The summary of essential information about Google Cloud in September 2022.
When developing services with Compute Engine, our customizable compute service that lets you create and run virtual machines on Google’s infrastructure, you’ll likely find yourself frequently switching between your code editor, terminal, and the Google Cloud Console.
Cloud Code is a set of IDE plugins for popular IDEs like VS Code and IntelliJ that make it easier to develop applications that use Google Cloud services. Read More.
An essential aspect of operating any application is the ability to observe the health and performance of that application and of the underlying infrastructure to quickly resolve issues as they arise.
Google Kubernetes Engine (GKE) already provides audit logs, operational logs, and metrics along with out-of-the-box dashboards and automatic error reporting to facilitate running reliable applications at scale. Read More.
Developing production applications or training large models requires additional tooling to help you scale beyond just code in a notebook, and using a cloud service provider can help. But that process can feel a bit daunting.
You’re working on a new machine learning problem, and the first environment you use is a notebook. Read More.
We’re all accustomed to Google magic in our daily lives. When we need information like the time a store closes, the best route to a destination, help cooking a favorite recipe — we just ask Google. Google is known for providing useful information everywhere, wherever you are.
Data’s journey begins in operational databases, where the data is born as users and systems interact with applications. Read More.
Customers want to leverage Google Cloud Storage for its simplicity, scalability, and security. But migrating 100s of TB of data from a self-managed object storage is complicated.
To accelerate and simplify this migration, Storage Transfer Service recently announced Preview support for moving data from S3-compatible storage to Cloud Storage. Read More.
As the data volumes have grown - one of the key challenges organizations are facing is how to maintain the data quality in a scalable and consistent way across the organization. Read More.
First things first, exporting your billing data to BigQuery is exactly what it sounds like: it exports data from your billing account (which handles the costs for all your Google Cloud resources) into BigQuery.
BigQuery is a great choice for analyzing data, where you can run queries against your data. Having this data in BigQuery also makes it much easier to integrate with other tools, like Looker or Data Studio for visualization. Read More.
Increasingly more enterprises adopt Machine Learning (ML) capabilities to enhance their services, products, and operations. As their ML capabilities mature, they build centralized ML Platforms to serve many teams and users across their organization.
Machine learning is inherently an experimental process requiring repeated iterations. An ML Platform standardizes the model development and deployment workflow to offer greater consistency for the repeated process. Read More.
Logging is a critical part of the software development lifecycle allowing developers to debug their apps, DevOps/SRE teams to troubleshoot issues, and security admins to analyze access.
Today, we’re pleased to announce Log Analytics, a new set of features in Cloud Logging available in Preview, powered by BigQuery that allows you to gain even more insights and value from your logs. Read More.
Google Cloud Logging launches Log Analytics powered by Big Query. The feature allows Log users to use the power of BQ within Cloud Logging to perform Analytics on Logs.
You can update your existing Log Buckets to start using Log Analytics. It does not require complex data pipeline configurations to ingest data. Read more.
Source: https://cloud.google.com/blog/products/gcp