Google Cloud Digital Leader Certification - For Beginners

Discover why Google Cloud is a top provider, with a vast global data center network, undersea cables, and regional coverage shaping product availability and renewable energy commitment.

Explore how regions and zones in Google Cloud provide geographic distribution, fault isolation, and low-latency access, enabling multi-region and multi-zone deployments for high availability, disaster recovery, and data safety.

Explore how regions and zones enable high availability through redundancy and failover. Understand monitoring-driven failover and uptime percentages to minimize downtime in critical services.

Learn how disaster recovery plans use backups across Google Cloud regions and zones to protect data, recover applications, and maintain business continuity after outages.

Explore cloud computing delivery models: public cloud, private cloud, and hybrid cloud, including multi-tenant sharing, dedicated resources, and cost considerations. Get a high-level introduction and prep for certification questions.

Public cloud provides a pool of virtual resources from Google Cloud, AWS, and Azure, with virtualization and consumption-based pricing, and supports multi cloud and redundancy to guard against outages.

Explore private cloud fundamentals, including dedicated hardware, organization-owned facilities, virtualization, and strict security and compliance. Compare it to public cloud, and note the cost implications and the hybrid cloud overview.

Hybrid cloud blends public and private resources, enabling apps to run across on‑premises and public clouds. Benefit includes flexibility, security, and lower latency via a private managed network and multi cloud.

Understand the shared responsibility model in cloud computing and how responsibilities shift across infrastructure as a service, platform as a service, software as a service, and function as a service.

Learn infrastructure as a service (IaaS), a pay-as-you-go model where you rent compute, storage, and network resources, manage your own OS and software, and move from capital to operational expenditure.

Learn how platform as a service provides a ready runtime and databases, reducing setup work. Compare it with infrastructure as a service and view a Google Cloud demo deploying MySQL.

Discover software as a service, where organizations plug in ready-made tools like JIRA and Zoom, while cloud providers handle security and maintenance and users configure data and access.

Explore function as a service, a serverless, event-driven model that runs code on demand, such as sending resume emails or classifying uploads, and its distinction from software as a service.

Compare the shared responsibility model across on-premises, IaaS, PaaS, SaaS, and FaaS, detailing hardware, storage, encryption, OS, and security responsibilities.

Explore infrastructure and application modernization on Google Cloud, covering virtual machines, containers, and serverless computing, and cloud-native apps with Kubernetes Engine, App Engine, APIs, and APIG.

Explore the evolution of cloud computing architectures from bare metal to virtualization, containers, microservices, and serverless, and learn how innovation, agility, speed, and customer focus drive modernization.

Explore Google Cloud's seven solution pillars for modernization, including infrastructure modernization, business applications platform, and application modernization. See how these pillars enable cloud native development, analytics, and security.

Explore the concept of virtual machines and virtualization, learn how hypervisors split physical hardware into isolated VMs, and identify Google Cloud's Compute Engine among cloud products.

Learn how Google Compute Engine creates virtual machines in Google Cloud with rapid boot, persistent disks, per second pricing, and lift-and-shift suitability for monolithic apps.

Create and configure a Google Cloud Compute Engine virtual machine. Learn naming, labeling, region and zone selection, machine type, boot disk, firewall, and browser-based SSH access.

Master VMware engine in Google Cloud for lift-and-shift of your VMware SDDC into a dedicated, physically isolated data center, leveraging existing licenses and high-speed, low-latency access to Google Cloud products.

Explore Google Cloud bare metal solutions for Oracle workloads, offering regional extensions, low-latency private networking, and the ability to bring licenses and modernize with Google Cloud products.

Understand containers and virtual machines, and learn how containerization with Docker and Kubernetes enables microservices on Google Cloud, improving deployment speed and portability.

Explore Google Kubernetes Engine for container-based microservices, comparing standard and autopilot (serverless) modes and learning how Google Cloud manages Kubernetes clusters of virtual machines for scalable deployment.

Create and manage a Kubernetes cluster with Google Kubernetes Engine, selecting standard, configuring cluster-1, and deploying containers, while understanding nodes, virtual machines, and cost considerations.

Anthos unifies management of multiple Kubernetes clusters across Google Cloud, on premises, and other clouds, enabling consistent infrastructure, policy enforcement, service mesh, and CI/CD anywhere.

The lecture introduces serverless computing as a cloud native architecture that runs functions on demand, with providers managing servers behind the scenes and pricing based on the number of requests.

Discover how App Engine enables serverless, fully managed deployment on Google Cloud, scaling web applications from zero to planet scale, with support for Java, Node.js, Python, Ruby, and Go.

Explore app engine, a serverless, managed platform on Google Cloud, to deploy web apps with language options and manage versions, security scans, cron jobs, and custom domains.

Cloud Run merges serverless and container deployment, letting you run any language app from a Docker image with zero server management and a quick 10–15 minute setup.

This lecture demonstrates a quick cloud run deployment of a container, showing how to create a service, container image, region, cpu allocation, and auto-scaling with logs and metrics.

Explore cloud functions as a serverless option for running small code snippets triggered by http requests, pub/sub, or cloud storage events, with scalable to zero capacity and event-based pricing.

Demonstrates creating and deploying a Google Cloud function, an event-driven serverless compute, with a storage event trigger, second-generation option, and enabling Cloud Build API and Cloud Functions API.

Learn sustained use discounts in Google Cloud, how automatic savings apply to compute engines and virtual machines, and that serverless resources and certain VM types are ineligible.

Learn how committed use discounts work in Google Cloud, including one- or three-year commitments, hardware or software usage, and discount sharing across multiple projects.

Explore spot and preemptible disks in Google Cloud, understand 30-second shutdown warnings and large discounts for fault-tolerant batch workloads.

Learn how to modernize applications alongside infrastructure by adopting cloud native approaches—microservices, containerization, Kubernetes, and serverless—while implementing CI/CD and gradual rollout.

Explore five Google Cloud recommended patterns for modernization: move applications first (lift and shift), change applications before moving to cloud native, invent greenfield or brownfield, and move applications without changes.

Explore legacy apps and their limitations, including not cloud-friendly designs, mainframe dependencies, and the inability to deliver real-time data or meet modern user expectations.

Learn how APIs connect legacy and cloud-based applications to securely exchange data, enable AI/ML training with historical data, and build a digital API ecosystem.

Explore Apigee, Google's API management platform, and its handling of the API lifecycle. Learn how it secures, analyzes, and monetizes APIs across legacy and cloud, with routing, keys, and quotas.

Explore how data drives transformation with Google Cloud data stack, covering storage and processing of structured and unstructured data, plus Looker, BigQuery, Spanner, Cloud SQL, Cloud Storage, and AI/ML solutions.

Explore why data matters, how on-premises limitations hinder storage and processing, and how Google Cloud Platform delivers economies of scale, rapid elasticity, and global data access for automated data management.

Explore the varieties of data: internal data, external customer data, and industry data, and examine how traditional on-premises infrastructure struggles with on-demand scaling, real-time processing, and handling unstructured data.

Explore the three data types organizations manage daily: structured, semi-structured, and unstructured. See how structured data is quantitative and searchable, while unstructured data is qualitative.

Explore how cloud infrastructure enables handling data of all types—structured, semi-structured, and unstructured—using APIs and AI/ML to extract value, classify sentiment, and scale storage to unlimited capacity in Google Cloud.

Define what a database is and why structured data matters. Learn how Google Cloud SQL products like Cloud SQL and Cloud Spanner offer scalable, transactional data solutions with integrity.

Discover Google Cloud SQL, a fully managed relational database supporting MySQL, PostgreSQL, and SQL Server with automatic backups, scaling, high availability, and easy migrations.

learn to create a Cloud SQL MySQL database in Google Cloud, select development settings, single zone deployment, enable backups and maintenance, and monitor with query insights and dashboards.

Explore Cloud Spanner, a fully managed, globally scalable database with multi-region replication, automatic synchronization, and high availability, supporting Google standard SQL and PostgreSQL dialects.

Explore NoSQL databases on Google Cloud, focusing on Cloud Bigtable and Firestore, and compare NoSQL formats: key-value, column family, graph, and document to SQL.

Explore Google Cloud Bigtable, a fully managed wide-column NoSQL database that scales to petabytes with low single-digit millisecond latency and real-time analytics, integrated with the Google Cloud ecosystem.

Discover Firestore, a serverless, fully managed NoSQL document database that scales from zero to global with real-time data synchronization and offline support.

Compare databases, data warehouses, and data lakes to store current and historical data for analytics, then leverage ETL, real-time processing, and tools like BigQuery for dashboards.

BigQuery is a serverless, multi regional SQL data warehouse that scales from terabytes to petabytes in minutes and enables real-time analytics with Pub/Sub, Dataflow, and Data Stream.

Explore cloud storage as a data lake for unstructured data, highlighting scalable, multi regional deployment, versioning, backups, security with encryption, and storage classes.

Explore creating buckets in cloud storage with globally unique bucket names, and select regional, dual region, multi region, or region locations for optimized access and availability.

Explore Google Cloud storage classes—standard for frequent access, nearline, coldline, and archive for infrequent data. Learn cost order, auto class, access control, and retention options.

Protects data in cloud storage with encryption at rest and in transit, and offers customer-managed or customer-supplied keys via KMS, with rotation and logging options.

Looker sits on top of your database or data warehouse as a Google Cloud business intelligence layer, enabling dashboards and analytics while integrating with BigQuery, Cloud Storage, and data sources.

Define AI and ML, and explain why data quality matters for prediction accuracy. Learn how Google Cloud differentiates AI from ML and applies AI/ML solutions to create value.

Explore how artificial intelligence and machine learning enable self-learning, handle unstructured data, and automate processes. See cloud-based demonstrations that show predictions and personalized experiences.

Prepare clean data, create labeled datasets, train models with TensorFlow, deploy for online or batch predictions, and monitor and retrain to maintain accuracy.

Explore Google Cloud's AI ecosystem, from Vertex AI to pre-trained models and AutoML, powered by TensorFlow and TPUs, with AI Hub.

Dive into Vertex AI, a unified Google Cloud platform to build, train, deploy, and manage ML models using custom or AutoML, with MLOps and a centralized model repository.

Explore Google Cloud AutoML products—tables, image, video, text, and translation—and learn to train custom models with your data or rely on pre-trained APIs when needed.

Explore Google Cloud's pre-trained AI APIs—speech to text, text to speech, translation, natural language, video, media translation, vision, document AI, and contact center AI—for instant results without training.

See Vision AI analyze an uploaded image, identifying objects and faces while revealing emotions and labels with confidence, and highlight data cleanliness, completeness, and coverage for insurance use cases.

Learn the essentials of Google Cloud security and operations, including cost management, cloud security, cybersecurity challenges, DevOps and sre, and monitoring production applications.

Learn how to govern cloud costs through people, processes, and technology, monitoring spend in real time, optimizing with native tools and governance practices.

Learn to implement financial governance with GCP tools, achieving visibility, budgets and alerts, access control, labeling, and using the recommender to optimize cloud spend.

Show how to use the Google Cloud Pricing Calculator to estimate compute engine and cloud storage costs, and how to add labels for resource organization.

Understand why total cost of ownership becomes complex when migrating to cloud, comparing on premises, single cloud, multi cloud, and hybrid setups, including Kubernetes autopilot versus standard.

Explore cloud jargons like privacy, security, compliance, and availability, and learn how data protection policies, controls, and regional considerations safeguard cloud data.

Explore common security risks organizations face, including cyber attacks, phishing, malware, ransomware, physical damage, and third-party library vulnerabilities, and learn how Google Cloud helps mitigate these threats.

Discover how Google Cloud keeps your data secure and private, with no selling or advertising, default encryption, insider protections, and external audits confirming privacy.

Google Cloud's six-layer security approach, from hardware (Titan chip) to operations, emphasizes zero-trust identity, encryption in transit and at rest, and proactive threat defense.

Explore identity and access management in Google Cloud, defining who can do what on which resource through principals, roles, and policies to grant granular, secure access.

Explore the three types of Google Cloud IAM roles (primitive, predefined, and custom) and learn to grant least-privilege access for end users, applications, and groups using the Google Cloud console.

Learn how to structure Google Cloud resources with a resource hierarchy, create groups by department or role, and assign roles at domain, folder, project, or resource levels to scale access.