cloud. Proactively managing your security posture by leveraging cloud-native security tools, multi-factor authentication and encryption is essential to staying ahead of evolving threats.

Myth 3: Cloud Migration is Complicated, Adding Security Risks

Yes, migrations are complex. Also, migrations (of any kind) involve risk. However, both the complexity and the risks of cloud migrations can be managed. Too often, organizations stall their migration plans due to concerns over data loss or exposure during the transition.

Cloud migrations don’t have to be a particularly intense security risk. A comprehensive migration strategy addresses potential risks before they become real problems. Tools that automate security checks throughout the migration process can ensure that you’re not leaving vulnerabilities open as you move data and systems to the cloud while helping manage the migration’s complexity.

The Right Approach to Cloud Migration: Strategy and Security

Now that we’ve cleared up some of the misconceptions, what does a secure cloud migration really look like? The success of your cloud migration relies on building a clear roadmap that takes both your business needs and security concerns into account. Here are three steps to ensure a smooth and secure journey:
1. Start with a Security-First Mindset
Before you begin your migration, assess your current security posture. Identify where your vulnerabilities lie and make sure your security policies are up to date. Remember, migration isn’t just about moving data, it’s about moving data safely.
1. Leverage Cloud-Native Security Tools
One of the biggest advantages of the cloud is access to cloud-native security tools. These tools are designed specifically for cloud environments and provide automated monitoring, data encryption and compliance tracking. OpenText’s DevOps Aviator, for instance, integrates seamlessly with cloud platforms, offering continuous security checks throughout the migration process.
1. Collaborate Across Teams
Cloud migration isn’t just an IT task—it involves everyone, from security to development teams. Open lines of communication are critical to ensuring that security measures are integrated at every stage of the migration and that everyone is aligned on how to respond to any issues that arise during or after the transition.

What’s Next? Watch the Full Session Recording

Migrating to the cloud doesn’t have to be an intimidating process, but it does require the right approach and a solid understanding of what’s true—and what’s not—when it comes to cloud security.

Don’t let cloud security myths hold back your organization. The benefits of the cloud are too significant to ignore. By taking a security-first approach and leveraging the right tools, your organization can confidently migrate to the cloud and reap the rewards.

If you’re ready to separate fact from fiction in your cloud migration journey, check out the DevSecOps Summit recording and take the first step toward a more secure, efficient and innovative future.

Watch the full session recording here and learn how to ensure your cloud migration is secure and seamless.
October 1, 2024
AWS Cloud Migration Strategy
Cloud migration is moving data, applications or other services to a cloud computing environment from an on-prem data center. Migrating your project means moving your data from the on-premises data center to the cloud. In this case, the cloud is the virtualization used over a data center to make the functionalities more flexible. Many companies like GoDaddy, Expedia, Netflix, startups, etc., have recently moved their business to the cloud. Migration is a big task and AWS facilitates migration in phases.

Need for Migration

Business is no easy task when handling situations like security, scaling up or down, etc. Let’s look at a few scenarios where AWS Migration could be a better resort.
1. More users coming in amid high-performance
2. Faster implementation and deployment needs
3. Expensive to manage a growing database
4. Datacenter mishap went down
We can conclude that the following are key reasons to migrate to the cloud:
- Zero capital cost on infrastructure
- Security
- Scalable (vertically and horizontally)
- Productivity
- Pay what you use
- Easy to manage services
- Reduced operational costs
If you migrate to the cloud, the abovementioned problems will be handled automatically. Let’s move further and understand what migration is.

Phases of AWS Migration

AWS Cloud Migration is a step-by-step process that consists of planning, preparation and discovery, migration and optimization. However, it is more complex as the process involves different phases. Let me now talk about the various phases of migration.

Phase 1: Migration Preparation and Business Planning

In this phase, you identify the need, objective and benefits of cloud migration. You perform a POC and verify the outcome to compare the performance and costs. There are also times when you need not move your entire business to the cloud. This is where segregation is important. You need to identify the applications which can be migrated and those which cannot. This is what the first phase is all about.

Phase 2: Assessment — Choosing Your Migration Method (Discovery and Analysis & Assessment)

The first step is to identify the assets that need to be migrated to the cloud — gather the details such as the number of servers to migrate, road map to migration, record business-critical activities, server size, memory utilization, CPU utilization, environment type and data. You can use the ‘AWS Application Discovery Service’ to check the technical details by installing it in your server. Depending on the data, AWS provides different ways to migrate your application, e.g., AWS Snowball, AWS Snowmobile, AWS Direct Connect, etc. Also, assess the cost of renting servers, storage, network and support. Prepare the checklist for regulatory and compliance assessment and perform the security assessment.

Phase 3: Proof of Concept (POC) for AWS Storage/Designing

Once you know how and what you are migrating, next, you must plan your delivery priorities and design the AWS Cloud Migration process. You will identify the dependencies (upstream and downstream components). Define the organization or account strategy for users, VPC strategy and what cloud pattern it will be (hybrid/fully cloud) and other parameters like security.

Phase 4: Migration to AWS

Now that you have all the prerequisites such as the blueprint, migration tools, a list of assignments, backups, and its synchronization with your on-premises data repositories, you can finally migrate your project/application to AWS Cloud. Once you have migrated your project to the cloud, reliability and durability are the added benefits you get. For application code/server migration use AWS Server Migration Service, for database, you can use AWS Database Migration Service and other import/export AWS services.

Phase 5: Enterprise Cloud Operations (Operate and Optimize)

At this point, you have already migrated to AWS and it will bring updates that you would need to incorporate in your existing architecture. Optimize your architecture, have your support team ready to look for the new features and cost optimization, improve the applications using serverless, use AWS monitoring and logging services and implement CloudFormation and DevOps.

Application Migration Strategies ‘The 6 Rs’

The complexity of migrating existing applications varies, depending on the architecture, Amazon came up with different strategies which they commonly termed as 6 R’s. Let’s look into each of them:

Benefits of AWS Migration
1. Elasticity: Adding and removing capacity whenever it is needed is the greatest benefit of
2. Disaster Recovery: With 95% guaranteed uptime, businesses can be confident knowing that their data will always be available.
3. Enhanced Cost Management: The IaaS platform provides two major First, an IaaS such as Amazon Web Services is available as a monthly service. Second, it eliminates the need to continue to purchase and maintain physical hardware.
Services for AWS Migration

Out of the many tools provided by Amazon to automate data migration, I will be talking about the more commonly used ones.

AWS Migration Hub: AWS provides a single location for tracking the migration process. Migration Hub gives you the freedom to choose your migration partner and tools that fit your needs.

AWS Server Migration Service (SMS): AWS SMS is an agentless service that helps migrate loads of on-premises workload to AWS easily and faster. It allows you to automate migration and track the replication of the server. It makes coordinating with your large-scale-server migration easier.

Amazon S3 Transfer Acceleration: This makes the transfer of files over long range to the AWS S3 bucket faster and more secure.

AWS Snowball: It is a petabyte-scale data transfer solution that uses secure devices to transfer a large amount of data in and out of AWS.

AWS Snowmobile: It is an exabyte-scale data transfer solution to move an extremely large amount of data to AWS. Snowmobile makes the transfer of massive volumes of data easier.

Amazon Kinesis Firehose: It is the easiest out of all the methods. It can capture and automatically load streaming data into Amazon S3. You can analyze real-time data to get timely insights of the migration.

Now that you know everything about AWS, and AWS Migration, let me show you a use case where you will be migrating a Virtual OS from my local machine to AWS Cloud.

AWS Data Migration

Data can be moved from on-prem to AWS Datacenter using the following means:
- Amazon S3 Transfer Acceleration — for faster transfer over the internet change the endpoint you use with your S3 bucket and acceleration is automatically applied.
- AWS Snowball — a petabyte-scale data transport solution that uses secure appliances to transfer large amounts of data into and out of AWS.
- AWS Direct Connect — you establish a dedicated network connection between your network and one of the AWS Direct Connect locations for transfer.
Demo: Implementing Migration Using the Import/ Export Method

You will be migrating an on-premises Virtual OS running on VMWare to AWS using the import/export (AWS Direct Connect) method.

Pre-requisite
Create a .vmdk export file for Ubuntu 14.04.
1. Download and configure AWS
1. Create an IAM
IAM – User —Add User —AccessType (Programmatic Access) —Attach Admin Access Policy —Create User.
1. Create an S3 bucket and add a file (image file of the OS).
2. You can upload the file using AWS CLI aws s3 cp “source path” <s3 bucket>
3. Once the file is uploaded, create an AMI for the imported image
Aws ec2 import-image –description “AWS Linux” –disk-containers file://containers.json
1. Now your AMI is available, and you can see it under AMI in EC2 and run it as and when
Thus, we have successfully migrated a virtual OS on AWS.

By now you should have a fair idea about the AWS Cloud Migration strategy and the process, tools and transfer mechanisms available.
August 31, 2024
Azure Migration Strategy: Tools, Costs and Best Practices
Azure migration refers to the process of moving applications, data and other resources from on-premises or other cloud environments to Microsoft Azure, which is a cloud computing platform and service offered by Microsoft.

Azure migration can include migrating virtual machines, databases and storage to Azure, as well as re-architecting applications to take advantage of Azure services. Typically, the goal of Azure cloud migration is to take advantage of the scalability, security and cost benefits of the Azure platform.

4 Azure Migration Tools

Here are several tools provided by the Azure cloud which can help you migrate applications to the Microsoft cloud.

Azure Migrate

Azure Migrate is a service offered by Microsoft as part of Azure that helps customers discover, assess, and migrate their on-premises workloads to Azure. It provides a centralized hub for the discovery and assessment of on-premises workloads and helps customers plan and execute their migration to Azure. Azure Migrate includes the following features:
- Discovery: Azure Migrate discovers on-premises servers and their dependencies and creates an inventory of them.
- Assessment: Azure Migrate assesses the readiness of on-premises servers for migration to Azure and provides recommendations for target Azure resources.
- Migration: Azure Migrate helps customers plan and execute their migration to Azure using various migration tools such as Azure Site Recovery, Azure Database Migration Service and Azure Data Box.
By using Azure Migrate, customers can move their workloads to Azure with confidence, knowing that they have a comprehensive plan and the right tools in place to ensure a successful migration.

Azure Database Migration Service

Azure Database Migration Service (DMS) is a fully managed service offered by Microsoft as part of Azure that simplifies and accelerates the process of migrating on-premises databases to Azure. The service supports a variety of databases, including SQL Server, Oracle, MySQL, and PostgreSQL.

Azure DMS can perform both online and offline migrations, depending on the requirements and availability of the source database. Online migrations are performed while the source database is still in use, while offline migrations require the source database to be taken offline during the migration process.

The service supports several migration scenarios:
- One-time migration: Move a database to Azure once and leave it running in Azure
- Lift and shift: Migrate an on-premises database to Azure and keep the same database engine running in Azure
- Change the engine: Migrate an on-premises database to Azure, but change the database engine running in Azure to a different one.
The Azure DMS provides a simple and efficient way to move your on-premises databases to Azure with minimal downtime, and it’s integrated with Azure Migrate for a holistic migration experience.

Azure Data Box

Azure Data Box is a service offered by Microsoft as part of Azure that allows customers to transfer large amounts of data to Azure for storage and processing. The service is designed for customers who have a large amount of data that needs to be transferred to Azure, but have limited network bandwidth or other constraints that make it difficult to transfer the data over the Internet.

Azure Data Box provides customers with a physical device (called an “Azure Data Box”) that they can use to transfer data to Azure. The device is shipped to the customer, who can then copy data to the device and ship it back to Microsoft. Microsoft will then transfer the data to the customer’s Azure storage account.

There are several different types of Azure Data Box, each with different storage capacities and data transfer speeds:
- Azure Data Box: 100TB storage capacity, data transfer speed of 100Mbps
- Azure Data Box Disk: 40TB storage capacity, data transfer speed of 40Mbps
- Azure Data Box Heavy: 1PB storage capacity, data transfer speed of 100Mbps
- Azure Data Box Edge: 100TB storage capacity, data transfer speed of 100Mbps and AI and IoT edge computing capabilities.
Azure Data Box is particularly useful for customers who have large amounts of data to transfer, and don’t have the network bandwidth or other resources to transfer the data over the Internet.

Azure Synapse Pathway

Azure Synapse Pathway is a new feature of Azure Synapse, which is a fully managed cloud-based data integration and analytics platform. Azure Synapse Pathway is designed to make it easy for customers to move their existing data integration and analytics workloads to Azure Synapse without requiring significant re-architecting or rewriting of their existing code.

Azure Synapse Pathway supports several migration scenarios, such as:
- Lifting and shifting on-premises SQL Server Integration Services (SSIS) packages to Azure Data Factory.
- Migrating on-premises SQL Server Analysis Services tabular models to Azure Analysis Services.
- Migrating on-premises Power BI reports to Power BI in Azure Synapse.
Azure Synapse Pathway also provides a migration tool that helps customers to move their existing data integration and analytics workloads to Azure Synapse, with minimal changes to their existing code. This allows customers to take advantage of the benefits of Azure Synapse without significant re-architecting or rewriting of their existing code.

Azure Migration Strategy and Best Practices

Prepare an Inventory of Digital Assets

Understanding your digital estate is an important part of creating an effective Azure migration strategy. A digital estate refers to the collection of digital assets and resources that an organization uses to run its business, including applications, data, and infrastructure. In order to migrate your digital estate to Azure, you need to understand what you have and where it’s running. This information can be used to create a migration plan that is tailored to your specific needs.

To understand your digital estate, you should start by creating an inventory of your applications, data, and infrastructure. This inventory should include information such as:
- The name and purpose of each application
- The technology stack used by each application
- The location of the application and its data
- The dependencies between applications and data
- The number of users and usage patterns for each application
- The storage and compute requirements for each application
Once you have an inventory of your digital estate, you can use it to create a migration plan that takes into account the specific needs of each application, as well as any dependencies or constraints that need to be considered.

Best practices for inventorying your digital assets:
- Start by identifying the most critical applications and data that need to be migrated first.
- Assess the readiness of your existing infrastructure and applications for migration to Azure.
- Understand the dependencies between your applications and data, and plan for them accordingly.
- Identify any constraints or limitations that need to be considered when planning your migration.
By understanding your digital estate and creating a migration plan that takes into account the specific needs of your applications and data, you can ensure that your migration to Azure is successful and that your business is able to continue to operate during and after the migration.

Estimate Monthly Workload Costs

Estimating the monthly costs of your workloads is an important part of creating an Azure migration strategy. By understanding the costs associated with running your workloads in Azure, you can make informed decisions about how to migrate your workloads and how to optimize your cloud costs after migration.

To estimate the monthly costs of your workloads in Azure, you can use the Azure pricing calculator. The pricing calculator allows you to estimate the costs of running various Azure services, such as virtual machines, storage, and networking, based on the specifications of your workloads. You can also use the Azure TCO calculator to estimate the total cost of ownership of your workloads in Azure, taking into account costs such as data transfer, management, and support. There are multiple other Azure calculators you can use to estimate storage costs, networking costs, and more.

Image Source: Azure

When estimating the costs of your workloads in Azure, it’s important to consider the following factors:
- The size and number of virtual machines required to run your workloads
- The storage and network requirements of your workloads
- The number of users and usage patterns of your workloads
- The services and features required to run your workloads, such as Azure Backup or Azure Security Center
It’s also important to consider how the costs of your workloads may change over time, such as how the number of users and usage patterns may change or how the costs of Azure services may change.

Best practices for estimating the monthly costs of your workloads in Azure:
- Use the Azure pricing calculator and TCO calculator to estimate the costs of running your workloads in Azure.
- Understand how the costs of your workloads may change over time and plan accordingly.
- Optimize your costs after migration by using Azure cost management and Azure reservations.
- Continuously monitor your costs and usage to ensure you’re getting the most value from your investment in Azure.
By understanding the costs associated with running your workloads in Azure, you can create a migration strategy that is optimized for cost and that enables you to get the most value from your investment in Azure.

Migrate Workloads and Optimize

Migrating your workloads to Azure and optimizing them for the cloud environment is an important step in creating an Azure migration strategy. Once you have a clear understanding of your digital estate and have estimated the costs of running your workloads in Azure, you can begin migrating your workloads and optimizing them for the cloud.

There are several ways to migrate workloads to Azure, depending on the type of workload and the migration requirements. Some common migration methods include:
- Lift and shift: This method involves moving an existing workload to Azure without making any changes to the workload. This is typically used for simple workloads that do not require significant changes to run in the cloud.
- Re-architecting: This method involves making changes to the workload to take advantage of the features and capabilities of Azure. This may include re-architecting the application to suit the target Azure service, such as Azure SQL Database or Azure Storage.
- Refactoring: This method involves making changes to the workload to optimize it for the cloud environment. This may include refactoring the application using cloud-native patterns and practices, such as microservices or containerization.
Once your workloads are migrated to Azure, you can optimize them for the cloud environment by:
- Scaling your workloads to match demand.
- Using Azure services and features to improve the performance and security of your workloads.
- Monitoring and managing your workloads to ensure that they are running efficiently.
- Optimizing costs by using Azure cost management and Azure reservations.
Best practices for migrating your workloads and optimizing for the cloud environment:
- Start by migrating your most critical workloads first.
- Understand the migration method that is best suited for your workloads and plan accordingly.
- Use Azure services and features to improve the performance and security of your workloads.
- Continuously monitor and manage your workloads to ensure that they are running efficiently.
- Optimize your costs by using Azure cost management and Azure reservations.
By following these best practices, you can ensure that your workloads are migrated to Azure and optimized for the cloud environment, which will help you to get the most value from your investment in Azure.

Monitoring

Monitoring is an important part of creating an Azure migration strategy and ensuring that your workloads are running efficiently in the cloud. By monitoring your workloads, you can identify and address issues that may arise during or after migration and ensure that your workloads are meeting the needs of your business.

Azure provides a variety of tools and services for monitoring your workloads, including:
- Azure Monitor: A service that provides a unified monitoring solution for Azure resources, including virtual machines, storage, and networking.
- Azure Log Analytics: A service that allows you to collect and analyze log data from your workloads.
- Azure Application Insights: A service that allows you to monitor the performance and availability of your applications.
- Azure Security Center: A service that provides security monitoring and management for your workloads.
When monitoring your workloads in Azure, it’s important to consider the following factors:
- The performance and availability of your workloads
- The usage and costs of your workloads
- The security and compliance of your workloads
It’s also important to consider how your monitoring needs may change over time, as your workloads and usage patterns may change.

Best practices for monitoring your workloads in Azure:
- Use Azure Monitor, Log Analytics, Application Insights, and Security Center to monitor your workloads.
- Continuously monitor your workloads to ensure that they are running efficiently.
- Use Azure alerts to notify you of issues that may arise.
- Use Azure dashboards to create custom views of your monitoring data.
- Continuously assess and monitor your digital estate to ensure that it’s up-to-date and that your migration plan is still valid.
By monitoring your workloads in Azure, you can identify and address issues that may arise during or after migration, and ensure that your workloads are meeting the needs of your business. This will help you to get the most value from your investment in Azure and ensure a successful migration.

Conclusion

In conclusion, migrating to Azure is a great way to take advantage of the scalability, security, and cost benefits of the cloud. However, migrating your workloads to Azure requires a well-planned strategy that takes into account the specific needs of your business and your digital estate.

To create an effective Azure migration strategy, it is important to understand your digital estate, estimate the costs of running your workloads in Azure, choose the right strategy, and optimize them for the cloud environment.

Additionally, monitoring your workloads in Azure is crucial to ensure that they are running efficiently, and to address any issues that may arise during or after migration. By following best practices and using the tools and services provided by Azure, you can ensure a successful migration and get the most value from your investment in Azure.
February 3, 2023
Migration to AWS Cloud: Do’s and Don’ts
What if one day, you were told to migrate to AWS/GCP/Azure/any other cloud provider? It would likely introduce some stress, to understate the case, right?

I’ve seen quite a few migrations in my career and, in this article, I would like to share some thoughts to help DevOps engineers, architects, managers or anyone else who might be involved in such a migration. I’m focusing on migration to AWS mostly because I have a specialization in this subject and because it’s fairly common, but I think these experiences are applicable to any other cloud provider. For this case, I’ll cover migration from on-premises to a cloud because these types of migrations are much more difficult (in my opinion) than cloud-to-cloud migrations. I’ll also share my own perspective on do’s and don’ts of migrations. But please filter them through critical thinking and remember that everybody has their own background and experience. What’s good for one might be bad for another.

What is a Cloud Migration?

Let’s start by defining what migration is. How do you know if it’s a migration? I like the following definition:

Cloud migration is the process of moving a data center’s capabilities to IaaS providers.

“Capabilities” is the keyword. A business does not migrate servers/virtual machines/hardware/data or anything else. A business migrates its capabilities.

There are seven migration strategies (or seven Rs): Relocate, rehost (lift and shift), replatform, repurchase, rearchitect/refactor, retain and retire.

For example, I’ve seen situations when a company was creating a new product that would replace an existing one hosted on-premises. The product would be designed to be well-architected and cloud-native from the very beginning and would be hosted in a cloud. Is that migration or greenfield project type? From the definition above, we can say that this is a migration. This is a rearchitecture strategy for migration.

What if we buy a new SaaS subscription instead of some software we host on-premises? This is also a migration: A repurchase strategy.

Let’s dig deeper into these seven migration strategies:

Relocate. We host some workloads on-premises and move them as-is into the cloud. This is possible in limited cases; when we migrate cloud-agnostic workloads or when a platform might be cloud-native. Examples: Relocate a Kubernetes cluster from on-premises to the cloud or relocate VMware machines with VMware Cloud.

Rehost. This is also known as lift and shift. We convert virtual machines/servers on-premises to virtual machines in the cloud. Examples: Migrate on-premises virtual machines to EC2 instances with CloudEndure, or create EC2 instances, install the software and apply the same configuration as on-premises.

Replatform. We migrate a workload to a cloud-native platform without rearchitecting systems. Examples: Migrating PostgreSQL to AWS RDS or Docker containers to ECS.

Repurchase. We replace some custom/legacy system with SaaS. Example: Replace custom on-premises email system with SendGrid or replace CRM with Salesforce.

Rearchitect. We change your application’s architecture to be cloud-native and use managed cloud services. That also includes rebuilding from scratch. Examples: Change the code of your apps to upload files to S3 instead of local storage or containerize an app and migrate to ECS.

Retire. We decide that a piece of the workload will be eliminated if not needed. Example: Log servers are no longer needed as migrated applications use CloudWatch.

Retain. We leave it on-premises. Usually, it’s temporary. Example: A huge Oracle database with a lot of functions and customization is not moved to AWS because of the massive costs. It’s decided to keep it on-premises until it’s replaced with another solution in the modernization phase.

I like the following picture that shows the high-level stages of each migration type. It may be a helpful cheat sheet to approach migrations.

Picture Source: https://aws.amazon.com/blogs/enterprise-strategy/new-possibilities-seven-strategies-to-accelerate-your-application-migration-to-aws/

Migration Don’ts

I don’t recommend the following when performing a migration:

Migrating too quickly. A business might think, “We have to migrate to the cloud quickly. Let’s do lift and shift right now!” It’s better not to start right away. Are you sure that rehosting strategy will really be faster than some other strategy? In one instance, we tried to migrate on-premises VMs, but it didn’t work because the OS on the machines was quite old. Some OS-level dependencies didn’t work on AWS’ hardware. This required a lot of configuration work, custom code and testing and the initial estimates were completely wrong. It eventually turned out to be a replatform.

Assess what you have, analyze it, analyze the business case and think. During this engineering work, we should stop and think before we act.

Migration without clear goals. Why are you migrating? If you can’t answer the question, you probably need to find an answer or not migrate at all. I’ve seen situations in which businesses wanted to migrate to the cloud to reduce costs. They performed a lift and shift migration without doing a total cost analysis. Only after the fact did they realize that it cost even more than when it was in a colocation center. This migration may be considered unsuccessful.

To avoid this, define clear goals and define the target state that achieves these goals. If costs are the driver, make the proper calculations and plan accordingly. Cost optimization will be the main thing to focus on when you plan your target state. If availability is the reason, measure it, define SLOs and focus on availability in a target state.

Black box migration. Don’t treat workloads you migrate as a black box. It is almost critically important to know what exactly you migrate, what technical requirements it has, what dependencies it has and how it works. That information will allow you to choose the correct strategy for workload migration. If this is not done, there’s an excellent chance that you will choose the wrong path and the result will be lost. Capturing information about your systems is the most important task when planning.

Don’t forget well-architected. Migration is stressful. That’s why people tend to try to get through it quickly on as short of a timeline as possible, focusing on the only thing: Making everything work in the cloud ASAP. They set aside well-architected framework pillars. This is a mistake; it may lead to a different set of negative consequences of different severity. Don’t ignore well-architected framework pillars, or you won’t know about potential issues or risks until they have already materialized.

Use one strategy for everything. It’s always easy to make a single overarching plan for all workloads. For example, deciding to do lift and shift and then rehost everything in the cloud. But what if some things could be retired? What if some components could be replatformed with little effort? You will save significant resources by analyzing all of the components of the workload and deciding on a unique strategy for every component. Collect data and plan properly.

Cloud Migration Do’s

Do perform a cloud readiness assessment. If a person migrates to a new location, they should consider:
- That the climate will be different
- The language spoken in that location may be different
- The cost of living will be different and perhaps more expensive
When a company migrates to the cloud, they should ask questions including:
- Have we considered all the risks?
- What must be changed for the workload to actually work?
- Do we have the human resources required for all of the work?
- Do I have the budget to accomplish everything?
The Cloud Readiness Assessment is a long-form interview about varying processes in the company that identifies whether a company is ready to live in the cloud. It’s based on the Cloud Adoption Framework (CAF) and allows us to understand what should be changed so that a migration can happen successfully. AWS has a Cloud Readiness Assessment tool which covers all of that. I urge you to use this process for all workloads being considered for migration.

Create a business case. Describe the business case. What are the drivers for migration? What problems are resolved with the migration? What goals should be achieved by migration? Be specific: If you intend to reduce costs, then calculate the sum. If you intend to improve availability, then define a clear SLO. That will be the number-one driving force for your migration. All other artifacts should be based on it.

Automate discovery and data collection. Use automated data collection and analysis to create a portfolio for migration. Don’t base your analysis on manually created spreadsheets or verbal communication. Let machines collect data about inventory and usage statistics. The more precise data you collect, the better. AWS Migration Evaluator service will help you to do this in most cases.

Portfolio management. Create a portfolio of workloads to migrate, analyze them, choose a migration strategy for each, choose some for the pilot/first wave migration, estimate timelines and prepare a plan. Execute the migration after that. Repeat until the migration portfolio is empty. Share the data. Have subject matter experts review it.

Found CCoE. This is part of the CAF mentioned above. Before executing the migration, create a team of people responsible for creating a strategy and defining a good standard of living in the cloud: A Cloud Center of Excellence. It should include people with a variety of subject matter expertise including management, operations, platform, people, finance and every other department consuming or providing services in the cloud. These people will prepare a landing zone for the migration, hard policies and softer policies for the landing zone; preparing the company for life in the cloud. Don’t fear revisiting any policy. Make changes as new factors come to light.

Well-architected framework reviews (WAFR). Do well-architected framework reviews (WAFRs) during migration waves and after them. That will allow you to avoid foolish mistakes and to get a high-level overview of what you have done from a technical perspective.

Modernization plan. Prepare a modernization plan along with the migration plan. Migration isn’t the end of the story. To be effective in the cloud, you will have to do well-architected reviews and modernization. The first modernization is likely to be huge; that’s why it’s important to plan and to schedule budgets and resources in advance. Failure to do this will lead to increased operational costs, not decreased costs.

Summary

If we want to reduce the risk associated with a migration and to make it less stressful, we do not rush. You may believe that rushing will save you time and other problems will be managed later, but experience tells us that this is wrong. The most likely outcome is that you will end up resolving all the issues encountered in the most stressful situation possible: When you cut over to your new cloud-based production environment. Instead of resolving issues in a well-considered way and in advance, the choices will be made under duress. Proper engineering principles must be applied here. Picking up a bridge and moving it from one river and plunking it down on another never works. The key to migration success is to measure, analyze, design and plan. Migrating a workload to the cloud is hard, but I hope these do’s and don’ts will make it easier.
December 9, 2022
From Cloud Chaos to Cloud Smart With Multi-Cloud Services

Organizations that embrace multi-cloud services experience real results and a heap of advantages—including greater resiliency, agility and data sovereignty. This is why it’s no surprise the majority of enterprises today are using two or more public clouds, with 81% reporting in a recent survey that they plan to adopt a multi-cloud strategy by 2024.

But despite the benefits, it’s also been proven that multi-cloud can be complicated and even overwhelming. It requires an entirely new approach to optimize workloads, simplify development and tackle a growing pattern of operational complexities. This has been described as cloud chaos, where greater cloud choice has led to a massive spike in complexity. Most report that building new apps is slow and cumbersome, and managing their entire app portfolio across disparate clouds is difficult and expensive. No wonder, when each cloud requires its teams to use proprietary tools that are siloed and incompatible with one another. Meanwhile, for the average employee, getting fast, secure access to critical apps from anywhere is imperative, yet it’s often a challenge. All this complexity has broad repercussions on the business, from managing costs to attracting and retaining top talent.

Tackling this multi-cloud complexity needs to start with creating a layer of abstraction that spans various, diverse cloud environments and doesn’t hinder access to each cloud provider’s unique services. However, organizations must take it a step further to solve the challenge of how best to build, manage, govern and optimize apps and workloads across a multi-cloud environment.

Multi-Cloud Services

Multi-cloud services—a fast-emerging category of IT services—are the key to helping organizations implement a multi-cloud strategy that simplifies and streamlines development, operations, networking and security across clouds. A multi-cloud service provides a consistent API, object model, identity management and other core functions across clouds. And, it has one or more of the following characteristics:

1. Runs on a single cloud but supports interactions with at least two different clouds
2. Runs on multiple clouds and supports interactions with at least two different clouds
3. Runs on any cloud or edge, even in disconnected mode, and basic operations are fully automated

Multi-cloud services can be used to standardize cloud infrastructure and operations into one platform to reduce the complexity of individually building or consuming the equivalent native services from multiple clouds. In this model, public clouds, data centers, private clouds and edge locations are all verticals, and multi-cloud services are horizontals that provide functionality across these environments.

Let’s take a closer look at the five most common categories of multi-cloud services and when to use them.

1. Application Services

Application services allow teams to use core services in a standardized manner across clouds. These core services include observability, replication, backup, and restoration. By implementing application services, enterprises can increase their speed of innovation and make their technologies easier to use. Examples of application services include developer tooling, databases, modular development platforms, artificial intelligence, machine learning and serverless and CI/CD capabilities.

2. Infrastructure Services

If your organization chooses to run its applications as VMs, containers, or both, using infrastructure services is the right move to standardize management and operations across the clouds. Different types of multi-cloud infrastructure services include the ability to manage and observe compute, storage and network services leveraging VMs or containers, infrastructure automation and Kubernetes management solutions. When applied, infrastructure services can improve resiliency, security, performance, interoperability and portability.

3. Security Services

When using multiple clouds, an issue enterprises run into is that each individual cloud has its own security tools and approaches, making it difficult to ensure your enterprise is protected against cyber threats. That’s where multi-cloud security services come in – centralizing security operations and secure software supply chains. Some examples of multi-cloud security services are network detection and response, endpoint detection and response, next-gen antivirus and secure access service edge. These services enable organizations to achieve cohesive visibility, context and control by providing a single interface.

4. End-User Services: Access to Data and Services

No matter how big your enterprise is, or how many applications you use, it’s necessary to provide the right level of access to applications and data. But this can prove difficult when you have a multi-cloud infrastructure. Multi-cloud end-user services, which consist of virtual desktops, device management and end-user application delivery, will provide your organization a uniform way for all employees to have quick, secure access to your applications, all while simplifying device management.

5. Data Plane

In the past, workloads were restricted to a single data plane contained within a single public or private cloud or edge location. Now with the rise of multi-cloud services, enterprises can run workloads in the multi-cloud data plane. A prime example of workloads running in the multi-cloud data plane is a distributed application that runs in multiple cloud provider environments. Part of the application might run in the private cloud and the other services are deployed in a public cloud. The multi-cloud data plane is where the containers, services and data exist and where user interaction occurs.

From Cloud Chaos to Cloud Smart

The desired destination of every multi-cloud journey should be to move from a position of cloud chaos to one of cloud smart. Being cloud smart means taking an architected and planned approach to multi-cloud and digital transformation. As more enterprises adopt multiple clouds, multi-cloud services will be key to becoming cloud smart by helping to reduce complexity and risk and proving the freedom to select the right cloud for all situations based on technical and business requirements.

October 4, 2022
How to Go Multi-Cloud: A Simple Three-Step Approach
Odds are your business already uses cloud technology for automation, cybersecurity, IT infrastructure updates and other purposes. But you don’t have to stick to a single cloud server or provider. In fact, you can take advantage of multi-cloud strategies for your organization and reap the benefits for years to come.

That said, multi-cloud adoption and optimization can be tricky, especially if you don’t have a plan in mind from the get-go. Today, let’s break down a simplistic yet effective three-step approach to go multi-cloud without running into major problems.

Step One: Map Your Cloud Zoning Policy

Your first major step should be to map out your cloud zoning policy and plan. In a nutshell, the cloud zoning choices you make can impact your commitments, costs, and even the eventual functionality of the multi-cloud setup.

By mapping out your clouds, you also map out which processes and applications will run on each individual cloud server or provider. In essence, you decide what gets locked into one cloud, what data has to be ported between clouds and what applications must run on multiple clouds simultaneously.

Need an example? A cloud zoning policy might cover whether you keep your web surfing or data analytics on separate cloud providers or on the same cloud servers.

You should map your cloud zoning policy regardless of whether you do the setup or hire a service. In the latter case, offering a read-to-go zoning map will make the service’s job easier and minimize the possibility of mistakes and/or hiccups.

How to Determine Optimal Cloud Zoning

Naturally, figuring out how to optimize cloud zoning can be difficult. The best way to do this is to determine your special focus areas. Alternatively, consider how you want your multi-cloud strategy to best benefit your organization.

Say that you want to guarantee 100% uptime for your customers or visitors, even if your service goes down or if you experience a data breach. In this case, you can set up your cloud zoning strategy to balance the data load across multiple clouds at the same time.

Or say that you want to make sure your users have global availability 24/7/365. In that case, you can set your cloud zoning policies to ensure that users can access your data or websites from anywhere around the globe at any point.

Essentially, figure out what matters most to your organization–and what you want the most from multi-cloud optimization–and zone your cloud applications and policies accordingly.

Step Two: Architect the Multi-Cloud Environment

The next major step is to architect your multi-cloud environment. This involves looking at the high-level design of the environment and creating a rock-solid foundation for multi-cloud servers.

At this stage, you should have at least some idea of how your business will grow and how the multi-cloud environment will assist with its scalability and resource needs. You need to know:
- Where your data science and machine learning applications need to be
- Where your product application serves from
- Where your data warehousing takes place
- Where cloud security is run from
- How each of those functions grows alongside the others
In the architecture phase of a multi-cloud setup, you need to figure out which projects and apps are so-called “cloud agnostic.” Cloud agnostic projects and applications don’t have to be portable; because of this, they can lean on managed services or proprietary IT infrastructure from your organization.

How to Set Up an Ideal Multi-Cloud Environment

To reap the most benefits out of a multi-cloud environment, you should adopt a containerized and flexible approach. Not only is this cost advantageous, it also allows you to set up your multi-cloud environment with as much versatility as possible.

If you architect or plan your multi-cloud environment so it is containerized and flexible, you can work with practically any infrastructure-as-a-service (IaaS) provider. Therefore, you can transition to different cloud hosts or service providers whenever necessary according to your budget or other factors.

To do this, be sure to perform heavy forecasting. You need to know how much data storage your computing will require, the database utilization your organization will need, the number of computer nodes you’ll likely require and so on.

As a side bonus, containerization in your multi-cloud setup means that if one server or process goes down, the others are less likely to fail from a cascade effect as well.

Step Three: Prep for Contracts and Forecast Costs

The last step in going multi-cloud for your organization is handling the financial aspect of the process. You need to prepare for contracts and commitments alongside forecasting costs.

Forecasting is critical in this phase because you’ll prepare for contracts and select different cloud services. Because of this, you need to understand how flexible your budget is relative to your infrastructure requirements.

Specifically, you’ll need to match the costs to every multi-cloud forecast and build your overall resource and money consumption budget. Basically, you need to know:
- How much you’ll likely spend on multi-cloud processing and services
- Whether you need certain features, such as website monitoring tools like Visualping
- Whether your budget can take that forecasted strain
If the answer to the third question is “no,” you may need to go with a more affordable service or adjust your multi-cloud environment architecture and zoning policies. By forecasting costs ahead of time, you won’t run into a disaster scenario where you already have your multi-caught environment up and running but can’t pay for it, forcing you to scramble as a result.

Minimize Commitment Risk

Luckily, there are ways to minimize commitment risk and avoid budgetary disasters. For example, you can use commitment buy-back guarantees, or you can use flexible commitment options like that from AWS. These include computing savings plans, which consume cloud resources in any region and which result in very low savings rates.

Of course, you can and should also practice very careful budgeting and accounting. Once you ensure that your commitment costs and savings are attributed to the right services, server resources, applications, etc., you’ll have a better idea of how much money and other resources you really need and avoid over-committing to a provider that is too expensive.

Then you can further avoid commitment risk when planning your migrations of applications between different vendors carefully. Budgetary costs can skyrocket if migrating applications and data between vendors take longer or result in unexpected complications. Therefore, you should make sure that your migrations go quickly and easily or that a cloud service provider offers assistance during this phase (possibly as part of a deal to get you to sign with them in the first place).

Wrapping Up

As you can see, you can transition your organization to multi-cloud in a matter of months. But even if you follow the above steps perfectly, remember that you won’t optimize your commitments, performance and costs up to 100%. Still, you can prep your organization for long-term success and multi-cloud benefits with the right preparation and planning.

With the right cloud services provider, you’ll gain additional assistance and support throughout this process and be able to take advantage of the extra resources from a multi-cloud setup quickly and easily.
September 6, 2022
Orgs Struggle to Get App Modernization Right

A survey published today found 93% of IT leader respondents admitted that application modernization efforts are either extremely or somewhat challenging.

The survey, which polled 150 IT leaders at organizations with at least 1,000 employees, was conducted by Asperitas, an IT services provider. The survey found the primary issues respondents identified were identifying the right tools and technologies (30%) followed by finding staff with the right experience (20%), choosing the right external partner (18%), getting users trained on new systems (17%) and securing the support of management (11%).

More than three-quarters of respondents (78%) said the top priority for organizations taking on application modernization should be “getting it right.”

The definition of application modernization is, like many things, in the eye of the beholder. One-third of respondents defined modernization as the use of cloud-based infrastructure while 32% said it was moving legacy systems to the cloud. Another 22% defined it as using cloud-based tools.

More than half of respondents (54%) said employing microservices meant that they were modernizing, while 59% said the use of any cloud-native services indicates as much.

Scott Wheeler, practice lead at Asperitas, said that while there are a lot of definitions of application modernization, it’s clear that the lift-and-shift era of moving applications to the cloud is coming to an end. Organizations are instead preferring to build and deploy applications using a wide range of cloud-native platforms and platform-as-a-service (PaaS) environments they don’t have to manage.

The challenge is many of these applications are being built and deployed on multiple clouds. More than a quarter (27%) said the biggest multi-cloud challenge was securing applications, followed closely by integration with on-premises systems and other cloud platforms (26%). Another 20% identified ensuring portability between platforms as the biggest challenge.

A total of 80% also noted upskilling of internal staff and making improvements to antiquated processes were extremely important to application modernization. In fact, one of the major issues that organizations still need to address is the degree to which “bad DevOps” practices are being applied to modern application environments, said Wheeler. Much of that issue stems from the simple fact that many IT teams still don’t know how to effectively manage cloud computing platforms, he added.

Well over a third (36%) said that a key application modernization best practice was giving yourself ample time to complete the project while an equal percentage identified avoiding shortcuts, distractions and/or partners that over-promise. Another 34% said the most important lesson was choosing the best tool for the job, while nearly a quarter (24%) identified waiting too long to modernize a system that’s clearly in need of modernization.

A full 100% of respondents said application modernization was very or somewhat important to their business, with 45% reporting the applications they had chosen to modernize were critical. A little more than a quarter (28%) also noted modernization has enabled them to innovate faster and scale more rapidly, while 25% said it could improve end user productivity and 11% said it might make their company more attractive to employees and customers.

Regardless of the motivation for modernizing applications, just about every organization is engaged in these efforts to varying degrees. The issue doesn’t seem to be the willingness to start these projects as much as it is making sure they are completed quickly enough to be relevant to the business.

August 4, 2022