GCP

Customize Compute Engine specifications according to workload needs

Compute Engine also provides custom machine types and predefined machine types, allowing you to create the needed architecture based on your workload, and adjust the specifications at any time. It supports quick clicking of customized rules from the command list tools on the main console and gcloud easily completes machine settings.
Make choices based on customer needs, the prices are transparent and the predictable usage fees can be used to estimate future expenses effectively; the pricing method of custom machine types is based on the usage per hour of each vCPU and each GB of memory. Usage prices can be estimated accurately to achieve the goal of saving costs as needed.

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  Get started easily on the creation of VM

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  Adjust the specifications of the existing VM at any time

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  Find the most suitable price based on your needs

Applied scenario

Compute Engine supports many machine specifications; customers can select virtual machines with different enhanced functions according to their usage scenarios. If existing specifications cannot satisfy your needs, you can also customize machine specifications according to your needs.

Quickly build clusters to accelerate application development

GKE is the first fully hosted Kubernetes service in the industry; it introduced a complete Kubernetes API, four-way automatic resource scheduling function, released version and multi-cluster support. Schedule pod resources horizontally automatically and adjust cluster resources for each note automatically according to CPU usage or customized indicators, and analyze CPU and memory usage continuously through the virtual automatic scheduling of pod resource function, to adjust the CPU and memory requirements automatically.

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  Four-way automatic resource adjustment Configuration function

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  Choose different deployment versions according to your need

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  Takes care of both the native network and security of containers

Applied scenario

Use GKE and Cloud Source Repositories, Cloud Build and Spinnaker for Google Cloud and other related services to connect the services then build, test and deploy applications automatically. Once the program code of the application is changed, the updated content will trigger continuous pushing of software updates, and further rebuilds, retests, and re-deploys the new version automatically, accelerating the development progress of applications.

Equipped with an expandable data analysis system to process massive amounts of data easily

BigQuery is a fully hosted data analysis warehousing system of GCP. There is no need to further set or manage the architecture, no need to learn other additional analysis languages, just focus on using standard SQL to search for specific data for analysis. Analyzing large amounts of data in near real-time can assist business growth effectively.
Use BigQuery ML to introduce machine learning technology and prediction models, learn and simulate related data analysis behaviors through AI integration function, estimate future results and discover potential business opportunities, and bring the possibility to expand the business effectively.

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  Equipped with flexible data expandability

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  Highly flexible distributed analysis engine

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  Built-in machine learning and model prediction

Applied scenario

Data from different sources can be pooled into BigQuery, and use simple and smooth methods to perform data analysis. In addition to uploading data files, BigQuery also supports data importing through plug-ins such as data transfer services (DTS) and Data Fusion. Use the extremely high flexibility of BigQuery to import data into the data warehousing system with the method you prefer for analysis.