What you'll get

  • Govt. Verified Certification
  • Job Credibility
  • Certification Valid for Life
  • Lifetime Access To E-learning

Description

Course content

Total: 129 lectures
  • The Case for Cloud Computing
  • Genesis
  • Why Google Cloud Platform (GCP)?
  • Autoscaling and autohealing
  • Capital expenditure (CAPEX) versus operating expenses (OPEX)
  • Career implications
  • Introduction to Google Cloud Platform
  • Global, regional, and zonal resources
  • Accessing the Google Cloud Platform
  • Projects and billing
  • Setting up a GCP account
  • Using the Cloud Shell
  • Compute Choices – VMs and the Google Compute Engine
  • Google Compute Engine – GCE
  • Persistent disks and local SSDs – block storage for GCE
  • More on working with GCE VMs
  • Modifying GCE VMs
  • GKE, App Engine, and Cloud Functions
  • GKE
  • Creating a Kubernetes cluster and deploying a WordPress container
  • Using the features of GKE
  • Google App Engine – flexible
  • Google App Engine – standard
  • Google Cloud Storage – Fishing in a Bucket
  • Knowing when (and when not) to use GCS
  • Serving Static Content with GCS Buckets
  • Storage classes–Regional, multi-regional, nearline, and coldline
  • Working with GCS buckets
  • Creating buckets
  • Transferring data in and out of buckets
  • Use case – Object Versioning
  • Use case – object life cycle policies
  • Use case – restricting access with both ACLs and IAM
  • Use case – signed and timed URLs
  • Use case – reacting to object changes
  • Use case – using customer supplied encryption keys
  • Use case – auto-syncing folders
  • Use case – mounting GCS using gcsfuse
  • Use case – offline ingestion options
  • Relational Databases
  • Relational databases, SQL, and schemas
  • GCP Cloud SQL
  • Use case – managing replicas
  • Use case – managing certificates
  • Use case – operating Cloud SQL through VM instances
  • Automatic backup and restore
  • Cloud Spanner
  • NoSQL Databases
  • NoSQL databases
  • Cloud Bigtable
  • Creating and operating an HBase table using Cloud Bigtable
  • Scaling GCP Cloud BigTable
  • The Google Cloud Datastore
  • Comparison with traditional databases
  • Working with Datastore
  • Full indexing and perfect index
  • BigQuery
  • Underlying data representation of BigQuery
  • BigQuery public datasets
  • Legacy versus standard SQL
  • Working with the BigQuery console
  • Loading data into a table using BigQuery
  • Deleting datasets
  • Working with BigQuery using CLI
  • BigQuery pricing
  • Analyzing financial time series with BigQuery
  • Identity and Access Management
  • Resource hierarchy of GCP
  • Permissions and roles
  • Managing Hadoop with Dataproc
  • Hadoop and Spark
  • Hadoop on the cloud
  • Google Cloud Dataproc
  • Compute options for Dataproc
  • Working with Dataproc
  • Load Balancing
  • Why load balancers matter now
  • Taxonomy of GCP load balancers
  • HTTP(S) load balancing
  • Configuring HTTP(S) load balancing
  • Configuring Internal Load Balancing
  • Other load balancing
  • Networking in GCP
  • Why GCP's networking model is unique
  • VPC networks and subnets
  • The default VPC
  • Internal and external IP addresses
  • VPN and cloud router
  • Working with VPCs
  • Working with custom subnets
  • Working with firewall rules
  • Logging and Monitoring
  • Logging
  • Monitoring
  • Infrastructure Automation
  • Managed Instance Groups
  • Cloud deployment manager
  • Security on the GCP
  • Security features at Google and on the GCP
  • Google-provided tools and options for security
  • Some security best practices
  • BeyondCorp – Identity-Aware Proxy
  • Pricing Considerations
  • Compute Engine
  • BigTable
  • BigQuery
  • Datastore
  • Cloud SQL
  • Google Kubernetes Engine
  • Pub/Sub
  • Cloud ML Engine
  • Stackdriver
  • Video Intelligence API
  • Key Management Service – KMS
  • Vision API
  • Effective Use of the GCP
  • Eat the Kubernetes frog
  • Careful that you don't get nickel-and-dimed
  • Pay for what you allocate not what you use
  • Make friends with the gsuite admins
  • Try to find reasons to use network peering
  • Understand how sustained use discounts work
  • Read the fine print on GCS pricing
  • Use BigQuery unless you have a specific reason not to
  • Use pre-emptible instances in your Dataproc clusters
  • Keep your Dataproc clusters stateless
  • Understand the unified architecture for batch and stream
  • Understand the main choices for ML applications
  • Understand the differences between snapshots and images

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