Operations | Monitoring | ITSM | DevOps | Cloud

Automating processes and the tools that enable them is vital for empowering highly productive teams. The right automation tools and workflows help DevOps and SRE teams minimize repetitive tasks, improve monitoring capabilities, enable continuous integration/continuous deployment (CI/CD), and work with massive volumes of data.

Kubernetes is a powerful platform that comes with many features to help engineers run their applications more efficiently. However, as you gain more experience and deploy more workloads, you’ll inevitably start looking for ways to optimize Kubernetes performance. There are many ways to approach optimization. On one hand, you could work exclusively with the tools and configurations provided by Kubernetes itself; on the other, you could reap the benefits of third-party tools.

The adoption of AI/ML in financial services is increasing as companies seek to drive more robust, data-driven decision processes as part of their digital transformation journey. For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year. But productionising machine learning at scale is challenging.

The first post in this series introduced the idea of the different layers of value that your business can gain from frequent deployments and focused on the hard costs you can save. We’re looking at the role the database plays here because it’s the most complicated part of the process and it’s difficult to hit aggressive KPIs and goals when your teams are burdened with process bloat due to mistake-prone, manual work.

Toil. Few other words have the same visceral impact for SREs as their four-letter nemesis: toil. Although pretty much everyone recognizes and agrees that toil is bad, it is a term that is frequently misused in colloquial use. In common English usage, toil is defined as “long strenuous fatiguing labor”. As a term of art in the SRE profession, “toil” has several very specific characteristics which distinguish it from other sorts of work which people spend time on.

How can enterprises streamline their operations for improved internal efficiency and customer satisfaction? The right combination of DevOps tools will do just that.

Companies face multiple challenges when migrating their applications and services to the cloud, and one of them is infrastructure management. The ideal scenario would be that all workloads could be containerized. In that case, the organization could use a Kubernetes-based service, like Amazon Web Services (AWS), Google Cloud or Azure, to deploy and manage applications, services and storage in a cloud native environment. Unfortunately, this scenario isn’t always possible.

There are so many interesting and very useful Azure services out there. Over my time in the IT industry, I have seen the evolution of virtualization and eventually the cloud. It has been very interesting to follow the growth and evolution of Azure, and the introduction of Azure Synapse is a great example of how cloud services grow and evolve. Through using Azure, organizations can do things they never thought possible in the traditional data center.

At the scale that Facebook operates, several traditional approaches to serving web content break down or simply aren’t practical. The challenge for Facebook’s engineers has been to keep the site up and running smoothly in spite of handling over two billion active users. This article takes a look at some of the software and techniques they use to accomplish that.