Explore Google Cloud Platform overview part 2 focusing on cloud identity and access management, who can access which resources, and how the hierarchical organization and quotas govern access and limits.

Discover Google Cloud database and storage services, from Cloud SQL and Cloud Spanner to Bigtable, Firestore, and Cloud Storage, with AlloyDB, Memorystore, and File Store options.

Explore Google Cloud's big data and machine learning services, including BigQuery, Dataflow, Dataproc, Pub/Sub, Data Fusion, Looker, Data Prep, and Vertex AI for end-to-end ML workflows.

Install the Cloud SDK on your laptop to access gcloud, Cloud Shell, and Cloud Storage, and learn to set up a default environment for Google Cloud projects.

Explore the Google Cloud compute engine navigation, reviewing vm instances, instance templates, sole-tenant nodes, images, disks and snapshots, instance groups, and marketplace options, plus settings.

Create a virtual machine instance from scratch or a template, configure machine type, boot disk, networking, labels, and security settings, and apply startup scripts and encryption keys.

Learn how to manage a Google Compute Engine virtual machine, configure networking, disks, firewall, startup scripts, and permissions, and monitor and modify it through the console.

Explore compute engine components and subsystems, from UI and console to Linux and Windows connections, disks, images, container VM, and learn IAM keys, labels, networks, and autoscaling with load balancer.

Explore Google Compute Engine machine types, from standard general purpose to memory and high-CPU options, including GPU configurations and custom builds with per-cpu memory and region considerations.

Explore Compute Engine disks, snapshots, and images to manage local ssd, persistent disks (zonal and regional), file storage, and machine images for compute instances.

Create an instance template to define machine type, image, zone, startup script, disks, metadata, and tags, then use it to launch managed or unmanaged instance groups.

View and manage quotas and limits in the cloud console to control usage and bills. Monitor consumption, enforce restrictions, and request quota changes across GCP and other clouds.

Spot instances, the new version of preemptible VMs, cut costs by up to 60–80% based on region and zone, with a 30-second preemption notice to save work to persistent storage.

Explore sole tenancy in Google Cloud, reserving a sole tenancy node group with a node template, auto scaling, GPUs and local SSDs, plus affinity label and maintenance settings.

Explore how Compute Engine charges for instances, disks, and egress, and leverage sustained, committed, and preemptible discounts to optimize costs across geography, custom versus predefined machines, and resource usage.

Enable shielded VM and confidential computing in Google Cloud to protect data in use with TPM, secure boot, integrity monitoring, and hardware-based protection on AMD EPYC processors.

Explore how compute engine load balancers secure and distribute client traffic across backend services, including global, regional, and internal options, with proxy and pass-through modes, and Cloud Armor protection.

configure a content aware load balancer to route front-end requests to specific back-end services based on content type, such as video content, bhp pages, or static images via defined paths.

Learn how Google Cloud load balancers enforce session affinity using client IP or generated cookies, keeping a user's requests on the same backend service or VM instance.

Learn how auto scaling works for virtual machines by configuring on/off in instance groups and setting minimum and maximum utilization targets to achieve elasticity with load balancers supporting high availability.

Create regional instance templates and groups, configure a HTTP load balancer with health checks and backends, then simulate cross-regional traffic to demonstrate routing to nearest instances.

Understand pods and containers, including resource requests and limits that govern scheduling. Learn deployment and replica set concepts, init containers for prechecks, and practical pod lifecycle steps in Kubernetes.

Explore how Kubernetes labels and selectors use key-value pairs to identify and select objects, including equality-based and set-based selectors.

Explore how Kubernetes namespaces isolate objects within a cluster, apply resource quotas, and manage objects with YAML definitions, API version, kind, and metadata across imperative and declarative workflows.

Explore how Kubernetes manages the application lifecycle with pod priority and preemption, horizontal pod autoscaler, and pod disruption budgets, plus rolling updates, recreate, blue-green, and canary deployment strategies.

Explore how Kubernetes services expose backend pods to clients using cluster IP, node port, load balancer, and headless configurations, with selectors, target ports, and DNS routing.

Learn how the Kubernetes kube scheduler filters and scores nodes to place pods based on resources, taints, affinities, and topology spread, and how to run multiple schedulers.

Learn App Engine architecture with apps, services, versions, and instances; use traffic splitting, scaling, and deployment across versions, with dashboard insights on pricing, errors, and security scans.

Learn how to use App Engine traffic splitting to deploy a new version and gradually migrate traffic, using A/B deployment and routing options like IP address, cookie-based, or random.

Explore App Engine's flexible environment, which lets you run docker containers and bring your programming language, with configurable vm types, cpu, ram, and ssd, plus weekly security patches.

Compare App Engine pricing across standard and flexible environments and how instance class affects costs. Learn charges for cloud datastore calls, search api, network traffic, blob storage, and logs api.

Launch fully managed Cloud Run services in a region or cluster namespace, replicate them across zones, and expose an endpoint, while you create services mapped to your domain name.

Explore Cloud Run revisions: learn why revisions are immutable, how to deploy a new revision of the same container, and how traffic shifts to the new revision.

Explore cloud run fully managed, scale to zero from hundreds of containers, no provisioning, up to 1000 containers, with quota options and per-request pricing.

Explore cloud run for Anthos, deploying containers as a service on Kubernetes whether on premises or in the cloud, with cluster-based capacity, memory constraints, and scale-to-zero behavior.

Explore cloud storage by creating and configuring buckets in the console, choosing regional, multi-region, or dual-region locations, and setting standard storage class with fine-grained access control, versioning, and encryption options.

Configure cloud storage lifecycle policies to automatically move objects from standard to nearline after six months, then to coldline after a year, and finally delete after five years.

Explore signed URLs and signed policy documents to grant time-limited access to cloud storage objects, enforce granular IAM conditions, and prevent public exposure using PAP and uniform bucket policies.

Explore cloud sql for mysql, a fully managed Google Cloud Platform service. Create and manage mysql instances with encryption, SSL, zone replication, backups, point-in-time recovery, cloning, and integrated logging.

Discover Google Cloud SQL for PostgreSQL, a fully managed, encrypted, multi-zone, backupable database service on Google Cloud Platform, with SSL, replication, import/export, and instance cloning.

Explore configuring automatic backups and on demand backups in Cloud SQL, creating backups, restoring from them, and adjusting backup windows, while noting point in time and logging.

Learn how Cloud SQL logging and point-in-time recovery protect data by enabling binary logging, preserving every transaction, and restoring the database to a crash point.

Enable high availability in Cloud SQL by configuring a synchronous standby replica and failover, with a master switching to standby and automatic failover across zones.

Explore how Cloud SQL pricing varies by machine type and generation, plus in-region data egress fees, and learn how to optimize costs by deleting unused instances.

Explore alloydb, a PostgreSQL-like database from Google Cloud, designed for horizontal scaling and high-speed oltp and olap workloads. Offers four times faster transactions and 100 times faster analytics.

Explore Cloud Spanner features like global scalability, multi-regional availability with ultra-high uptime, strong consistency, automatic replication, import/export, time-stamped commits, and data splitting.

Explore cloud spanner interleaved tables, nesting albums and songs inside a table and supporting seven levels. Learn to create indexes on album table and on songs within albums.

Learn identity and access management in Cloud Spanner by mapping instance, database, and data level permissions to create, read, write, and get metadata, including sessions and data operations.

Cloud Firestore pricing by comparing two pricing mechanisms for documents and stored data, including per 100,000 documents and 18 cents per month per GB, noting identical structures across database types.

Explore Cloud Firestore IAM, learn about database permissions and rules, including insert, update, delete, and roles like data store owner, app, readers, and writers for secure access.

Demonstrates Cloud Firestore in Datastore through hands-on entity creation, property indexing, and querying, then shows backup and restore workflows with export and import options.

Explore Cloud Dataproc architectural aspects, migrating on-premise jobs to Dataproc, running Spark or big data workloads, and routing outputs to Cloud Storage or BigQuery.

Explore storage options in Cloud Dataproc, including cloud storage backups, data shuffling, persistent disks, and local SSDs, and assess node failure impacts on data.

Submit and run Dataproc jobs, including Spark and Presto, on a cluster, specify the main jar and class, and understand the Dataproc versus Dataflow distinction.

Explore cloud dataproc workflows to model complex jobs with dags and dependencies, use templates to launch and delete clusters, run on existing clusters, parameterize execution, and enforce security.

Explore Cloud Dataproc pricing and its mapping to compute engine pricing, with usage-based margins. Practice using gcloud to manage clusters, operations, workflow templates, and jobs for certification prep.

Learn how to clean up cloud dataproc by deleting the cluster to save costs on Google Cloud Platform, freeing the computing resources used by the cluster.

Explore design considerations for cloud dataflow pipelines, including input sources, formats, transformations, pcollection patterns, and output destinations such as BigQuery or cloud storage.

Compare cloud dataflow (Apache Beam) and cloud dataproc (Hadoop and Spark) by matching batch or streaming needs, ecosystems, and notebooks to choose the best pipeline engine.

Explore cloud dataflow quotas and limits, including 1000 workers per pipeline, 160 shuffle slots, and 15,000 monitoring requests per minute, with guidance for streaming and greenfield workloads.

Explore Google Cloud Dataflow pricing by examining per-second billing and how actual usage of memory, storage, and data processed drives charges for batch and streaming jobs.

Explore BigQuery IAM: manage dataset permissions, create and delete datasets and jobs, and apply primitive roles like dataset reader, writer, and owner to secure access.

Explore BigQuery slot-based pricing, including flat rate options and dedicated slots for cost control. Learn to estimate usage, run queries, and manage datasets with the BQ command line.

Google Cloud Data Lab offers an interactive tool for data exploration and machine learning, linking to BigQuery and Data Studio for visualization.

Explore Google Cloud VPC basics, a global, software-defined private network that spans regions and zones, supporting subnets, firewall rules, routes, flow logs, and shared VPC for secure resource isolation.

Explore types of VPCs, including default auto mode VPC with predefined subnets, firewall rules, and internet gateway configurations. Learn how custom VPCs let you define IP ranges, subnets, and routing.

Explore how a Google Cloud project acts as a container for resources with a default VPC and up to five networks. Learn to plan for internal traffic and egress charges.

Learn how Google Cloud VPC subnets define internal IP addresses for resources, with subnet zoning across zones and regions, firewall rules, and traffic routing via load balancer.

Explore how internal IP addresses work in Google Cloud VPCs, including subnets, default versus custom networks, automatic and static internal IPs, DNS resolution, and VM lifecycle effects.

Assign an external ip address to a vm to enable access from outside the network; note charges when inactive. Manage static ip addresses and publish public dns records.

Explore how Google Cloud VPC routes traffic between subnets and to the internet, including default routes, destination IPs, and next hops, guided by instance tags.

Explore configuring a shared VPC with a host project and guest projects, assign network and security admins, and manage rules and permissions for centralized control.

Enable vpc and firewall flow logs to monitor network traffic, support real-time security analysis, forensics, and expense optimization across subnets and vm instances.

Understand that VPC pricing charges egress traffic, not for network creation, and that premium tier uses google fiber optic routing for lower latency, while standard tier differs.

Discover how to secure internal servers with a bastion host, enabling maintenance access without exposing external IPs by using a jump host in the same subnet.

Demonstrates configuring a bastion host by removing external IPs and using an internal IP to reach the VM. Outlines interconnect, VPN, and cloud DNS topics for hybrid cloud access.

Install and configure the Google Cloud SDK command line interface, initialize gcloud, sign in, set a default project and zone, and verify by listing compute instances.

Plan and configure a cloud solution on Google Cloud Platform by exploring compute, storage, and network options, and use the pricing calculator to estimate costs.

Explore the pricing calculator to estimate Google Cloud Platform costs for Compute Engine instances, premium tier options, currencies, and sustained use discounts.

Explore deploying and implementing cloud solutions on Google Cloud Platform, covering Compute Engine, App Engine, Cloud Functions, Container Engine, data store, networking, Deployment Manager, monitoring, and pub/sub triggers.

Deploy and implement data solutions on Google Cloud Platform by initializing databases and storage, loading data, and managing services like Cloud Spanner, Bigtable, Datastore, Pub/Sub, Dataflow, and Dataproc.

Establish a direct pipe from your data center to Google Cloud Platform with cloud interconnect, offering dedicated or partner interconnect and access to Google services and vpn on premises.

Connect your data center to Google Cloud Platform using cloud interconnect, cloud vpn, and peering for hybrid connectivity, secure traffic, and low latency.

Explore how cloud router enables dynamic routing with BGP to exchange routing information between your Google Cloud VPC and on-prem networks, replacing static routes and enabling automatic route updates.

Learn how to ensure successful operation of cloud solutions on Google Cloud by configuring monitoring, logging, alerts, backups, autoscaling, and proper separation of duties for managed and self-managed services.

Manage Kubernetes engine resources by creating clusters, configuring node pools and auto scaling, deploying apps with deployments and services, using container registry, load balancers, and monitoring and logging.

Manage App Engine resources by configuring scaling and traffic splitting, monitoring with dashboards and logs, and setting up metrics, alerts, security scans, firewall rules, and custom domains.

Learn to deploy and test cloud functions with storage event triggers, publish messages to topics, monitor logs, and handle errors while processing files and notifications.

Learn to manage data solutions in Cloud SQL by backing up, exporting to cloud storage, importing data, and connecting from external tools while handling failover and replica configurations with gcloud.

Store, analyze, monitor, and alert on multi-cloud log data with Stackdriver logging, enabling real-time insights, 30-day retention, and export of log-based metrics to cloud storage or BigQuery.

Enable Stackdriver Debug in production with a debug agent, add log points, and view live variables without redeploying for real-time diagnosis.

Explore monitoring and logging in Google Cloud, including alerts, custom metrics, log sinks to cloud storage, filtering logs, and diagnostic tools like trace and debug.

Learn to configure access and security in Google Cloud Platform by applying minimum access permissions, separation of duties, encryption at rest and in transit, and monitoring with audit logs.

Learn to view, enable, and analyze audit logs for projects and managed services, including admin activity, data access, and system events, with queries and exports to BigQuery or Cloud Storage.