Operations | Monitoring | ITSM | DevOps | Cloud

Challenges of Going Serverless (2020 edition)

While we know the many benefits of going serverless - reduced costs via pay-per-use pricing models, less operational burden/overhead, instant scalability, increased automation - the challenges are often not addressed as comprehensively. The understandable concerns over migrating can stop any architectural decisions and actions being made for fear of getting it wrong and not having the right resources.

Improve Website Performance and Availability with Synthetic Monitoring

Enterprise monitoring tools have traditionally paid attention to the performance of monolithic applications hosted on physical and virtual infrastructure resources. The adoption of cloud native and microservices architectures has brought greater focus to end-user experiences delivered by web applications across different global locations. The bar for a great digital customer experience has never been higher.

Improving Application Quality through Log Analysis

Throughout the history of software development, one statement has remained true: no application is perfect. Due to that fact, development organizations must work with all resources at their disposal to limit the impact that application problems have on the end-user. Server log files represent an important resource that should be referred to during the process for troubleshooting any application issue.

Encryption at rest with Ceph

Do you have a big data center? Do you have terabytes of confidential data stored in that data center? Are you worried that your data might be exposed to malicious attacks? One of the most prominent security features of storage solutions is encryption at rest. This blog will explain this in more detail and how it is implemented in Charmed Ceph, Canonical’s software-defined storage solution.

Data science workflows on Kubernetes with Kubeflow pipelines: Part 2

This blog series is part of the joint collaboration between Canonical and Manceps. Visit our AI consulting and delivery services page to know more. Kubeflow Pipelines are a great way to build portable, scalable machine learning workflows. It is a part of the Kubeflow project that aims to reduce the complexity and time involved with training and deploying machine learning models at scale. For more on Kubeflow, read our Kubernetes for data science: meet Kubeflow post.

Importance of System Resource Monitoring on Graylog, Elasticsearch, and MongoDB Servers

The first thing we tell Graylog users is, “Monitor your disk space.” The core set of metrics discussed below should always be in acceptable parameters and never grow over extended periods without going back to normal levels. This is why it is critical to monitor metrics that come directly from the hosts running your Graylog infrastructure.

Guide to Serverless Information Security

Information security (infosec) is a broad field. Its practitioners behave more like artists than engineers. After all, the mandate for security is not “do X”, but instead “ensure no one can do X, Y, Z, ɑ, β, ɣ, etc.”. The array of possibilities leading to infosec failure are vast. It’s like trying to prove a negative, thus making the task near impossible. On one hand we have an impossible task, on the other we have the affordance of time.

Community Highlight: How Supralog Built an Online Incremental Machine Learning Pipeline with InfluxDB OSS for Capacity Planning

This article was written by Gregory Scafarto, Data Scientist intern at Supralog, in collaboration with InfluxData’s DevRel Anais Dotis-Georgiou. At InfluxData, we pride ourselves on our awesome InfluxDB Community. We’re grateful for all of your contributions and feedback. Whether it’s Telegraf plugins, community templates, awesome InfluxDB projects, or Third Party Flux Packages, your contributions continue to both impress and humble us.

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Achieving a 12% performance lift migrating Raygun's API to .NET Core 3.1

Here at Raygun, improving performance is baked into our culture. We don't just think about our application performance, but more broadly, we look at our own infrastructure and ask if there's anything we can do to make it more performant for our business and for our customers. Two years ago, we switched our API from Node.js to .NET Core and achieved a 2000% increase in throughput. To continue that story, we recently upgraded .NET Core 2.1 to 3.1 and saw a 12% increase in performance. We enjoy presenting our performance findings, so in this post, we'd like to give some context into why we upgraded and the conditions that helped us achieve the 12% performance lift.