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Observability

The value of data observability to the data analyst

At the beginning of my career as a data analyst, I had to rely on other team members when something went wrong in our data pipeline, often only finding out about it after the event. That experience was one of the driving factors for me to join Kensu. When I spoke with the team for the first time, I had that “lightbulb moment”: data observability is a way of providing help to various data team members, including data analysts, in making their lives more productive and less painful.

Serverless Observability in N|Solid for AWS Lambda

We are excited to release Serverless Observability for N|Solid with support for AWS Lambda. With the growth of organizations leveraging serverless increasing as they realize the performance and cost benefits, we're excited to provide customers with this new visibility into the health and performance of their Node.js apps utilizing Serverless Functions utilizing serverless architectures. Img 1. Serverless Cloud Providers.

Kensu Brings Data Observability to Data Engineers

What can an organization do to troubleshoot flawed data sets before they get into the hands of end-users? In this episode of “Powered by Snowflake,” host Daniel Myers explores that topic with Andy Petrella, Founder and CPO of Kensu, which offers a data observability platform built specifically for data engineers. The conversation includes a demo of the platform that spotlights how it enables data engineers to proactively identify data problems before the data gets to stakeholders.

How Kensu's Integration with Matillion empowers data teams to deliver reliable data

It’s a common thread amongst data-driven organizations: data teams face soaring volumes of data with varying complexities, which raise issues regarding data reliability. Efficiently monitoring data pipelines has become paramount to swiftly identifying and addressing potential data incidents, ensuring minimal impact on data practitioners and end users.

Observability Tools: Cutting Costs Without Compromising on Quality

In software development, striking a balance between cost and quality can sometimes feel as tricky as finding a bug in a spaghetti code. Observability tools face a similar dilemma, often consuming a significant portion of the budget and growing significantly year over year. The irony? The vast majority of the data gathered is never used. As is often the case, the driving force behind this trend is not an emotional response.

Observe Your Phoenix App with Structured Logging

In this post, we'll configure a Phoenix LiveView application to use a structured logger. We'll then use AppSignal to correlate log events with other telemetry signals, like exception reports and traces. Along the way, you'll learn about the benefits of structured logging, and you'll see how to configure a distinct framework and application logger in your Phoenix app. Let's get started!

Episode 3: Cut Observability Costs (SD Times Microwebinars)

If you dig down to the bottom of it, you’ll find that Observability will eat up any budget allocated to it and then some. That’s because the need for more Observability is rarely rooted in engineering needs. It is in fact coming from a much more primal place: the fear of the unknown. This is why Observability is a huge cost driver, growing year over year, and tremendously hard to optimize. Because anything you cut away will be quickly replaced by new data points.

Episode 2: Live Observability (SD Times Microwebinars)

In the first episode, we will discuss the fourth pillar of Observability and how Snapshots are so much better than logs. One of the big benefits of Snapshots in particular and agile Observability in general is that you can adapt your Observability in real-time without requiring code changes or redeployments.