Google Cloud Technology Nuggets — January 16–31, 2023 Edition

Romin Irani
Google Cloud - Community
6 min readFeb 1


Welcome to the January 16–31, 2023 edition of Google Cloud Technology Nuggets.

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Google Cloud Next ‘23

Google Cloud Next will be held on Aug 29–31, 2023 and is back as an in-person event in San Francisco. Sign up here for information on Next ‘23.

Top 25 Questions and 30-plus cloud computing stats and trends

There are a couple of interesting posts in this edition. First up, is a compilation of Top 25 questions that users asked of Google Cloud in 2022. These questions were determined from the most popular search queries on Google Cloud. It’s both a good insight for Google and all of us to understand what’s most interesting to users to learn about Google Cloud. Check if any of your queries are in the top 25 list :-) ?

Next up is a set of Cloud Computing stats from various research and surveys across categories like Infrastructure, Security, AI/ML and more. Each of the stats have their sources listed for you. Check out the blog post for more details.


Developer Productivity is a key challenge that most organizations face. In terms of infrastructure provided to them to get their tasks done, challenges range from having the right tools, environment mismatch, security issues and many more. Cloud Workstations, a recently released service from Google Cloud, aims to provide fully-managed development environments that addresses this challenge head on. Check out how L’Oreal addressed this challenge for their team spread across geographies with Cloud Workstations.

Kubernetes and GKE

If you manage Kubenertes configurations across multiple clusters, Config Sync is your friend. Even with a tool like that, it is important to know if the sync has happened, what are possible failures that have occurred and more. A new Config Management Dashboard aims to address this requirement and it helps with multiple tasks like installing Config Sync, getting status of sync, reconciling issues and more. Check out the blog post for more details.

GKE Sandbox, Cloud Run, Cloud Functions have been using the gVisor, which provides a safe sandbox environment to isolate and secure workloads. The gVisor filesystem has got a new file system (VFS2) that improves performance across multiple areas as highlighted below. Check out the blog post for more details.

DevOps and SRE

Cloud Logging’s Log Analytics feature has gone General Availability (GA). Log Analytics is powered by Google BiqQuery and it allows you to search, aggregate and transform all log types using the power of SQL. For the GA release, additional features like multi-region support, custom data retention up to 10 years and an improved query experience have been released. Note that to start using Log Analytics, you will need to upgrade your Log Buckets to Log Analytics supported buckets. Check out the blog post for more details.

Software delivery performance is generally measured in terms of the four key metrics : change lead time, deploy frequency, change failure rate, and failure recovery time. It has a direct impact on the organization’s performance. Having said that, since 2021, the State of DevOps Report has been investigating the role of reliability vis-a-vis software delivery performance and there is a direct correlation between reliability and software delivery performance, in a positive way. Check out the blog post that investigates this and provides links for further highlighting how reliability now has wider implications.

Storage, Databases and Data Analytics

There are a variety of fully managed Databases available on Google Cloud ranging from Cloud SQL, Spanner, Bigtable and more. How have some customers used these databases to transform their offerings and achieve key performance and more. Check out this blog post that highlights success stories from 6 customers with Google Cloud databases.

Migrating a SQL Server database to Cloud SQL is not a trivial task since each customer scenario could be different and there is not one fixed way to do that. How do you understand the requirements and plan the activities? Is it a one-time migration or continuous migration? How long would it take to migrate? Check out this post that provides deep guidance on the entire process.

Looker connector is now available for Looker Studio, and Looker Studio Pro. Google Cloud-hosted Looker instance immediately after your Looker admin turns on its Looker Studio integration. Check out the blog post for more details.

BigQuery ML has been supporting univariate time series modeling using the ARIMA_PLUS model. There has been a demand for multivariate time series forecasting support and now with the new model type ARIMA_PLUS_XREG, a public preview of multivariate time series forecasting with external regressors is available. Check out the blog post that provides a step by step guide with examples of how to do that.

Machine Learning

Machine Learning architectures and services in production is a completely different game. When you deploy this in a cloud environment, you have to create a set of best practices typically using landing zone principles that take care of security, data and more. In Google Cloud, we have the Vertex AI set of services and while there are several of them, guidance is necessarily on blueprints to use these services in production. Check out a Blog post that highlights multiple such architectures (Cloud Foundations) that you can use as references.

Identity and Security

Security Command Center (SCC) is Google Cloud’s built-in security and risk management solution that highlights misconfigurations, vulnerabilities, and threats in your Google Cloud environment. SCC has seen two new features: self-service implementation for individual projects and deployment for individual projects. In addition to the features, a pay-as-you-go pricing model has been provided at individual project level. Check out the blog post for more details.

Developers and Practitioners

Uptime checks is an excellent feature available in Cloud Monitoring that lets you periodically validate an application’s availability. Uptime checks can now be enabled directly against a Cloud Run service. The feature ensures that the checks remain in place even if there is a newer revision of the service available. Check out the blog post for more details.

A common point of discussion vis-a-vis Serverless runtimes has been long startup times for the first request to get processed. Java applications have been particularly called out here and significant work has been in progress via Native Image Compilation. Check out this blog post that provides an update around Spring 3’s official native image support, the areas being improved on and a few updates to the Pic-A-Daily codelab that you can experience today to understand native image compilation.

Are you still using a manual pipeline to build and deploy your containerized application from your source code repository to say Cloud Run? If yes, check out this post that automates both the build and deployment to Cloud Run using multiple Google Cloud services like Cloud Build, Artifact Registry and more.

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