Engineering
Spanner On A Modern Columnar Storage Engine
Google was born in the cloud. At Google, we have been running massive infrastructure that powers critical internal and external facing services for more than two decades. Our investment in this infrastructure is constant, ranging from user visible features to invisible internals that makes the infrastructure more efficient, reliable and secure. Constant updates and improvements …
Leading Towards More Trustworthy Compliance Through EU Codes Of Conduct
Google is committed to be the best possible place for sustainable digital transformation for European organizations. Our Cloud on Europe’s terms initiative works to meet regional requirements for security, privacy, and digital sovereignty, without compromising on functionality or innovation. In support of this initiative, we are making our annual declaration of adherence to two important …
Integrating ML Models Into Production Pipelines With Dataflow
Google Cloud’s Dataflow recently announced the General Availability support for Apache Beam’s generic machine learning prediction and inference transform, RunInference. In this blog, we will take a deeper dive on the transform, including: Showing the RunInference transform used with a simple model as an example, in both batch and streaming mode. Using the transform with …
Introducing Pay-As-You-Go Pricing For Apigee API Management
Apigee is Google Cloud’s API management platform that enables organizations to build, operate, manage and monetize their APIs. Customers from industries around the world trust Apigee to build and scale their API programs. While some organizations operate with mature API-first strategies, others might still be working on a modernization strategy. Even within an organization, different …
Building A Fleet Of GKE Clusters With ArgoCD
Organizations on a journey to containerize applications and run them on Kubernetes often reach a point where running a single cluster doesn’t meet their needs. One example, you want to bring your app closer to the users in a new regional market. Add a cluster to the new region and get the added benefit of …
VMware Advances Multi-Cloud Management With VMware Aria
VMware Aria is powered by VMware Aria Graph, a new graph-based data store technology that reduces multi-cloud complexity across any cloud, any app, and for every persona SAN FRANCISCO–(BUSINESS WIRE)– VMware Explore US 2022 – More organizations are delivering applications across public clouds and at the edge, and they’re increasingly using Kubernetes to manage and scale their modernized and …
Introducing Vertical Autoscaling In Streaming Dataflow Prime Jobs
Dataflow has provided a number of capabilities to improve utilization and efficiency by automatically provisioning and scaling resources for your job. The following are some of the examples: Horizontal Autoscaling that automatically scales the number of workers. Streaming Engine, which decouples storage from the workers. This also gives workers access to unbounded storage and more …
Introducing On-Demand Backup, Schema Extension Support For Google Cloud’s Managed Microsoft AD
Managed Service for Microsoft Active Directory (Managed Microsoft AD) is a Google Cloud service that offers highly available, hardened Microsoft Active Directory running on Windows virtual machines. We recently added on-demand backup and schema extension capabilities that can help Google Cloud users more easily and effectively manage AD tasks. Managed Microsoft AD is a fully …
Writing Your Own Scheduler With Kube-Scheduler-Simulator
As the default Kubernetes scheduler is highly configurable, in many cases we don’t have to write any code to customize the scheduling behavior. However, people who want to learn how the scheduler works and interact with other components may try to develop their own scheduler. In this article, I describe how to build a scheduler …
Spatial Clustering On BigQuery – Best Practices
Most data analysts are familiar with the concept of organizing data into clusters so that it can be queried faster and at a lower cost. The user behavior dictates how the dataset should be clustered: for example, when a user seeks to analyze or visualize geospatial data (a.k.a location data), it is most efficient to …