This blog focuses on Google’s support and contributions to open source, Kubernetes, and how open source can help organisations avoid vendor lock-in and make multicloud a little less complex.
Google has been a leader in sustainability for a long time. They’re striving for net-zero carbon emissions, and creating technologies to help others reduce their carbon footprint too…
One of the great things about Google Cloud is their approach to security. Google has designed Google Cloud with protection of customers data at the forefront…
In this article, we will discuss a very beautiful feature from GKE which is Workload Identity.
This article is part of a multi-part series covering below best practices around securing GKE workloads…
Function as a Service, or FaaS, has been a cornerstone in app development. Popularized by AWS Lambda service, all the major Cloud Providers offer their version, with different features. And they also extend this principle to containers, with Cloud Run on Google Cloud for example.
In this article, we will see how we can schedule GCE Instance (VM) restart using Instance Schedule managed featured in GCP. We will also see all limitations with this feature.
Serverless is a new paradigm that changes the development habits. The “no-server-management” mode is great, but it comes with counterparts. In FaaS (Functions as a Service), only a function is exposed, only one entry-point for a single purpose workload.
Cloud Run comes in addition of other Google Serverless products, especially Cloud Function. Even if each product has a typical use case and recommendations, some use cases can be implemented in several ways with several products. So, What I use in my current developments? And Why do I make this choice?
Serverless is a game changer in the cloud and in application architecture. BigQuery, had different processing performances according to the region; and mainly correlated to the region age. Cloud Run and Cloud Functions being also serverless product,Are there performance differences among regions also with Cloud Run and Cloud Functions?
The new regions are deployed with up-to-date hardware, the older regions have older hardware.
So, are the performance equals in all regions?
Distributed computing is the key to process big data at scale. All data processing systems use clusters of VMs: Hadoop, Dataflow, and, of course, BigQuery.
During the code packaging, you might need to rely on other data than only your code, for configuration, enrichment, customization, versioning,… That new source of data can be in a database but also on a Google Workspace document.
BigQuery features offer DML statements: Data Manipulation languages, to insert, update or delete data. And because your golden source is precious, you want to be sure that DML does not to break the current data value
You have to backup your data in BigQuery
In its perfect form, managed service becomes serverless service, a pay-as-you-use solution. You haven’t to worry about the infrastructure and the cost: if you don’t use it, you don’t pay for it.
BigQuery is the Google Cloud data warehouse flagship. It’s serverless, you pay-as-you-use, there are tons of features.
One of them, not very known, is time travel: you can access your data state at any point of time over the past 7 days.
