Want to know the secret for a swifter and less turbulent Azure Cloud Migration? Let me show you how Gitential can help you optimize your software engineering team’s performance to mitigate risks and “in-flight” emergencies with cloud migrations. Migrations can be very complex, involve significant code changes, app modifications, and even a complete refactoring for architectural alignment. To help you reach your destination, we’ve compiled a few tips to assist with your Azure Cloud Migration and post-migration development.
The top migration challenge
Straight to the point, the root cause behind most problems with Azure Cloud Migrations that we see start with poor planning and thinking it’s possible to take shortcuts. In some cases they might be possible, but not well-advised. Teams already experienced enough in performing migrations and collaborating efficiently together to always deliver a high quality effort can navigate the most efficient course. A proper due diligence effort will uncover the majority of challenges other companies have encountered as a consequence of shortcutting their planning phase. These challenges include database incompatibilities, application dependencies, bandwidth provisioning, capacity planning, and service level agreement requirements. You will also want to have a contingency or disaster recovery plan along with a VPN for end-to-end encryption. The most important tip, and it deserves the underscore, is to not skimp on your due diligence evaluation for defining and creating a checklist of your project requirements. Missing or forgetting to include requirements early on often leads to rework and delays later. Every project is different in size, complexity, and resources, but should apply to the same basic process in planning and conducting any kind of migration. Loosely speaking, and not meant as something you can take to the bank or your C-Levels, most organizations find they can complete their migration within two months, but some may take a year or more. There’s wide variability and an accurate assessment for your situation can only be obtained after determining your requirements. Note, that even after you’ve completed your migration, you will probably want to spend additional time optimizing it.
Improving Azure Cloud Migration with Gitential
Gitential is dedicated to providing software teams the best analytics and recommendation engine to build better, more sustainable code and software products. Our platform analyzes source code and its evolution in git repositories. Gitentials reports provide deep insights to help software engineering managers improve the quality, collaboration, efficiency, and productivity of software development teams. Engineering managers are also provided with an extra layer of risk management if unforeseen technical issues arise in the process of a migration. Let’s take an in-depth look at how Gitential an help you on these four pillars:
Improve Quality. Normally, to understand software engineering team quality drivers requires a labor-intensive process. Your software engineering managers probably have a lot of questions like:
- How much is my team testing and how frequently are their tests failing?
- How old is our code? How much code is unsafe or copied from elsewhere?
- Who’s authoring? Who’s committing? Who’s working on the same code?
Gitential provides insightful data engineering managers can immediately put to use. With it, they can be more proactive in mitigating risks by tracking code hotspots, enforce better code quality, and achieve more cost-efficient codebase maintenance. Example KPI’s: Code Churn, test volume, code structure, aging and error counts. Enhance Collaboration. Sometimes it’s hard to be “socially agile” in understanding team dynamics. With advanced analytics, engineering managers can organize highly productive teams to promote collaboration and breakdown knowledge silos. Some of their questions here might include:
- What is my team setup and what are the team dynamics in my project?
- Who are my top developers in each programming language and who are they working with?
- What is the best team setup I can put together for this project?
Gitential takes out a large portion of the guesswork so engineering managers can better align mentors for those needing it, identify silent A-team players, and create the right “team mix” for any given project. Some KPI’s: Collaboration map, number of contributors, utilization, delay, feedback, among others. Continuously Increase Productivity. One of the greatest challenges in improving productivity is defining where and how it can be improved, and by how much, so efforts can be prioritized. With automated tracking of all activity on your git repositories, more productive meetings may be your toughest challenge! A few of the nearly infinite productivity-related questions are:
- How fast is my team deploying new features, versions, or projects?
- What is my team’s velocity in writing code, testing, and code reviews?
- Are we reusing our code or continuously reinventing the wheel?
The more productive you become… the easier it is to become even more productive. Agile team monitoring from planning, through standups and all the way to deployment helps keep your projects on track. Faster and more predictable deliveries help to pay down your technical debt. Good KPI’s: Velocity, active days, coding hours, code volume, number of commits. Maximize Efficiency. Stripe’s 2018 report The Developer Coefficient indicates that developer inefficiency often exceeds 30%. Gitential enables you to objectively quantify software engineering team performance. Like with improving productivity, you can define where, how, and by what extent efficiency can be improved on a targeted basis. Good engineering managers always want answers to:
- How well are my engineers performing - and in which areas?
- What are their strengths and weaknesses?
- Have code reviews been helping? By how much?
With a direct line into transparent and objective team performance metrics, it becomes quite easy to identify at-risk and vulnerable team members - who’s burned out or looking for a new job? This provides you a chance to find out why and remedy the situation, as needed. There can be many contributors to inefficiency, but often a handful of reasons are the root cause for the majority of it. Useful KPI’s: code efficiency, lead time, code complexity ration, coding hours, code volume, and commit frequency. Gitential provides four levels of visibility for tracking and measuring your software development with the same kind of detail Google Analytics provides for web sites. Any cloud migration will involve code changes that can also be tracked to provide four levels of oversight:
- Organizations. For CTO’s, if your organization is migrating multiple projects to Azure or other Cloud services, you have a direct line of sight on how each is proceeding.
- Projects. Software engineering managers can see how their workloads are progressing, and proactively engage team leaders who appear to be running into difficulties.
- Teams. One of the most useful tools here is our collaboration mapping providing you a view of how much each team member has collaborated with other team members. This can help identify knowledge silos and arrange better pairings and code reviews with team members more experienced with Azure’s environment.
- Individuals. No one’s left guessing when you have detailed metrics on individual code volume, utilization, efficiency vs. utilization, code complexity, and more. This provides an extremely helpful way to find knowledge gaps and other issues to better understand and help individual developers become more productive, knowledgeable and efficient.
Continuous team development
As noted previously in Stripe’s report, developer inefficiency can exceed 30%, an estimated $32.4k based on average US software engineer wages of $107k, according to DAXX (March, 2020). How can you tell whether your team is improving or losing ground? Organizations that have developers doing daily one-hour code reviews are, in a manner of speaking, investing nearly $15,000 per developer per year. What’s the ROI on that investment? In a competitive market, there’s a constant drive to perform better - to increase efficiency, productivity, product quality, better meet customer demands, increase revenue. Demand for software developers, engineers, and really the full spectrum of IT specialists continues and is expected to remain high. Despite the huge pool of IT professionals, qualified applicants are still difficult to find. Performance metrics are not meant to intimidate developers, but to help identify where their skills can be improved. The ROI of improving team skills can be measured, at least in relative terms to more accurate cost estimations, fewer project overruns, increased productivity and efficiency, and reduced technical debt. But hey, feel free to take a look at the performance metrics Gitential can automatically track for you in our free demo. If you like what you see or want to give it a try, start a free trial - we won’t even ask for your credit card!
An extra tip or two for a smooth Azure Cloud Migration
One part of doing the planning to migrate your project to the Azure Cloud is to grab the documentation. Microsoft provides a step-by-step walkthrough of the Azure Cloud Migration process and some free tools to help you: Data Migration Assistant - Detect and receive recommendations on resolving compatibility issues that can impact database functionality and reliability in your transition. Azure Database Migration Guide - Microsoft provides step-by-step guidance for your specific scenario, like migrating from Oracle, MySQL, SAP ASE, etc., to Azure SQL Database and other options. Azure App Service migration assessment - Another Microsoft tool to get a detailed report of the compatibility of the technologies you are using are compatible and alternatives on App Service. An increasingly frequent requirement of organizations entails being able to conduct the migration with zero downtime or decrease in performance. Microsoft provides the technical details and resource specifications for conducting zero downtime migrations. One includes a case study for a high-performance platform involving over 120 TB and 54 onboarded applications.