Google Cloud Platform Certification Training & Exams

Course Outlines

Title: Architecting with Google Compute Engine
Duration: 3 Days
Price: R15,000 (ex vat)

Module 1
Introduction to GCP

• List the different ways of interacting with GCP
• Use the GCP Console and Cloud Shell
• Create Cloud Storage buckets
• Use the GCP Marketplace to deploy solutions

Module 2
Virtual Networks

• List the VPC objects in GCP
• Differentiate between the different types of VPC networks
• Implement VPC networks and firewall rules
• Design a maintenance server

Module 3
Virtual Machines

• Recall the CPU and memory options for virtual machines
• Describe the disk options for virtual machines
• Explain VM pricing and discounts
• Use Compute Engine to create and customize VM instances

Module 4
Cloud IAM

• Describe the Cloud IAM resource hierarchy
• Explain the different types of IAM roles
• Recall the different types of IAM members
• Implement access control for resources using Cloud IAM

Module 5
Storage and Database Services

• Differentiate between Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore, and Cloud Bigtable
• Choose a data storage service based on your requirements Implement data storage services
• Implement data storage services

Module 6
Resource Management

• Describe the cloud resource manager hierarchy
• Recognize how quotas protect GCP customers
• Use labels to organize resources
• Explain the behavior of budget alerts in GCP
• Examine billing data with BigQuery

Module 7
Resource Monitoring

• Describe the Stackdriver services for monitoring, logging, error reporting, tracing, and debugging
• Create charts, alerts, and uptime checks for resources with Stackdriver Monitoring
• Use Stackdriver Debugger to identify and fix errors

Module 8
Interconnecting Networks

• Recall the GCP interconnect and peering services available to connect your infrastructure to GCP
• Determine which GCP interconnect or peering service to use in specific circumstances
• Create and configure VPN gateways
• Recall when to use Shared VPC and when to use VPC Network Peering
• Describe Cloud DNS

Module 9
Load Balancing and Autoscaling

• Recall the various load balancing services
• Determine which GCP load balancer to use in specific circumstances
• Describe autoscaling behavior
• Configure load balancers and autoscaling

Module 10
Infrastructure Automation

• Automate the deployment of GCP services using Deployment Manager or Terraform
• Outline the GCP Marketplace

Module 11
Managed Services

• Describe the managed services for data processing in GCP

Title: Architecting with Google Kubernetes Engine
Duration: 3 Days
Price: R17,000 (ex vat)

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Module 1

• Introduction to Google Cloud Platform
• Use the Google Cloud Platform Console
• Use Cloud Shell
• Create GCP projects
• Understand the differences among GCP compute platforms
• Cloud Resource Manager, Quotas, Billing

Module 2

• Launching Workloads in Kubernetes Engine
• Understand the architecture of Kubernetes: pods, namespaces
• Understand the components of Kubernetes
• Create Docker containers using Google Container Builder
• Store container images in Google Container Registry
• Create a Kubernetes Engine cluster
• Install software using Helm charts

Module 3

• Debugging, Monitoring, Logging, Error Reporting
• Introspect Kubernetes containers
• View pod logs
• Troubleshoot common Kubernetes problems
• Use Stackdriver Kubernetes Monitoring
• Use Prometheus monitoring with Stackdriver

Module 4

• Scheduling and Autoscaling Workloads in Kubernetes Engine
• Apply labels
• Create and manage Deployments
• Perform rolling upgrades and rollbacks of Deployments
• Define Services
• Expose Services with LoadBalancers and NodePorts
• Run cron jobs
• Control pod execution with taints and tolerations
• Configure Kubernetes Engine clusters for autoscaling

Module 5

• Kubernetes and Google Cloud VPC Networking Fundamentals
• Understand the Kubernetes networking model
• Understand how Kubernetes networking differs from Docker networking
• Understand how Kubernetes networking differs from Compute Engine networking
• Understand VPC networks and subnets
• Understand load balancer types
• Use Kubernetes DNS

Module 6

• Persistent Data and Storage
• Use Secrets to isolate security credentials
• Use ConfigMaps to isolate configuration artifacts
• Push out and roll back updates to Secrets and ConfigMaps
• Configure Persistent Storage Volumes for Kubernetes Pods
• Use StatefulSets to ensure that claims on persistent storage volumes persist across restarts

Module 7

• Access Control and Security in Kubernetes and Kubernetes Engine
• Understand Kubernetes authentication and authorization
• Define Kubernetes RBAC roles and role bindings for accessing resources in namespaces
• Define Kubernetes RBAC cluster roles and cluster role bindings for accessing cluster-scoped resources
• Define Kubernetes pod security policies to only allow pods with specific security-related attributes to run
• Define Kubernetes network policies to allow and block traffic to pods Understand the structure of GCP IAM
• Define IAM roles and policies for Kubernetes Engine cluster administration
• Decide between building one larger cluster and many smaller clusters

Module 8

• Using GCP Managed Storage Services from Kubernetes Applications
• Understand the pros and cons of using a managed storage service versus self-managed containerized storage
• Understand use cases for Cloud Storage, and use Cloud Storage from within a Kubernetes application
• Understand use cases for Cloud SQL and Cloud Spanner and use them from within a Kubernetes application
• Understand use cases for Cloud Datastore, and use Cloud Datastore from within a Kubernetes application
• Understand use cases for Bigtable, and use Bigtable from within a Kubernetes application

Google Data Engineer Training

The Google Data Engineer Professional certification is a sought-after qualification that validates a holder's skills in architecting, maintaining, and tuning big data pipelines and processes. The recommended course for the Google Data Engineer Profesional certification are:

• Big Data & ML Fundamentals
• Data Engineering on Google Cloud

It is recommended that candidates who want to obtain this certification have the Google Cloud Architect Associate certification or have the requisite skills and knowledge required for holders of the certificate. It is not a mandatory requirement.

Course Outlines

Title: Google Cloud Big Data & Machine Learning Fundamentals
Duration: 1 Day
Price:  R8,000 (ex vat)

Module 1: Introducing Google Cloud Platform

• Google Platform Fundamentals Overview.
• Google Cloud Platform Big Data Products.
• Lab: Sign up for Google Cloud Platform.

Module 2: Compute and Storage Fundamentals

• CPUs on demand (Compute Engine).
• A global file system (Cloud Storage).
• Cloud Shell.
• Lab: Set up an Ingest-Transform-Publish data processing pipeline.

Module 3: Data Analytics on the Cloud

• Stepping stones to the cloud.
• Cloud SQL: your SQL database on the cloud.
• Lab: Importing data into CloudSQL and running queries.
• Spark on Dataproc.
• Lab: Machine Learning Recommendations with Spark on Dataproc.

Module 4: Scaling Data Analysis

• Fast random access.
• Datalab.
• BigQuery.
• Lab: Build a Machine Learning Dataset.

Module 5: Machine Learning

• Machine Learning with TensorFlow.
• Lab: Carry out ML with TensorFlow.
• Pre-built models for common needs.
• Lab: Employ ML APIs.

Module 6: Data Processing Architectures

• Message-oriented architectures with Pub/Sub.
• Creating pipelines with Dataflow.
• Reference architecture for real-time and batch data processing.

Module 7: Summary

• Why GCP?.
• Where to go from here.
• Additional Resources.

Title: Data Engineering on Google Cloud
Duration: 4 Day
Price:  R28,000 (ex vat)

Module 1: Introduction to Data Engineering

• Explore the role of a data engineer.
• Analyze data engineering challenges.
• Intro to BigQuery.
• Data Lakes and Data Warehouses.
• Demo: Federated Queries with BigQuery.
• Transactional Databases vs Data Warehouses.
• Website Demo: Finding PII in your dataset with DLP API.
• Partner effectively with other data teams.
• Manage data access and governance.
• Build production-ready pipelines.
• Review GCP customer case study.
• Lab: Analyzing Data with BigQuery.

Module 2: Building a Data Lake

• Introduction to Data Lakes.
• Data Storage and ETL options on GCP.
• Building a Data Lake using Cloud Storage.
• Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.
• Securing Cloud Storage.
• Storing All Sorts of Data Types.
• Video Demo: Running federated queries on Parquet and ORC files in BigQuery.
• Cloud SQL as a relational Data Lake.
• Lab: Loading Taxi Data into Cloud SQL.

Module 3: Building a Data Warehouse

• The modern data warehouse.
• Intro to BigQuery.
• Demo: Query TB+ of data in seconds.
• Getting Started.
• Loading Data.
• Video Demo: Querying Cloud SQL from BigQuery.
• Lab: Loading Data into BigQuery.
• Exploring Schemas.
• Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
• Schema Design.
• Nested and Repeated Fields.
• Demo: Nested and repeated fields in BigQuery.
• Lab: Working with JSON and Array data in BigQuery.
• Optimizing with Partitioning and Clustering.
• Demo: Partitioned and Clustered Tables in BigQuery.
• Preview: Transforming Batch and Streaming Data.

Module 4: Introduction to Building Batch Data Pipelines,

• EL, ELT, ETL.
• Quality considerations.
• How to carry out operations in BigQuery.
• Demo: ELT to improve data quality in BigQuery.
• Shortcomings.
• ETL to solve data quality issues.

Module 5: Executing Spark on Cloud Dataproc

• The Hadoop ecosystem.
• Running Hadoop on Cloud Dataproc.
• GCS instead of HDFS.
• Optimizing Dataproc.
• Lab: Running Apache Spark jobs on Cloud Dataproc.

Module 6: Serverless Data Processing with Cloud Dataflow

• Cloud Dataflow.
• Why customers value Dataflow.
• Dataflow Pipelines.
• Lab: A Simple Dataflow Pipeline (Python/Java).
• Lab: MapReduce in Dataflow (Python/Java).
• Lab: Side Inputs (Python/Java).
• Dataflow Templates.
• Dataflow SQL.

Module 7: Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

• Building Batch Data Pipelines visually with Cloud Data Fusion.
• Components.
• UI Overview.
• Building a Pipeline.
• Exploring Data using Wrangler.
• Lab: Building and executing a pipeline graph in Cloud Data Fusion.
• Orchestrating work between GCP services with Cloud Composer.
• Apache Airflow Environment.
• DAGs and Operators.
• Workflow Scheduling.
• Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.
• Monitoring and Logging.
• Lab: An Introduction to Cloud Composer.

Module 8: Introduction to Processing Streaming Data

• Processing Streaming Data.

Module 9: Serverless Messaging with Cloud Pub/Sub

• Cloud Pub/Sub.
• Lab: Publish Streaming Data into Pub/Sub.

Module 10: Cloud Dataflow Streaming Features

• Cloud Dataflow Streaming Features.
• Lab: Streaming Data Pipelines.

Module 11: High-Throughput BigQuery and Bigtable Streaming Features

• BigQuery Streaming Features.
• Lab: Streaming Analytics and Dashboards.
• Cloud Bigtable.
• Lab: Streaming Data Pipelines into Bigtable.

Module 12: Advanced BigQuery Functionality and Performance

• Analytic Window Functions.
• Using With Clauses.
• GIS Functions.
• Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.
• Performance Considerations.
• Lab: Optimizing your BigQuery Queries for Performance.
• Optional Lab: Creating Date-Partitioned Tables in BigQuery.

Module 13: Introduction to Analytics and AI

What is AI?.
From Ad-hoc Data Analysis to Data-Driven Decisions.
Options for ML models on GCP.

Module 14: Prebuilt ML model APIs for Unstructured Data

• Unstructured Data is Hard.
• ML APIs for Enriching Data.
• Lab: Using the Natural Language API to Classify Unstructured Text.

Module 15: Big Data Analytics with Cloud AI Platform Notebooks

• What's a Notebook.
• BigQuery Magic and Ties to Pandas.
• Lab: BigQuery in Jupyter Labs on AI Platform.

Module 16: Production ML Pipelines with Kubeflow

• Ways to do ML on GCP.
• Kubeflow.
• AI Hub.
• Lab: Running AI models on Kubeflow.

Module 17: Custom Model building with SQL in BigQuery ML

• BigQuery ML for Quick Model Building.
• Demo: Train a model with BigQuery ML to predict NYC taxi fares.
• Supported Models.
• Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.
• Lab Option 2: Movie Recommendations in BigQuery ML.

Module 18: Custom Model building with Cloud AutoML

• Why Auto ML?
• Auto ML Vision.
• Auto ML NLP.
• Auto ML Tables.