Google Cloud Platform — Technology Nuggets — February 1–15, 2022 Edition

Welcome to the February 1–15, 2022 edition of Google Cloud Technology Nuggets.

Google Cloud Next ’22 dates have been announced. Mark your calendars for the biggest event of the year for Google Cloud, from October 11–13, 2022.

Google Cloud Products in 4 words or less (2022 Edition)

The latest edition of Google Cloud Products in 4 words or less has been released and you will definitely like the features introduced this time. It’s an interactive version, can work as a flashcard to test your knowledge (useful for certifications) and helps you navigate quickly to product documentation, samples and more. This product search is available in Cloud Console too and has an API to integrate with.

The interactive version is available here and there is a poster that you can download and print too. Check out the blog post for more details.

Customers

In the customer story for this edition, we look at Wayfair implementation of an analytics platform based on Google Data Services. Wayfair had to meet the demands that looked like this:

• 3,000 engineers with tens of millions of customers.
• They serve 20 Million items using more than 16,000 supplier partners.
• Process a billion ‘analytic’ database queries a year, from both humans and systems, against multi-petabyte datasets.

​The result after utilizing Google Cloud Data Services: “We have seen a greater than 90% reduction in the number of analytic queries in production that take more than one minute to run.” Check out the blog post for more details.

Containers and Kubernetes

Running a Kubernetes workload is tough and GKE over the years has helped to make this easier. One of the challenges is to run these workloads in a cost-optimized fashion. GKE provided Cost Optimization Insights within the Cloud Console (preview mode) to help discover optimization opportunities across your clusters. The feature is now in GA and you can take a look at GKE cost optimization video series to understand this step by step. Check out the blog post for more details.

In related GKE news, Anthos Service Mesh is now available on GKE Pilot and you are advised to look at upcoming kubectl auth changes.

Data Analytics

The BigQuery team has demonstrated its love for user-friendly SQL and BigQuery users, by announcing support for a string of SQL features and what better than to announce it on Valentines Day? The features include expanded Datatypes, expanded SQL Expressions Scripting Control statements, Table copy DDL and expanded INFORMATION_SCHEMA views. Check out the blog post for more details.

Support for Apache Spark on Google Cloud got a boost, with General Availability of Serverless Spark and availability on Spark inside of BigQuery, to allow BigQuery users to use serverless Spark for their data analytics, along with BigQuery SQL. The later feature is available as a Preview and you can request the same via this form.

AI and ML

Speech to Text API has got a new interface that sees it getting integrated right within Google Cloud Console. This makes it easy for anyone to get started, understand the integrations better, see the results there and perform quick model iterations. Check out the new interface in the Google Cloud Console.

While building ML models using Vertex AI, chances are that you have your data in BigQuery. It is essential that you understand the various ways in which Vertex AI and BigQuery can work together. If you are using Vertex AI, you can import the data for your model from BigQuery, potentially tap into accessing various public datasets available in BigQuery and use your BigQuery data in Vertex AI notebooks. The other way around, you can export your batch results from Vertex AI into BigQuery for a detailed analysis and even understand your models test predictions by exporting it to BigQuery.

Check out this blog post for more details.

Do you need to supercharge your debugging process while doing Vertex AI model training? Vertex AI Training has an improved Local Mode, you can iterate and test your work locally on a small sample data set without waiting for the full Cloud VM lifecycle. Check out the local mode documentation guide and the blog post for more details.

Finally, if you enjoy muffins but are looking to create a new muffin recipe, how about this blog post, which covers how to use machine learning to invent your own muffin recipe.

Serverless App Development

Gen 2 of Google Cloud Functions has been launched with significant updates. It has been built on Cloud Run and Eventarc. Key updates include:

• Longer request processing: Run your functions for up to 60 mins for HTTP functions, ideally suited for processing large streams of data from Cloud Storage or BigQuery.
• Larger instances: Leverage up to 16GB of RAM and 4 vCPUs, ideally suited for larger in-memory, compute-intensive and more parallel workloads.
• Concurrency: Leverage up to 1000 concurrent requests with a single function, minimizing cold starts and improving latency and cost when scaling.
• Minimum instances: Provide for pre-warmed instances to cut your cold starts.
• Traffic splitting: Support for multiple revisions of your functions, splitting traffic between different revisions and rolling your function back to a prior version.
• Native support for Eventarc, which brings over 90+ event sources from direct sources and Cloud Audit logs.
• New Developer Experience

Check out the blog post for more details. How about a quick start to go with it?

In other Serverless news, Cloud Scheduler has seen key updates that include:

• Availability across 23 GCP Regions
• Multiple regions can be used from a single project now.
• You can schedule jobs across cloud regions to ensure cross-regional availability and fail-over scenarios
• You no longer need an App Engine application in order to use Cloud Scheduler.

We wind up this section with developer posts that demonstrate how to implement key patterns using Cloud Workflows.

The first blog post describes in detail how you can implement the Saga Pattern using Cloud Workflows. This pattern is critical when there are multiple services involved in an overall transaction and if one of them fails, how do you play out compensating actions that roll back the changes correctly.

The next one is very interesting. How about using Compute Engine VMs to run long running tasks/applications but choreograph the whole thing via Cloud Workflows. Check out this blog post.

Let’s learn about GCP

It’s never too late to get started on your Google Cloud learning journey. Throughout February, you can sign up for a role-based learning path. Check out the blog post or pick a learning path today.

If you are looking to run a next-gen digital commerce platform, what are your options if you want to do that on Google Cloud? You have 3 options: headless commerce, build your own or host out of the box solutions on Google Cloud. Check out this blog post that provides details, walkthrough on each of these options. A suggested start is to view the video first.

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.