In the eternal hunt for elusive bugs, logging is an indispensable aid. By recording the events and messages that occur during the execution of your program, logging opens the door to unparalleled debugging and performance monitoring capabilities. It all starts with Python’s built-in logging module. However, the true power of Python logging is unlocked not merely by using it, but by mastering it.

Cribl has been named to the 5th annual Enterprise Tech 30 (ET30) – a definitive list of the most promising, private enterprise tech companies. This is our first time on the ET30 list, ranking number four on the list of ten companies in the late stage category. The recognition highlights the value our innovative products deliver to our customers and partners as we work together to unlock the value of all observability data.

Enterprises have enough data, in fact, they are overwhelmed with it, but finding the nuggets of value amongst the data ‘noise’ is not all that simple. It is bucket’d, blob’d, and bestrewn across the enterprise infrastructure in clouds, filesystems, and hosts machines. It’s logs, metrics, traces, config files, and more, but as Jimmy Buffett says, “we’ve all got ’em, we all want ’em, but what do we do with ’em”.

Today's applications are incredibly intricate and interconnected, often relying on numerous third-party services and libraries. With this complexity comes an increased likelihood of things going wrong. However, an error doesn't usually announce itself with great fanfare and a detailed explanation. More often than not, it shows up as an unexplained crash, a suspicious slowdown, or a surprising output. Error logging shines a spotlight on these problems.

Enterprises are accumulating more and more observability and security data in isolated silos, not much different than the dust and spare change under couches and chairs in your grandparent’s rarely-used living room. There is something of value in both examples, but the nature of the value is very unclear and hard to measure without a lot of effort.

InfluxDB is a powerful tool for managing time-series data. It is widely used in industries such as IoT, finance, healthcare, and more. Using InfluxDB, you can query and store large amounts of data in real-time, making it easier to identify patterns, trends, and anomalies. InfluxDB dashboards provide a comprehensive overview of your system performance, metrics, and KPIs in real-time. You can customize these dashboards to meet your specific requirements.

If you’ve worked with NGINX web servers, you know they’re efficient but can generate a lot of log data. While this data is valuable, sorting through it can be a challenge, and the storage and processing costs can quickly add up. This is where BindPlane OP comes in. It helps reduce log volume while still preserving the crucial information. It streamlines your data, filters out the irrelevant bits, and zeroes in on key data points, helping manage storage and keep costs under control.

Access to data is critical for SaaS companies to understand the state of their applications, and how that state affects customer experience. However, most companies use multiple applications, all of which generate their own independent data. This leads to data silos, or a group of raw data that is accessible to one stakeholder or department and not another.

As a big proponent of open source and all things open, I jumped at the opportunity to expand on Cribl Stream’s OpenTelemetry implementation. I’m happy to report that as of Cribl Stream 4.1, both our OpenTelemetry source and destination now support OTLP over HTTP!

At Logz.io, we’re seeing a very fast pace of adoption for Kubernetes–at this point, it’s even outpacing cloud adoption, with companies running on-prem fully adopting Kubernetes in production. Why are companies going in this direction? Kubernetes provides additional layers of abstraction, which helps create business agility and flexibility for deploying critical applications. At the same time, those abstraction layers create additional complexity for observability.