Systems | Development | Analytics | API | Testing



Distributed tracing with Envoy, Kuma, Grafana Agent, and Jaeger

As a cloud service provider, observability is a critical subject as it's strongly related to the availability of the services running on the platform. We need to understand everything that is happening on our platform to troubleshoot errors as fast as possible and improve performance issues. A year ago, while the platform was still in private beta, we faced a tough reliability issue: users were facing random 500 errors when accessing their applications.


DataOps Observability Designed for Data Teams

Today every company is a data company. And even with all the great new data systems and technologies, it’s people—data teams—who unlock the power of data to drive business value. But today’s data teams are getting bogged down. They’re struggling to keep pace with the increased volume, velocity, variety, complexity—and cost—of the modern data stack. That’s where Unravel DataOps observability comes in.


DataOps Observability: The Missing Link for Data Teams

As organizations invest ever more heavily in modernizing their data stacks, data teams—the people who actually deliver the value of data to the business—are finding it increasingly difficult to manage the performance, cost, and quality of these complex systems. Data teams today find themselves in much the same boat as software teams were 10+ years ago. Software teams have dug themselves out the hole with DevOps best practices and tools—chief among them full-stack observability.

A DataOps Observability Dialogue: Empowering DevOps for Data Teams

A DataOps Observability Dialogue: Empowering DevOps for Data Teams It used to be said that software is eating the world, but now data is running things. And it’s high-functioning data teams who make it all happen. But data teams are facing several obstacles that prevent them from delivering innovative analytics at today’s increased speed and scale. Software teams have been facing the same challenges for 10+ years and have tackled them with DevOps. So why are DataOps teams struggling when DevOps teams aren’t? They’re using the same tools to solve basically the same problem. . . .

Data Observability: How to Make Your Data Work for You

Ecommerce companies like yours collect and store data for analytical activities such as business intelligence (BI). However, many organizations don't know how to harness the power of data or improve the efficiency of analytics. Data observability solves the problems of the modern data infrastructure by helping you understand the current state or health of data in your Ecommerce enterprise.

Observability trends and future best practices, with Daniel González Lopes (k6 Office Hours #59)

What are some observability trends and best practices in 2022 and going forward? Technical Program Manager Daniel González Lopes joins Developer Advocates Nicole van der Hoeven and Paul Balogh to chat about trending concepts and new areas of exploration in the field of observability. We're going to talk about distributed tracing, continuous profiling, eBPF monitoring, and more!

Distributed Tracing on Kubernetes | Andrew Kew | QuadCorps | Kongcast Episode 20

In this episode of Kongcast, Viktor speaks with Andrew Kew, Director at QuadCorps and Sr. Field Engineer at Kong, about the pillars of observability, distributed tracing on Kubernetes, and tools that can help you get the most out of distributed tracing.
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Using Open Source for API Observability

API Observability isn't exactly new, however it's popularity has seen rapid growth in the past few years in terms of popularity. API Observability using open source is different from regular API monitoring, as it allows you to get deeper and extract more valuable insights. Although it takes a bit more effort to set up, once you've got an observability infrastructure running it can be immensely helpful not only in catching errors and making debugging easier, but also in finding areas that can be optimized.