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Observability

The latest News and Information on Observabilty for complex systems and related technologies.

New Year's (observability) Resolutions

A new year has started and I've been pondering my hopes and dreams for the year to come. In the world of SRE, observability is the most prominent pillar of my work. So, I decided to drill into the topic of observability and what I'd like to see happen in the industry in 2023. Rather than focusing on any tool, technology, or methodology, I'lll be exploring concepts that can be broadly applied in any organization.

Introducing CloudZero Support For New Relic: Enabling A More Efficient Approach To Observability

As a leader in observability and application performance monitoring (APM), New Relic empowers engineers with a data-driven approach to planning, building, deploying, and running great software. Last month, we announced support for New Relic on the CloudZero platform. With this new functionality, customers can gain visibility into their New Relic spend, combine it with any other IT or infrastructure spend, and achieve a complete view of business dimensions — such as products and customers.

Elastic Observability 8.6: Maximizing operational efficiencies with improved application analysis and workflow integrations

Elastic Observability 8.6 introduces a set of capabilities improving production operations through the introduction of host (EC2/GCP compute/Azure compute) observability, application dependency operations views (insights into databases, caches, etc), and a new connector for Opsgenie. These new features allow customers to: Elastic Observability 8.6 is available now on Elastic Cloud — the only hosted Elasticsearch offering to include all of the new features in this latest release.

The Importance of Observability

While IT pros know they need to monitor IT services, they also know it can be the most difficult part of their job. Traditionally, enterprises have cobbled together several disparate monitoring products to address all their monitoring needs – but there are often gaps. Within these gaps, issues are missed, and the possibility of proactive issue resolution becomes nearly impossible.

What Databases Taught Me About Scaling Observability

I recently attended a virtual event and heard the speaker comment, “Relational databases don’t scale.” To my ears, this is about as silly a statement as saying, “No one can eat 26 hot dogs in 12 minutes” right before Kobayashi shows up and eats 50. In my experience, relational databases scale when they’re placed in the hands of someone who knows what they’re doing. Just imagine if Kobayashi was your data architect!
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The Five Myths of Observability

Observability is a term that has gained a lot of traction in recent years, particularly in the realm of software engineering and DevOps. At its core, observability refers to the ability to gain insight into the internal workings of a system by observing its external outputs. This allows engineers to diagnose and troubleshoot issues with the system, as well as to monitor its performance and behaviour.

How can observability cultivate collaboration among engineering teams?

If an application breaks, much time is spent shifting blame instead of solving the problem at hand. With synthetic monitoring, teams can come together to identify problems before they occur and hence assign them to the correct people to get them solved.

How to monitor Kubernetes with Grafana and Prometheus: Inside Powder's observability stack

David Calvert is a site reliability engineer working remotely from the south of France. He’s currently focused on observability, reliability, and security aspects of cloud infrastructure. You can find him as dotdc on GitHub and @0xDC_ on Twitter. Over the past three years, I’ve built and operated Kubernetes clusters for two different companies — the first one on-premises, and the second on a public cloud platform for my current job at Powder.

How to Deploy a Cribl Stream Leader, Cribl Stream Worker, and Redis Containers via Docker

In this video, we’ll walk through how to deploy a Cribl Stream leader, Stream worker, and Redis containers via Docker. Then we’ll show how we can bulk load data into Redis, then use it to enrich data in Stream.

Author's Cut-A Sample of Sampling, and a Whole Lot of Observability at Scale

Brick by brick, block by block—if you’ve been with us throughout our Author’s Cut blog series (and if you haven’t, you can go catch up), you’ve seen us build the case for observability from the ground up. We’ve covered structured events, the core analysis loop, and use cases for managing applications in production—and that’s just to start.