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GCP Data Engineering On Google Cloud Platform

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GCP Data Engineering On Google Cloud Platform

Cloud Computing

Duration
45 Hours

Course Description


          The Google Cloud Platform (GCP) Data Engineering course provides hands-on training in designing, building, and managing data processing systems on Google Cloud. It covers topics such as data storage, data pipelines, data analysis, and machine learning on GCP, with a focus on practical application using tools like BigQuery, Dataflow, and Dataproc. 

Course Outline For GCP Data Engineering on Google Cloud Platform

Interacting with Google Cloud

  • Overview of Google Cloud
  • Using Google Cloud
  • Lab: Working with the Google Cloud Console and Cloud Shell

Virtual Networks

  • Virtual Private Cloud
  • Projects, networks, and subnetworks
  • Demo: Expand a Subnet
  • IP addresses

Demo: Internal and external IP

  • Mapping IP addresses
  • IP addresses for default domains
  • Routes and firewall rules
  • Pricing

Lab: VPC Networking

  • Common network designs

Lab: Implement Private Google Access and Cloud NAT

Virtual Machines

  • Compute Engine

Demo: Create a VM

  • VM access and lifecycle

Lab Intro: Creating Virtual Machines

  • Compute options
  • Compute pricing
  • Special compute configurations
  • Images
  • Disk options
  • Common Compute Engine actions

Lab: Working with Virtual Machines

Identity and Access Management (IAM)

  • Identity and Access Management
  • Organization
  • Roles

Demo: Custom roles

  • Members
  • Service Accounts
  • Organization Restrictions
  • IAM best practices

Lab: Exploring IAM

Storage and Database Services

  • Cloud Storage
  • Cloud Storage Features
  • Choosing a storage class
  • Filestore

Lab: Cloud Storage

  • Cloud SQL

Lab: Cloud SQL

  • Cloud Spanner
  • AlloyDB
  • Firestore
  • Cloud Bigtable
  • Memorystore

Resource Management

  • Resource Manager
  • Quotas
  • Labels
  • Billing

Demo: Billing Administration

Lab: Examining Billing Data with BigQuery

Resource Monitoring

  • Stackdriver Overview
  • Monitoring

Lab: Resource Monitoring

  • Logging
  • Error Reporting
  • Tracing
  • Profiling

Interconnecting Networks

  • Cloud VPN
  • HA VPN

Lab: Configuring Google Cloud HA VPN

  • Cloud Interconnect and Peering
  • Cloud Interconnect
  • Peering
  • Choosing a connection
  • Shared VPC and VPC Peering

Load Balancing and Autoscaling

  • Managed instance groups
  • Autoscaling and health checks
  • Overview of HTTP(S) load balancing

Example: HTTP load balancer

  • HTTP(S) load balancing

Lab: Configuring an HTTP Load Balancer with Autoscaling

  • Cloud CDN
  • SSL proxy load balancing
  • TCP proxy load balancing
  • Network load balancing
  • Internal load balancing

Lab: Configuring an Internal Load Balancer

  • Choosing a load balancer

Infrastructure Automation

  • Terraform

Lab Intro: Automating the Infrastructure of Networks Using Terraform

  • Google Cloud Marketplace

Lab: Launch Infrastructure Solutions on Google Cloud Marketplace

Cloud Dataflow

  • Why customers value Dataflow.
  • Dataflow Pipelines.
  • Dataflow Templates.
  • Dataflow SQL.

Lab: A Simple Dataflow Pipeline (Python)

Lab: MapReduce in Dataflow (Python)

Dataproc

  • The Hadoop ecosystem.
  • Running Hadoop on Cloud Dataproc.
  • GCS instead of HDFS.
  • Optimizing Dataproc.

Lab: Running Apache Spark jobs on Cloud Dataproc.

Bigquery

  • Intro to BigQuery.
  • Data Lakes and Data Warehouses.
  • Transactional Databases vs Data Warehouses.
  • Partner effectively with other data teams.
  • Manage data access and governance.
  • Build production-ready pipelines.
  • Review GCP customer case study.

Demo: Federated Queries with BigQuery.

  • Website Demo: Finding PII in your dataset with DLP API.

Lab: Analyzing Data with BigQuery.

  • The modern data warehouse.
  • Intro to BigQuery.
  • Getting Started.
  • Loading Data.
  • Exploring Schemas.
  • Nested and Repeated Fields.
  • Optimizing with Partitioning and Clustering.
  • Schema Design.
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