Bugs can remain dormant in a system for a long time, until they suddenly manifest themselves in weird and unexpected ways. The deeper in the stack they are, the more surprising they tend to be. One such bug reared its head within our columnar datastore in May this year, but had been present for more than two years before detection.
Observability is the ability to see and understand the internal state of a system from its external outputs. Logs, Metrics, and Traces, collectively called observability data, are external outputs widely considered to be three pillars of observability.
Just like shopping on Black Friday, AWS re:Invent has become a post-Thanksgiving tradition for some of us at Datadog. We were excited to join tens of thousands of fellow AWS users and partners for this annual gathering that features new product announcements, technical sessions, networking, and fun. This year, we saw three themes emerge from the conference announcements and sessions.
Scrum metrics are an essential indicator of your team’s progress. In an agile team, they help you understand the pace and progress of every sprint, ascertain whether you’re on track for timely delivery or not, and more. Although scrum metrics are essential, they are only one facet of the delivery process — sure, they ensure you’re on track, but how do you ensure that there are no roadblocks during development? That’s precisely where observability helps.
With digital transformations continuing apace and the popularity of cloud-native and microservice-based applications and architectures growing, Gartner sees investments in such technologies and services increasing, predicting that "cloud-native platforms will serve as the foundation for more than 95% of new digital initiatives by 2025 - up from less than 40% in 2021."
As a developer, how much time do you actually spend writing code? According to this survey by The New Stack, developers spend less than one-third (32%) of their time writing new or improving existing code. And this is because they get stuck in multiple tasks that aren’t really part of their main responsibilities.
Observability data is mission-critical for businesses that want to provide stellar customer experiences, remain secure and compliant, and mitigate risk. However, organizations are creating more data as they expand their digital presence. Its increasing volume and complexity have teams looking for solutions that enable them to better control that data, derive more value by making it actionable, and all while keeping their costs under control.
When we created Cribl Search, we wanted to give systems administrators the ability to query data without having to spend resources on collection and processing first — but we didn’t stop there. With Search, we’re also making it possible to query all the data you’ve already collected, processed, and kept in places like object stores, file systems, analytics tools, S3 buckets, or other data stores.
Our product strategy this year was relatively simple. Many observability practitioners we spoke with complained that observability was oftentimes slow, heavy, complex, and costly – which can be summed up in our CEO’s recent blog on modern observability challenges. While our customers didn’t report similar challenges, we wanted to further distance ourselves from this typical observability experience.
