Kensu is the first solution to bring advanced data observability capabilities to support Matillion, empowering organizations to gain richer insights into their data pipelines and ultimately strengthening trust and data productivity. Matillion ETL is a popular tool for building and orchestrating data integration workflows. It simplifies extracting data from various sources, transforming it according to business requirements, and loading it into a cloud data platform.

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.

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.

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.

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.

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!