Google Cloud Platform — Technology Nuggets — January 16–31, 2022 Edition

Welcome to the January 16–31, 2022 edition of Google Cloud Technology Nuggets.

All Products Page

The Google Cloud console does its best to keep the user experience efficient enough to help you navigate to the services that you often use. With the increase in the number of services, the main menu navigation available at the top left can be a bit daunting at times and currently one works around that via “pinning” the most often used services. With the new All Products page, Google Console console takes this a step further by providing a new top level menu that allows you to see the products in an intuitive way. The documentation links, starter guide links are provided next to each product with the option to pin them if needed. This is a much needed feature and one that you should definitely check out in your console. Check out the blog post for more details.

Infrastructure

Google Cloud launched Tau VMs, based on AMD EPYC 7003 processors that provide leading price performance in the industry. Several of Google Cloud customers, who have been using Tau VMs have seen significant performance benefits and price performance numbers. Do note that Tau VMs are available in predefined configurations and are also available at Spot VMs. Check out the customer experiences over here.

Google Cloud VMware Engine has seen key updates: support for NetApp Cloud Volumes Service, service availability in Toronto with expansion into a second zone in Frankfurt and Sydney, a 60-day single node non-production environment for VMware Engine that allows you to do proofs-of-concept, compliance certifications and more. Check out the blog post that goes into details for each of the above updates.

Customers

Sabre does not need any introduction and is a key provider of technology for its partners ranging from airlines, travel websites, agencies, and hotels. As part of its 10-year program to modernize its technology landscape, it has been looking at various Google Cloud services to help meet its current and future needs, plus keeping the expectations of users in mind. Often this comes down to ensuring that data is available globally and is in sync. Additionally, certain use cases like flight itineraries require a low-latency delivery to the end users. As you would have guessed, Cloud Spanner and Cloud Bigtable play a key role here. Read from the Chief Architect of Sabre Labs, how these databases are meeting their stringent requirements.

Databases

If you have been using Cloud SQL for MySQL and have been struggling with the audit logs, you can now use the new Cloud SQL for MySQL Audit Plugin that offers enterprise-grade database auditing features. The plugin masks sensitive data out of the audit logs and sends them across to Cloud Logging, where you can view who did what on which resource. Additionally, you can even define log-sinks to send them to Google Cloud Storage (GCS),. BigQuery and other log management tools. Check out the blog post for more details.

Cloud Bigtable has seen some significant updates:

  • Now in General Availability (GA) : Autoscaling for Bigtable that automatically adds or removes capacity in response to the changing demand for your applications.
  • Storage capacity per node has been doubled. 2X storage limit that lets you store more data for less.
  • Recently Bigtable introduced a feature to allow an instance to be deployed in up to 8 regions, to allow for better customer experience.A new feature Cluster group routing will allow you to determine where to route your application traffic, thereby avoiding certain clusters that are busy serving customer requests.
  • More granular utilization metrics to help you understand CPU usage and how the instance resources are getting used.

Check out the blog post for more details.

Machine Learning

It is critical that organizations that deploy machine learning models are able to understand some of the factors that were used to make an inference. This is critical in regulated industries, where audits are often required to understand the decisions made by these models. Explainable AI has been about why and how these models make decisions. Google Cloud Vertex AI provides these services to help you understand the factors behind those decisions, but what if you are a citizen data scientist, possessing SQL knowledge and would like to do the same. This blog post goes into the details of how you can build a Machine Learning model using SQL in BigQueryML and then explain those model predictions to stakeholders and domain experts using “What-If Scenario Dashboards” in Looker.

Operations

Google Cloud Deploy, a Continuous Delivery (CD) tool is now GA. It is a fully managed, opinionated continuous delivery service that makes continuous delivery to GKE easier, faster, and more reliable. Cloud Deploy works with GKE tooling systems to ensure that it integrates with the CI tools, supports configuration management and provides notifications for software delivery downstream. It integrates with Google Cloud IAM, Service Controls, Logging and Monitoring with built-in metrics. Check out the documentation and tutorials.

Several other features were announced around tools / services in the Cloud Operations Suite:

  • Delivering your Alert Notifications to various notification channels which can then deliver to your own applications and/or formats, got a boost with the release with Pub/Sub, Webhook and Slack notification channels, which are now in GA. Other channels like Email, SMS, Mobile and PagerDuty (Beta) continue to be available.
  • Continuing with the previous point, Google Cloud Monitoring delivers alerts only to supported channels. Often customers want to route these alerts to their own third-party systems. Here is a step-by-step guide, where you can set up a generic application to deliver these alerts to any third-party system that supports delivery via webhooks.
  • An interesting set of solution architectures created for a couple of customers, who had to deal with large amounts of logs from hybrid and multi-cloud applications and which had to be in near real-time and with effective cost-management.

https://cloud.google.com/blog/products/operations/pub-sub-webook-and-slack-notifications-are-now-available

https://cloud.google.com/blog/products/operations/write-and-deploy-cloud-monitoring-alert-notifications-to-third-party-services

App Development

If you are developing Kubernetes-based applications, chances are that you have come across Skaffold, which is an open-source tool that simplifies your development experience with development, deploying and testing of Cloud Native applications. It also provides building blocks for creating CI/CD pipelines.

What if you had an application with multiple microservices and you wanted to do iterative development for each of the microservices. Module support was recently added to Visual Studio Code, via Cloud Code, which is Google Clouds Google Cloud’s IDE plugin. Check out the blog post for a complete example of how to develop and debug a sample microservice application with Skaffold modules from your IDE.

One of the earliest and most popular services available on Google Cloud Platform is seeing a key component: Java Runtime for App Engine being made available as open source. The idea behind open sourcing this key technology is to help customers understand what goes on under the hood. It is also a first step to help you run the whole App Engine environment wherever you want: on your local development environment or on-premise in your own data center. What is interesting is also the transition to ensure that the future runtimes are based on newer Long-Term Support versions of the Java programming language.

The binary artifacts are available in the Maven central repository, including Javadocs and sources. Get started with the repository on Github. Read the blog post for more details.

Let’s learn about GCP

In our learning section, we have two fantastic guides to Data Science on Google Cloud and DevOps on Google Cloud.

The Introduction to Data Science on Google Cloud is a solid guide to first understanding the multiple steps involved in Data Engineering right from ingestion, processing, storage, querying and visualization and/or machine learning applications. Each of these areas is highlighted with the set of technologies on Google Cloud, codelabs, solution architectures and more. If you are into Data Science, this is a link you need to bookmark and then navigate into the various tools.

The next series is on DevOps on Google Cloud, that does a deep dive into DevOps and how you can implement that on Google Cloud. The series has a unique conversational style that takes customer requirements and then translates them into how Google Cloud services can help to meet those requirements and bring in the transformation. Do check it out : Google Cloud DevOps Series

Stay in Touch!

Have questions, comments, or other feedback. Do send it across.

Looking to keep a tab on new Google Cloud product announcements? Check out this handy page that you should bookmark → What’s new with Google Cloud.

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