Google Data Talks: March & April Updates

Google is always on the move, especially in the data field. As always, we kept an eye on all recent announcements. In this blog we will give an overview on the March & April updates of 2022.

Spanner offers fundamental discounts for guaranteed purchases

Cloud Spanner is Google’s completely managed relational database which provides for near unlimited scale, strong consistency and high availability up to 99,99%. Since recently, Google offers Committed Use Discounts (CUDs) for Spanner. In exchange for agreed usage of Spanner’s calculating capacity (measured in nodes or processing units) during a period of one or three years, one can receive a 20 to 40 percent discount, respectively. This offer is valid for all Spanner-related configurations, and in all Google regions.

 Continue reading: Maximise your Cloud Spanner savings with new committed use discounts

New release Memorystore increases performance

Cloud Memorystore for Redis offers a fully managed in-memory environment, compatible with open-source Redis. Memorystore for Redis makes it easy to implement and conduct scalable, permanently available and safe Redis services on Google Cloud. In the newest version, launched in March, these features were added:

  • Read Replicas are now generally available and enable ten times better performance. For other improvements in the newest version, see blog below;
  • RDB Snapshots are now available in preview;
  • An update for base layer agencies, without completely emptying the cache, decreases application downtime;

Continue reading: What’s new with Cloud Memorystore for Redis

Connection between CCAI Insights and Data Fusion available

Also hot in March, was the in-preview launch of a Contact Centre AI (CCAI) Insights  Accelerator. As a user of the CCAI platform, you get a ready-to-use approach at your disposal for the import and export of big quantities of data from and to Contact Centre AI Insights.

The importance of such an option is that Cloud Data Fusion empowers an up to 80% lower TCO for the maintenance of data pipelines. The launch enables users of the CCAI platform to import large amounts of historical data for analysis in Contact Centre AI Insights.

Continue reading: Bulk import and exports with Cloud Data Fusion for Contact Centre AI Insights made easy

How Google made a contribution to the AIOps solution of Zenoss

In the blog below, Google describes a cutting-edge machine learning solution for production environments, with the purpose to monitor events in IT and industrial operations, and to explain their symptoms. The solution is mainly meant for complicated surroundings such as extensive industrial applications, for the safekeeping of events in devices that are linked with the Industrial Internet of Things (IoT), and for predictive monitoring of every part of IT governance, like virtual infrastructure, applications, networks and microservices.

The solution has been implemented by Google itself on the Google Cloud Platform, and is the result of innovative research by Google’s corporate engineering. A combination of machine learning and operative tools of Google Cloud were devoted to it. Originally developed to guard tens of thousands of climate control devices in hundreds of Google buildings, Google established a pipeline for general purposes to make anomaly detection extremely scalable. They made the machine learning algorithm open source for this. Their entire approach is also described in this document. Further down in the blog, Google talks about the way Zenoss, supplier of Intelligent Application and Service Monitoring tools, used the solution in the development of an AIOps platform for infrastructural environments. They deployed Vertex AI, which unifies the Google Cloud services for building machine learning in one, undivided user interface and API. The result is a distributed, deep-learning solution within the Zenoss platform that supports the client to understand, prioritise and solve mistakes within an IT infrastructure. 

Continue reading: Event Monitoring with explanations on the Google Cloud

Contact Centre AI becomes an exhaustive platform

As you know, Google has been building specific engines for partners and companies to use for the improvement of certain processes in their environment for a while now. Most of the time, those engines make use of artificial intelligence and/or machine learning.

A good example is Contact Centre AI, where the purpose of the engine is to boost the amount of automatically handled conversations, which gives call centre agents more time to process complex files.

In a next step, Google is now launching Google Cloud Contact Centre AI Platform: an extension of the Contact Centre AI, with a ready-to-use, end-to-end solution for contact centres. By expanding the platform, Google was able to put extra focus on the maintenance of customer relationships. It makes linking up with CRM platforms possible, as well as predicting the number of calls per period, executing your Workforce Optimisation, and providing self-service solutions such as Visual Interactive Voice Response (IVR).

Moreover, Google announced an intensive collaboration with UJET: an innovative and experienced Contact Centre as a Service (CCaaS) provider. UJET offers a safe, user-oriented design, scalability and a mobile-focused solution with ready-to-use implementation, strong omnichannel possibilities and a powerful user experience, which fits the product organically in the vision Google has regarding contact centres. Check this out for more information on their joint effort. With this project, Google conceived a second (next to Salesforce), impressive partnership for its contact centre platform.

 Continue reading:  Contact Centre AI reimagines the customer experience through full end-to-end platform expansion

We’re looking forward to the Google Data Cloud Summit, we’re convinced that huge announcements will be made! Keep an eye on our website, we will be sharing all updates soon!

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