Google Cloud Digital Leader Certification BootCamp

Discover how cloud computing delivers on-demand services over the internet, enabling scalable storage and hardware resources in everyday life, while managing costs via GCP, AWS, and Azure.

Trace the evolution from dedicated and virtual private servers to cloud hosting, noting improved scalability, reliability, flexibility, and pay-as-you-go economics across cloud services.

Discover the size of the Google Cloud Platform global network, its security by design, open cloud model, and analytics-driven AI, plus core networking with VPC, subnets, and IAM.

Cloud computing offers rapid scalability and cost-effective workloads, with pricing based on usage, scalable storage, secure data access from anywhere, and robust data backup and recovery.

Explain the three cloud service models: software as a service, platform as a service, and infrastructure as a service, with third parties providing the stack or infrastructure.

Compare the three cloud deployment models: public cloud, private on-premises cloud, and hybrid cloud, highlighting pay-as-you-go scalability, control and security tradeoffs, cost and latency considerations, and backup use cases.

Compare the shared responsibility model across compute options from infrastructure as a service to platform and software as a service, detailing who manages hardware, hypervisors, containers, and security.

Explore cloud total cost of ownership (TCO) and compare capex versus opex, covering compute, data transfer, storage, security and management tools, plus machine learning and AI integrations for scalable usage.

Explains key cloud architectural concepts such as availability, scalability, elasticity, fault tolerance, and disaster recovery, and shows how load balancers, multi-zone deployments, and failover strategies safeguard services.

Explore google cloud resources organized hierarchically, linking the life cycle to the immediate parent and enabling policy, access control, and inheritance across folders, projects, and resources.

Understand environment oriented hierarchy, where an organization uses a folder per environment—production, quality assurance, and development—containing projects and multiple app instances across three layers, including challenges of shared services.

Explore how a flexible function oriented hierarchy groups work by expertise within GCP, using organization folders for each function, environments, and projects with shared services like JIRA and the website.

Explore granular access oriented hierarchies where each business unit has a folder in an organization, with division, function, and environment levels. This adaptable structure enables deployments but increases management complexity.

Discover Google's global infrastructure and its worldwide hardware and data centers. See how 35 regions, 106 zones, 173 edge locations, and over 200 countries enable the GCP suite.

Understand Google Cloud regions and zones, their geographic locations, and how proximity lowers latency. Intra-region communication is faster and more cost-effective than cross-region communication.

Explore the network edge, the gateway to the internet, and how edge pops, CDN pops, and cloud media edges deliver low latency, bandwidth savings, and access to Gcp resources.

Explore zones as deployment areas within a region and treat them as a single failure domain. Deploy fault-tolerant, high-availability apps across multiple zones to protect against failures.

Apply resource scoping to map resources across regions and zones, covering zonal, regional, multi-regional, and internal services like Cloud Spanner, plus global resources abstracted from regions and zones.

Explore data residency and compliance boundaries in Google Cloud, defining where resources reside and policies that limit locations, with assured workloads for managing data residency, access control, and encryption.

Explore how digital transformation uses cloud technologies to replace manual processes and go paperless, pillars: infrastructure modernization, business application platform, portfolio application modernization, database storage, smart analytics, artificial intelligence, security.

Discover cloud solution pillars from infrastructure modernization and hybrid deployments to scalable APIs, automated pipelines, data security, and AI services like Vertex AI and Looker.

Open the Google Cloud console in an incognito tab, sign in, select a project from the top left, view all products in the side menu, and activate Cloud Shell.

Explore the Google Cloud CLI to manage resources, authenticate, configure, and automate tasks with gsutil, bq, and Cloud Shell.

Learn how to install and configure the Google Cloud SDK, use the CLI and cloud client libraries in Python, and run a BigQuery example from VS Code.

Explore Google's compute engine to host VMs on GCP with predefined or custom machine types across general purpose, compute optimized, and memory optimized families, including GPU acceleration for ML.

App Engine provides a fully managed serverless platform that auto-scales from zero to millions of requests, handles deployment, load balancing, storage, and database integration across supported languages.

Understand how containers package application code with libraries and dependencies to run anywhere, and how Kubernetes orchestrates containerized workloads for automated deployment, scaling, and failover.

Explore Google Kubernetes Engine to deploy, manage, and scale containerized apps with a fully managed cluster, control plane, nodes, pods, and kubectl.

Explore relational and non-relational databases, compare SQL-driven data management, integrity, and ACID transactions, and learn Google Cloud options like Cloud Spanner, Cloud SQL, and NoSQL services.

Explore how a data warehouse centralizes data from transactional systems for analysis, enabling BI, dashboards, and informed decisions through scalable, consolidated analytics with Google Cloud's BigQuery.

Explore key value stores in Google Cloud, storing data as unique keys with associated values, including blobs. Compare Bigtable and Memory Store for high throughput, low latency, and caching needs.

Understand document databases that store documents in collections, with flexible, semi-structured data. Compare Cloud Firestore and Cloud DataStore, focusing on document models, subcollections, and scalable NoSQL design.

GCP serverless services enable development and deployment of scalable applications with automatic scaling and fault tolerance, while GCP handles infrastructure from minimally managed VMs to Firebase.

Master cloud storage fundamentals, including objects, buckets, projects, and permissions, with encryption options, pay-as-you-use pricing, storage classes (multi-regional, regional, nearline, coldline), and access via console, gsutil, and APIs.

Explore networking in GCP by examining VPCs, VPNs, DNS, IP addressing, and firewalls, and see how subnets, regions, zones, and compute engines connect in the big picture.

Explore data pipelines—from ingestion to visualization—and learn how Google Cloud offers scalable services like BigQuery, Cloud SQL, Spanner, Bigtable, Memorystore, and DataStore for storage, processing, and analytics.

Dataproc is a managed Google Cloud service that runs Hadoop and Spark jobs, handling VMs and storage and offering auto-scaling and per-second billing.

Learn to design and run dataflow pipelines for real-time and batch processing using the Apache Beam SDK, data sources like BigQuery and Pub/Sub, and autoscaled cloud computing.

Store and manage container images and language packages with artifacts registry and container registry, and use Cloud SDK and Cloud Code to deploy Kubernetes apps and automate Cloud Build pipelines.

Explore hybrid and multi-cloud strategies with Google Cloud, using Anthos and GKE to run Kubernetes across on premises and public clouds, and unify policy, serverless with Cloud Run.

Learn how Google Cloud IoT Core securely connects and manages devices at scale, ingests data, and enables apps that integrate with Google Cloud data services; edge devices serve entry points.

Discover how Google Cloud Deployment Manager uses infrastructure as code with yaml, jinja, and python templates to deploy, update, and tear down resources via configuration and template files.

Explore a model for operation monitoring services for Google Cloud, enabling you to monitor, log, trace, and profile Google Cloud applications and services.

Explore Firebase, Google's fully managed, serverless platform with unified client libraries for developing and deploying mobile and web apps, including authentication, deep links, custom domains, analytics, and remote configuration.

Learn how data migration moves data across locations, formats, or applications, on premises or private hosting, and how migrating to Google Cloud saves time and resources in flexible hybrid environments.

Explore migration types: lift and shift, improve and move, and remove and replace, highlighting when to move workloads as is, refactor for cloud native capabilities, or redesign for Google Cloud.

Assess your environment, plan cloud infrastructure, deploy workloads to Google Cloud, and optimize using cloud-native capabilities to improve performance, scalability, disaster recovery, machine learning and artificial intelligence integrations.

Explore data and application migration on Google Cloud, moving data, databases, and IT resources across platforms and formats with easy, optimized cloud migration and modernization services.

Migrate for Compute Engine moves virtual machines from source to target with minimal modifications. It continuously replicates data to the cloud, enabling easy cloning and testing in the cloud console.

Explore Anthos, a modern platform for hybrid architectures spanning Google Cloud and on-premises data centers with VMware. Manage Kubernetes compute via a single control plane and a managed service mesh.

Use storage transfer service to import data into cloud storage, schedule one-time or recurring transfers, delete objects when needed, and synchronize cloud storage, local, and POSIX file systems with filters.

Transfer appliance enables secure data transfer by shipping storage to a Google upload facility for cloud storage; ideal for data in US/EU/UK/Singapore or when network transfer would take a week.

Explore Vertex AI on Google's unified AI platform to build, deploy, and scale ML models with AutoML and pre-trained or custom tooling, integrating PyTorch and TensorFlow.

Explore the TensorFlow ecosystem for deep learning, including TensorFlow Lite, TensorFlow.js, and TensorFlow extended, and learn how Keras interfaces with TensorFlow for CPU, GPU acceleration, and CUDA cores.

Explore how AutoML makes machine learning accessible for businesses, enabling you to build and deploy custom models using neural architecture search and transfer learning, with AutoML vision for labeled data.

Explore machine learning compute and notebooks, using GPUs or CPUs, VM images and containers for deep learning, and interactive Jupyter Lab-based tools like Vertex AI and Colab.

Explore GCP's fully managed machine learning software as a service (saas) offerings, including Vision API, Video API, Natural Language API, and Recommendations API for personalized experiences and retail ai.