Kubernetes-native cloud with world-class data services
Day 1
Compute, Networking & Identity
- GCP resource hierarchy: organization, folders, projects
- IAM: roles, service accounts, workload identity federation
- Compute Engine: machine types, custom images, instance templates, managed instance groups
- GKE: Autopilot vs Standard mode, node pools, workload identity
- Cloud Run: serverless containers, traffic splitting, scaling to zero
- VPC: subnets, firewall rules, shared VPC, VPC Service Controls
- Cloud Load Balancing: global HTTP(S), TCP, SSL
- Cloud DNS, Cloud CDN, Cloud Armor
- Cloud Storage: buckets, object lifecycle, signed URLs
Day 2
Data, Databases & Operations
- Cloud SQL: PostgreSQL and MySQL managed service, HA, read replicas
- Cloud Spanner: globally distributed relational database
- Firestore: document database for mobile and web backends
- BigQuery: serverless data warehouse — datasets, tables, partitioning, clustering, BI Engine
- Pub/Sub: asynchronous messaging for event-driven architectures
- Dataflow: Apache Beam-based streaming and batch processing
- Cloud Monitoring: metrics, dashboards, alerting policies
- Cloud Logging: log sinks, log-based metrics, structured logging
- Cloud Trace and Cloud Profiler
- Cost management: committed use discounts, billing budgets
- Terraform on GCP: provider setup, GCS backend
What your team walks away with
GCP practitioners who understand the platform's strengths — especially Kubernetes and data analytics — and can design, deploy, and operate production workloads on GCP.
- Navigate GCP IAM with service accounts and workload identity federation
- Deploy workloads on GKE with Autopilot or Standard cluster configurations
- Query and optimize BigQuery datasets for analytics workloads
- Set up Cloud Monitoring with alerting and structured logging
Book the GCP training
Available as a 2-day course or combined with GKE and BigQuery deep-dives for data or platform engineering teams.
Get in touch