Greetings, fellow o11ynaut! You may recall a post we shared here about two months ago that told tales of the themes we felt best represented our recent release of the Framework for an Obsersvability Maturity Model. Well, the o11y maturity model was once again the primary topic and focus of Honeycomb’s most recent Observability Roundtable event held in San Francisco in mid August.
It’s a sad story that has become so common, that it just kind of blends into the background — like that awful elevator jazz that some coffee shops play (Thelonious Monk would NOT approve), or economy class in-flight meals (there’s less sodium on a salt lick, and you don’t get rammed in the ankle by a cabin trolley). Alas, we’re talking about the cart abandonment epidemic. And epidemic is indeed the right word, because this problem is not local or limited.
Analyst firm Gartner recently predicted that “75% of all databases will be deployed or migrated to a cloud platform by 2022, with only 5% ever considered for repatriation to on-premises.” Enterprise architects are deploying analytics, artificial intelligence, and machine learning workloads on cloud database platforms for greater scalability and lower operational overhead.
Okta is a cloud-based identity management service that provides authentication and authorization tools for your organizations’ employees and users. You can use Okta to incorporate single sign-on, multi-factor authentication, and user management services right into your applications.
At Coralogix, we strive to ensure that our customers get a stable, real-time service at scale. As part of this commitment, we are constantly improving our data ingestion pipeline resiliency and performance. Coralogix ingests messages at extremely high rates — up to tens of billions of messages per day. Every one of these records needs to go through our entire pipeline at near real-time rates: validation, parsing, classification, and ingestion to Elasticsearch.
For a lot of DevOps engineers and SREs, a Grafana dashboard is often the beginning of a troubleshooting procedure. It might be an alert in Slack or a colleague pointing out anomalous system behavior. Or maybe it’s just part of your day-to-day monitoring workflow. Whatever the reason, staring at a beautiful Grafana dashboard is the starting point of what can be either a long and excruciating process, or a short and efficient one.
Most of us SCOM users have our servers and infrastructure pieces monitored – but more often than not, the hypervisor remains a big black box where there is no level of monitoring whatsoever. Any kind of outages that come from that lack of monitoring blindside our administrators, and people eventually grow to lose confidence in the platform itself.
We are building Grafana Labs to be a sustainable open source company. In addition to maintaining the open source project and community around Grafana, we offer paid products that help make that possible. Grafana Enterprise was introduced over a year ago and adds features needed by enterprise-level organizations. In this blog post we’ll discuss some of those features and how we decide what goes into Grafana Enterprise.
In our Sentry for Data series, we explain precisely why Sentry is the perfect tool for your data team. The present post focuses on how we used Sentry to make debugging Apache Beam easier (and faster). Since its creation, Sentry has embraced a single vision: help all developer teams build the best software, faster. We want to give developers the information they need to resolve issues quickly, without having to dig through noisy log lines.
