If you have used hosting services like Heroku before, you might be familiar with the user workflow where you run “git push heroku main”, and you see your code being pushed, built, and deployed. When your code is received by the remote git server, your build is started.  With source-based build triggers in Cloud Build, […]

When it comes to detecting threat actors in your cloud infrastructure, Security Operations teams often need to be able to stitch together various bits of data from logs to help tell a story and validate the presence of an attacker. The three core data sources that help identify patterns of attackers in the cloud are […]

Cloud Run, Google Cloud’s serverless container platform, offers a very granular pay-per-use pricing, charging you only for CPU and memory when your app processes requests or events. By default, Cloud Run does not allocate CPU outside of request processing. For a class of workloads that expect to do background processing, this can be problematic. So […]

Editor’s note: When German cooking platform Chefkoch was looking to bring treasured hand-me-down recipes into the 21st century it found a scaleable, well-supported solution with Google’s data cloud. Here’s how it was cooked up.  Whether it’s salad dressing or chicken soup, most households have a favorite dish passed down across the generations. These recipes are […]

Diversity, equity, and inclusion (DEI) are more than buzzwords, they are critical components of workplace culture that have real, tangible impacts on your entire organization. Well-executed and robust DEI initiatives ensure that every single employee feels welcomed and valued when they are at work. And that’s not all—done right, DEI will create a thriving environment […]

Overview Cloud Spanner is a fully managed relational database built for scale with strong consistency and up to 99.999% availability. Key features include the following:  ACID transactions, SQL queries (ANSI 2011 with extensions) and global scale. Automatic sharding – optimizes performance by automatically sharding the data based on request load and size of the data. […]

Introduction ML Ops is an ML engineering culture and practice that aims at unifying ML system development and ML system operation. An important ML Ops design pattern is the ability to formalize ML workflows. This allows them to be reproduced, tracked and analyzed, shared, and more. Pipelines frameworks support this pattern, and are the backbone […]