Operations | Monitoring | ITSM | DevOps | Cloud

With the growing utilization of AI, modern business applications rely more and more on machine learning (ML) models. But the complexity of these models poses significant challenges to data scientists, engineers, and MLOps teams seeking to maintain and optimize performance.

In Elasticsearch 8.8, we’re introducing the reroute processor in technical preview that makes it possible to send documents, such as logs, to different data streams, according to flexible routing rules. When using Elastic Observability, this gives you more granular control over your data with regard to retention, permissions, and processing with all the potential benefits of the data stream naming scheme. While optimized for data streams, the reroute processor also works with classic indices.

Application Programming Interfaces (APIs) are a crucial building block in modern software development, allowing applications to communicate with each other and share data consistently. APIs are used to exchange data inside and between organizations, and the widespread adoption of microservices and asynchronous patterns boosted API adoption inside the application itself.

Data is like the oxygen that fuels the digital revolution. While critical and readily available, data becomes dangerous when misused. Leaders and users alike are becoming concerned with how organizations can protect data, especially personal information. It’s a complex and dynamic challenge, making it harder than ever to share data to the extent needed to facilitate innovation and research. To meet these challenges, many organizations are leveraging federated data systems.

RapidSpike is committed to revolutionising website reliability, performance, and security — to make the web faster, safer, and easier for everyone to use. With the direct correlation between website speed and conversion now widely acknowledged, even marginal gains of 0.1% could represent millions of extra revenue for the UK’s largest brands.

As the popularity of Windows Subsystem for Linux increases, the Ubuntu development team is committed to delivering a first class experience for Linux developers on Windows. To achieve this we’ve made improvements to our automated testing workflows via the creation of WSL-specific GitHub actions. In this post, Ubuntu WSL engineer Eduard Gómez Escandell talks us through the motivation for this approach and how you can implement these actions for your own CI workflows.

Recently, I wrote an article discussing why industrial organizations should migrate from legacy data historians to modern, open source technologies. The reasons for such a migration remain valid; however, it dawned on me that such a heavy-handed approach is not always right for every organization.

The evolution of information technology is governed by how businesses adapt to ever-increasing amounts of data. Those businesses most able to exploit more data, win. Terms such as ‘speed’ and ‘agility’ refer to how a business handles and uses this data. Given the 2.5 quintillion bytes created every day, there is a huge opportunity for businesses to create unassailable leadership.

Logging provides a wealth of information about system events, errors, warnings, and activities. When troubleshooting issues, logs can be invaluable for identifying the root cause of problems, understanding the sequence of events leading to an issue, and determining the necessary steps for resolution. By regularly analyzing logs, administrators can identify performance bottlenecks, resource limitations, and abnormal system behavior.

Building Docker images efficiently is crucial for developers and organizations seeking streamlined development and deployment processes, and it’s a real topic for the 50k developers on Qovery; a few weeks ago, a topic was even open on our forum about it. In this article, we will explore three valuable tips gathered along the way to improve the build time of your Dockerfiles, allowing you to optimize your workflow and save valuable time.