If you’ve already centralized your log analysis on BigQuery as your single pane of glass for logs & events…congratulations! With the introduction of Log Analytics (Public Preview), something great is now even better. It leverages BigQuery while also reducing your costs and accelerating your time to value with respect to exporting and analyzing your Google Cloud logs in BigQuery.

Logging can be a life-saver when it comes to discovering bugs or faults in your Go (Golang) code. The three most popular ways to log errors in Golang are: This article will walk you through how to log errors using each method, when and why you’d want to use each, along with examples.

Software engineering is an exciting field that offers various roles and responsibilities to individuals. Some prominent roles are developer, quality assurance engineer, project manager, product manager, DevOps, and many more. Each of these roles also has sub-roles. For example, we have front-end, back-end, and full-stack developers in development. In testing, we have manual testing, automation, unit testing, and end-to-end testing.

More data is available than ever, challenging organizations to change how they interact with their data so they can get the most out of it. Ahmed Munir, Lead SAP Functional Technology Architect Manager at Whirlpool Corporation, has 16 years of SAP leadership experience. He joins us to share what he has learned about building great data teams, upcoming trends in data analytics to keep an eye on, and how data teams will evolve over the next 5-10 years.

Many companies tell you that “the security of our customers’ data is very important to us” in their marketing communications. And you believe them, for a while. But then you discover they were hacked with an open FTP server, using a password like “nameOfTheCompany2022”, and you realise that it’s not that important after all. Why do we mention this, you ask? Well, a few months ago Bugfender got ISO 27001-certified.

As a global leader in technology research, Gartner supports enterprise organizations, non-profits, and government agencies by sharing information and in-depth analysis of emerging technological trends, tools, and products. With the continued growth of big data over the past decade, Gartner has been especially invested in helping data and analytics (D&A) leaders make the right decisions for managing and generating value from data within their organizations.

Debug logs are incredibly valuable for the wealth of information they contain, but generally speaking, companies avoid collecting them in production because they are very expensive in both dollars and performance. When we talk about production observability, we are likely spending more money for more data, data that itself might be excessive.

AutoML with experiment tracking enables logging and tracking results and parameters, to optimize machine learning processes. But current AutoML platforms only train models based on provided data. They lack solutions that automate the entire ML pipeline, leaving data scientists and data engineers to deal with manual operationalization efforts. In this post, we provide an open source solution for AutoMLOps, which automates engineering tasks so that your code is automatically ready for production.