The Raygun ingestion pipeline is kept pretty busy processing over 90 million crash reports every day. So needless to say, we need the processing pipeline to be as efficient as possible to reduce resource usage and avoid costly scaling.

Today, I’ll cover Shift Left Monitoring: A Pathway to Optimized Cloud Applications and how left-shifted troubleshooting of Spring Boot code issues using observability tooling can avoid production issues, unnecessary costs and improve product quality. Shift-left is an approach to software development and operations that emphasizes testing, monitoring, and automation earlier in the software development lifecycle.

Welcome to the 28th edition of our monthly product newsletter - SigNal 28! Our team shipped many features and improvements last month. We also had an amazing team workation in Goa, India. Let’s dive in to see what humans at SigNoz were up to in the month of August 2023.

While support for logging in the OpenTelemetry Python project is listed as 'experimental,' it's completely possible to send logs from your Python application. The Opentelemetry Collector has support for numerous existing logging systems, effectively exporting log data from wherever you were sending logs currently; you can also use the filelog receiver to tail and send logs from files. The only 'experimental' portion of the Python SDK is sending logs directly from code-level instrumentation.

Before adopting any new SaaS (Software-as-a-Service) business tools, the IT department of an organization should evaluate whether it is wise to trust the SaaS product and indeed vendor.

You did it! Sure, it might be four weeks overdue and late on a Friday, but you’ve finally finished deploying a long-awaited update to the web app. However, your celebrations are cut short as your phone vibrates off the table. Picking it up, you’re confronted with a developer’s worst nightmare. You’re getting flooded with messages that the login is no longer working. Was it your deployment? This is bad. Nobody can use the site if they can’t log in.

I recently worked on a customer project to migrate an in-house application to the cloud, using a shift-left monitoring and testing strategy. The original application was developed with LAMP architecture and was being migrated to Spring Boot to modernize it and then run it on the cloud. I was fortunate to be part of the conversation during the day-0 talks. Not all IT managers do this.

Up until recently, machines mainly focused on analyzing large, existing amounts of data and finding patterns for a multitude of use cases. This is called “traditional AI.” But lately, machines have also started creating new content. And this is now known as “generative AI.” And given the rise of ChatGPT and its peers, generative artificial intelligence (AI) has quickly emerged as one of the most transformative technologies in recent years.

Artificial Intelligence (AI) is surely revolutionizing numerous industries. The AI market is projected to grow from $150 billion in 2023 to $1,345 billion in 2030, at a whopping 36.8% Compound Annual Growth Rate (CAGR). And at least 35% of companies are already using AI in their business, and an additional 42% are exploring it. However, the exhaustive list of AI business applications is still in the early stages.