In DevOps world, everything is connected

 

The Law of Divine oneness says that Everything is connected to everything else. What we think, say, do and believe will have a corresponding effect on others and the world around us. As you gain a fuller understanding of the law, you will see how we all are related and overlap each other and impact the world we live in. But I don’t wish to discuss it on a spiritual or metaphysical level, let’s discuss it in the context of the role of IT in DevOps or for that matter role of DevOps in IT.

 

My Lean Kanban guru and friend Masa K Maeda says and I quote him “The critical problem is not understanding the problem and trying to solve it”. Today we are facing very real challenges that need new and innovative ways to handle them, we really need to understand the real problem and then solve it.

DevOps is different things to different people. Now that might not be a bad thing. In my opinion the greatest thing about the DevOps movement is that people in one discipline started reaching out to teams in other disciplines looking for ways to collaborate and become more efficient to support the ongoing success of the business. That’s the real value of DevOps… the oneness, the feeling of connection.

Once the DevOps as a concept is understood, the practices and tools will evolve around it as the IT field will. It might be called something else in the future but it marks the introduction of a number of improvements at all levels within the IT organization. It is the next evolutionary step that IT (dev, test, ops, networking, security etc.) will take to address today’s challenges.

1. Big Data

Given the complexity and specialised nature of fields, the big data scientists and specifically ones dealing with the analytical sciences portion of big data tend to work in research mode. Therefore, these big data analysts and big data developers formed their own group apart from the operations side of the house. This separation of functions is how many big data companies still operate to this day. For instance the predictive analysis for financial firms or consumer data analysis for online retailers, some big data projects are more volatile and challenging than originally expected. There is an increased pressure to produce results in small amount of time and react at a fast speed. This forces analytics scientists to revamp their algorithms continuously, at times change them altogether. These major changes in analytic models often require drastically different infrastructure resource requirements than was originally planned for. When the infrastructure change requests do finally trickle to operations team, the lag in communication and resource allocation coordination slows down progress. This slowdown can affect any potential competitive advantage that big data analytics can provide. This is precisely why a DevOps can help and thus is much needed.

2. IoT

Everyone out there seems to be very excited about IoT, the next big thing! The idea of connected devices, smart sensors, cars, appliances, homes, offices and even the entire city, as if we are living in some science-fiction movie. In the recently concluded doppa17 conference, my friends Shardul Rao and Shital Kumar spoke about the practical usage of IoT and it’s implementation at a large scale, and then came the risks and challenges associated with it.

I wonder when it comes to IoT who really thinks of the deployment, management, security, etc.? Definitely not the marketing or the ideation teams, this is something IT teams needs to address jointly – across silos. DevOps practices can guide teams to do the right thing. Success is only possible if everything from cradle to grave is taken into consideration.

3. Cloud

Today cloud adoption and it’s success seems synonymous with the success of CIOs, they go together. If everyone involved in the ongoing success of a cloud connected app or a cloud service is not properly involved in the process of creation, testing, deployment, and improvement, there is very slim chance to be successful. Too much time will be spent on dealing with “old way of working” problems that there is almost a guarantee of failure. Infrastructure as Code is required for successful cloud implementation. Cloud providers created programmable infrastructure. Cloud and DevOps go hand in hand – while you can have one without another, they are much stronger when applied together. But you will still hear “cloud is only for startups”, “cloud is not compatible with our IT processes”, or “cloud is insecure”. Regressive? Too Old? I don’t know.

4. Machine Learning

I am no expert in Machine Learning (or anything for that matter 🙂 ), I was reading a bit old but very interesting blog by Steve Burton, let me share his views. “There is only so much information we as humans can observe, digest and interpret at any one time. I’ve noticed a lot of the new Application Performance Monitoring (APM) startups believe that real-time dashboards are the future, where operators manually pick from thousands of different metrics and overlay them over time-series charts hoping to spot anomalies. This all sounds good in theory, except it’s completely unmanageable in reality when your applications have thousands of components, billions of metrics and hundreds of changes every day. Humans can no longer cope with this complexity, they now need machines to do the leg work, process this Big Data and provide operational insight into what the f**k is going on.”

Now see the connection, here Machine Learning feeds to DevOps and to some extent the other way around will also be true.

While designing the CP-DOF course my friends and co-creators of the course, Mukta Aphale, Valerian D’Souza and Sunket Ingale, always kept the culture aspect of DevOps in mind and along with the processes and tools that are useful in DevOps. We gave ample focus on the culture, the connectivity aspect of it.

We at DevOps++ Alliance always take the holistic picture of DevOps and that’s why we are DevOps++… without the ++, the DevOps has no purpose.

Our charter:

1. Develop actionable learning roadmap for enterprise adoption and use of DevOps practices across the roles.

2. Identify the learning milestones that can be evaluated, certified and thus recognised.

3. Connect the learning roadmap in DevOps with other areas like Big Data, Lean, IoT and Analytics.

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