In today’s data-driven world, the need for comprehensive observability has never been greater. Organizations rely on observability to gain insights into their systems’ and applications’ performance, availability, and behavior. However, the traditional approach to observability, which involves ingesting, processing, and storing massive amounts of data, is becoming increasingly challenging and expensive.
Fintech companies operate in a complex technological and regulatory environment. They rely heavily on cloud-native technologies and microservices architectures to handle financial transactions and data, often at a massive scale. To maximize application reliability, fintech companies need full visibility into their software systems and applications. An agile monitoring solution like observability is crucial to improving performance and user experience.
Cribl has a unique position right in the middle of the observability market, giving us a distinct view of all things security, APM, and log analysis. Observability as a concept has exploded into specialized areas over the past two years, and making sense of the players and market forces, particularly in a difficult macro environment, can be tricky. Let’s break it down.
Code instrumentation is an essential practice in modern software development. Not only does it aid in debugging, it ultimately impacts the MTTR (Mean Time to Resolve) for software running in production. With changing software architectures and deployment patterns over the years, approaches to code instrumentation have also undergone a significant shift.
Quickly: if you’re interested in observability for LLMs, we’d love to talk to you! And now for our regularly scheduled content: In early May, we released the first version of our new natural language querying interface, Query Assistant. We also talked a lot about the hard stuff we encountered when building and releasing this feature to all Honeycomb customers. But what we didn’t talk about was how we know how our use of an LLM is doing in production!
On June 28th I will be hosting a webinar, ‘The Fundamentals of Searching Observability Data’. So why should you attend? Because things have, and will continue to change in the way we manage the IT data collected across the enterprise. A recent study shows that enterprises create over 64 zettabytes (ZB) of data, and that number is growing at a 27 percent compound annual growth rate (CAGR). The scary part?
IT, DevOps, and security teams are figuring out the best ways to manage their complex, ever-growing, ever-changing environments. And one contributing factor to all the complexity is the rise of using multiple cloud services. One cloud service to manage is difficult enough, but adding more to the mix — each with its own interface and set of tools — makes everyone’s job significantly more difficult.
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.
