Google Cloud Architecture Best Practices

Use Deployment Manager's preview feature to validate the changes before applying them. This will help you identify any potential issues early on and avoid unexpected errors.

Configure firewall rules to restrict communication between tiers based on the specified ports. Create firewall rules for each tier allowing inbound traffic from the specific tier that needs to communicate with it. For example, the firewall rule for tier #2 should allow inbound traffic from tier #1 on port 8080.

Use BigQuery to query billing export data and create reports. Use the billing.export dataset to access billing data. Utilize the billing_export_table table for service cost details. You can use standard query syntax to group data by service type and generate cost estimates for the desired time period.

Enable the Cloud Pub/Sub API in your project. Create a service account with the necessary permissions. This will grant your App Engine application access to the Cloud Pub/Sub API.

Utilize App Engine's Split Traffic feature to randomly direct a percentage (1% in this case) of your users to the new test version. This ensures that the new version is tested on a small subset of users without major disruption to the majority.

Use Cloud Storage to store log audit files. Configure retention policies in Cloud Storage to automatically delete files older than 3 years. This solution provides a cost-effective approach for long-term storage and automatic cleanup.

Create a Cloud Spanner database role for your support team with read-only permissions. This allows them to monitor the environment without granting access to sensitive data. This ensures that they can perform essential tasks without compromising data security.

Utilize a Cloud Load Balancing service like HTTP(S) Load Balancing to distribute traffic across multiple instances. Configure it to act as a caching HTTP reverse proxy, ensuring low latency and high availability for your website.

Granting permissions to a support team to monitor Cloud Spanner environment without access to table data. This approach follows Google-recommended practices.

An HTTP reverse proxy with 30GB in-memory cache and minimal CPU usage, best implemented for cost efficiency.

Scaling a single binary application on Google Cloud Platform based on CPU usage, ensuring operational efficiency and speed.

Grant the auditor the "Cloud Audit Logs Viewer" and "Data Access Viewer" roles to access the corresponding logs.

Use Cloud Logging to combine logs from all projects into a single location. Enable Log Router to collect logs from all projects and route them to a shared sink. This allows for easier analysis and visualization.

Disable all services in the GCP project by navigating to the project and using the 'IAM & Admin' section. Then, deactivate all services within the project individually.

Create a service account in the web-applications project and grant it the "BigQuery Data Viewer" role in the crm-databases-proj project. Allow the service account to access the BigQuery datasets. This ensures minimal access and adheres to Google-recommended practices.

Check the Cloud Audit Logs to investigate the employee's actions after their termination. The Audit Logs can provide information on who accessed sensitive data after their termination.

Create a custom IAM role with suitable production permissions. Document the role's purpose, permissions, and intended use. This provides clarity, transparency, and control over custom roles.

Use Cloud Storage to upload your unstructured data in various formats. This allows for storage and organization of data before being processed.

Configure the Google Cloud SDK to include multiple projects. Manage the gcloud configurations to switch between projects easily.

A network security rule that controls incoming traffic to instances. An ingress firewall rule can specify the source of the traffic, the target instances, and the allowed protocols.

A network security rule that controls outgoing traffic from instances. An egress firewall rule can specify the destination of the traffic, the source instances, and the allowed protocols.

A service account used to represent the identity of a group of instances within a specific tier of a multi-tier application.

A rule that allows communication on TCP port 8080 between instances in Tier #1 and Tier #2.

A rule that allows communication on TCP port 8080 between instances in Tier #2 and Tier #3.

The process of checking and validating the configuration of a deployment template against a different GCP project.

A deployment manager option allowing to preview the changes without actually deploying the resources.

Observing the state of different resources during deployment, especially when resources are interdependent.

Point-in-time recovery allows you to restore your data to a specific point in time, even if the data has been modified or deleted. This is crucial for protecting your data against accidental deletions or modification and for disaster recovery.

To support point-in-time recovery, you should implement a mechanism to regularly create backups or snapshots of your data. These backups can be stored in a separate location or on different storage media to ensure data redundancy and protection against data loss.

To provide an application in your data center with access to Cloud Storage without public IPs, use a private service access connection. This enables secure communication between your on-premises environment and Google Cloud services through Private Google Access.

Use Cloud Identity and Access Management (IAM) to control access to your BigQuery data. You can create custom roles for your data science team, granting them only the necessary permissions to perform queries. Avoid giving them broad access to prevent potential misuse.

Deploy a new Compute Engine instance in the europe-west1 region, then use a VPN connection to establish a private network link between the two instances in different regions. This will allow the new instance to communicate securely with the existing instance in the us-central1 region.

For processing time-series data, you can use Cloud Pub/Sub to ingest data in real-time, Cloud Storage to store large volumes of time-series data, Cloud Dataflow for batch processing of data, and BigQuery for storing processed data and performing analysis. This combination provides a robust and scalable solution for time-series data processing.

To minimize cost, you can configure the Cloud Logging agent to filter logs based on severity levels. Only send critical or warning logs to your BigQuery dataset, and filter out less important information to reduce storage costs. This will ensure that you retain the most important logs while keeping costs under control.

To deploy an application on Cloud Run that processes messages from a Cloud Pub/Sub topic, use a Cloud Run service triggered by a Cloud Pub/Sub topic. This will automatically execute your application whenever a new message arrives on the topic. This approach ensures that your application reacts in real-time to new data and processes it efficiently.

A rolling update starts updating instances one by one, limiting the number of instances being updated at a time. maxSurge sets the maximum number of extra instances that can be created during the update, while maxUnavailable sets the maximum number of instances that can be stopped for the update.

Update the existing Managed Instance Group with a new instance template. This means updating the configuration of existing instances to use the new instance template. Usually done when you want to upgrade your application code or change resources available to running instances.

Create a new Managed Instance Group with an updated instance template, which often means creating instances with the new template. This will allow you to add new functionality to your application or resource, increasing performance or reliability.

Ensure all instances in the new Managed Instance Group are healthy and working as expected before deleting the old one, minimizing down time and ensuring a seamless transition.

You need to grant the users the spanner.databaseUser role, which allows them to view and edit data in the Cloud Spanner instance. You can do this either by directly adding them to this role or by creating a group and adding them to that group, and then adding the group to the role.

This step involves creating a new billing account separately from your Google Cloud project and then linking this new billing account to your existing Google Cloud project. This allows you to manage billing for your project under a separate billing account.

The Activity log is a detailed record of events in your Google Cloud project, including data access activities. You can filter it by specific users, buckets, and actions, providing you with a clear picture of who accessed what, when, and how.