Google Cloud Cost Optimization Technique - GCP

Configure budgets and alerts in Google Cloud Platform to set monthly limits, thresholds, and alert channels like email, link monitoring, and pop-ups to avoid bill surprises.

Learn how to configure budget alerts and email notifications in Google Cloud Platform, monitor spending, and manage multiple notification channels to trigger manual or later programmatic actions.

Learn to configure Google Cloud budgets and alerting with Pub/Sub topics, set threshold-based notifications, and automate responses using a Cloud Function.

Explore configuring budget alerts in Google Cloud, test email notifications, and trigger programmatic actions from cloud functions to cap spending and manage billing.

Explore Google Cloud quotas and rate limits in the cloud console, including per-service, per-region, and per-minute caps, and learn how to view, adjust, or request increases.

Explore preemptive versus spot instances on Google Cloud Compute Engine, highlighting cost discounts and lifecycle differences, with termination notices before spot preemption.

Learn two methods to optimize Google Cloud billing: committed use discount and sustained use discount, including hardware and software licensing commitments in Compute Engine and automatic savings.

Export billing data to BigQuery for detailed cost analysis across projects. Configure billing export settings to include standard and detail cost, pricing data, and a ten-day dataset expiration.

Enforce organization policies to restrict developers and gain control over your Google Cloud account by disabling public IPs for Cloud SQL and using internal IPs.

Learn to schedule compute engine virtual machines with cloud scheduler and cloud functions to start and stop instances at defined times, saving costs.

Schedule and automate your virtual machine with cloud scheduler to start and stop Compute Engine, testing via http triggers and cloud functions for cost optimization.

Discover how to use the instance schedule in Google Cloud to automatically start and stop a VM with cron expressions for daily, weekly, or monthly schedules, saving costs.

Export all detailed billing data to Google Cloud BigQuery, review a billing export dataset and its tables to identify costly resources, then disable the export to stop ongoing charges.

Explore how Google Cloud Storage lifecycle policy automatically moves objects between storage classes, such as standard, nearline, coldline, and archive, to optimize costs and manage data retention.

Identify unused disks and IP addresses not attached to a compute engine, detach or discard them to cut costs, using cloud console and cloud scheduler with cloud functions to monitor.

Explore custom machine type selection in Google compute engine to tailor virtual CPUs and memory to exact workloads, avoiding underutilization from fixed machine types.

Learn how enabling object versioning in Google Cloud Storage prevents data duplication, how to manage versions and costs, and how to recover earlier versions using Cloud Shell commands.

Explore how Google Cloud's free trial and free tier help optimize costs, detailing $300 credit for three months and free quotas for Cloud Run, Firestore, Cloud Build, and Cloud Shell.

Explore how region selection impacts cost optimization in Google Cloud, using pricing calculator examples with compute engine and Spanner, and balance price against latency as a design decision.

Reduce BigQuery costs by avoiding select star and querying only the needed fields. The lecture shows a public dataset example to illustrate how filtering fields lowers data processed.

Explore BigQuery caching to serve identical query results from the cache, reducing bytes processed and costs, by enabling cache results in the UI or via the command line.

Learn how to use Google Cloud BigQuery in sandbox mode with a free tier, no credit card required, including storage and data processing limits, and how to upgrade.

Discover how to apply table and view expiration in BigQuery, set dataset defaults, and override expiry for specific tables or views to auto-delete unused data.

Learn how partitioning and clustering in Google Cloud BigQuery reduce data scan size and query cost by organizing data by date or ingestion time, with a hands-on demo.

Compare non-partitioned and partitioned queries to show partition elimination reducing scanned data from gigabytes to kilobytes. Explore partition date usage, clustering, and cost-saving practices like deleting unused cloud resources.

Explore BigQuery pricing in Google Cloud, covering active vs long-term storage, partition rules for storage, and compute models like on-demand, flat-rate slots, and flex slots to optimize costs.

Compare delete and truncate queries to optimize costs; truncating processes zero bytes and preserves the table, while deleting scans data and incurs costs.

Limit the bytes billed per query in BigQuery to control costs by setting the maximum bytes billed in the query editor; if exceeded, the query fails and incurs no charges.

Explore on demand versus provision capacity in Google Cloud to optimize costs, with examples from Compute Engine, Kubernetes Engine, Cloud SQL, App Engine, and Cloud Run.

Explore Google Cloud egress costs as data moves between regions, noting that ingress is free. See how cloud storage, disks, and snapshots influence cross-region transfers and per GB charges.

Learn to reduce cloud costs through continuous resource monitoring, thresholds, and automated actions with cloud monitoring. Create dashboards for VM, Disk, and Spanner metrics and configure alerts.

Google Cloud Shell offers a free Linux-based development environment with default tools and an editor. Interact with cloud services via gcloud and store 5 GB of ephemeral data.

Analyze how general, regional, and multi-region services in Google Cloud impact cost, availability, and disaster recovery, with examples from Compute Engine, BigQuery, Cloud Storage, and VPC.

Compare HDD and SSD storage options in Google Cloud, balancing latency, throughput, and cost for SQL instances, Bigtable configurations, and Compute Engine disks, with regional versus single-zone choices.

Explore how the GCP compute engine recommendations help identify underutilized resources, and encourage actions like resizing or deleting idle virtual machines to optimize costs.

Identify underutilized compute engine instances via the Google Cloud recommendations hub, resize to e2 custom with 2 vCPU and 5.5 GB RAM, or delete to save $106 per month.

Configure scheduled deletion for data proc clusters on Google Cloud to prevent idle costs, using after elapsed time or after cluster idle time.

Celebrate completing the Google Cloud cost optimization course. Provide honest feedback, share your certificate on social networks, and contribute new cost-saving techniques for future updates.