Google Cloud Platform Training Courses

Google Cloud Platform Training

Google Cloud Platform

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Google Cloud Platform Course Outlines

Code Name Duration Overview
cpo200 CPO200: Google Cloud Platform for Systems Operations Professionals 32 hours This 4 day instructor-led class introduces participants to the implementation of application environments and public cloud infrastructure using Google Cloud Platform. Through a combination of instructor-led presentations and hands-on labs, students learn how to deploy cloud infrastructure components such as networks, systems, and applications. This course is designed to give participants a robust hands-on experience and is primarily lab-focused. This class is intended for Systems Operations professionals and Systems Administrators using Google Cloud Platform to create or migrate application environments and infrastructure. At the end of this four-day course, participants will be able to: Understand the core tenants to be considered when designing & deploying to a cloud Use the Google Developers Console to create and manage multiple projects Use service accounts and permissions to share view-level access between projects Create Google Compute Engine instances Create a non-default network and review your network configuration Compare default and non-default networks Create firewall-rules with and without tags Create and use a customized Compute Engine image Set authorization scopes for a Compute Engine instance Reserve an external IP address for an instance Snapshot a Compute Engine instance Snapshot a data disk Create an image using a boot persistent disk Upload an image to Google Container Registry Create a Compute Engine instance group with instances Create a Cloud SQL instance using the Cloud SDK Deploy and test a web application Add instance and project metadata Query instance and project metadata using the Cloud SDK Create an instance using a startup script in metadata and Google Cloud Storage Create an instance with a shutdown script and install the Cloud Logging agent Use the API Explorer to query an API request Run sample code that uses the Google API Client Library Test and build a container that uses the Cloud SQL APIs Create an instance template and managed instance group Configure a managed instance group for autoscaling Create multiple autoscaled managed instance groups Configure fault-tolerant HTTP load balancing Test health checks for use with HTTP load balancing Manage application deployment using Jinja and Python templates with Google Cloud Deployment Manager Delete Google Cloud Platform projects and resources Module 1: Google Cloud Platform Projects Identify project resources and quotas Explain the purpose of Google Cloud Resource Manager and Identity and Access Management Lab: Google Cloud Platform Projects Use the Google Developers Console to create and manage multiple projects Use service accounts and permissions to share view-level access between projects Module 2:Instances Explain how to create and move instances Identify how to connect to and manage instances Lab: Google Compute Engine Instances and Machine Types Create an instance using the Google Developers Console Configure the Cloud SDK on the Compute Engine instance Initialize Cloud Source Repositories using Git Module 3: Networks Explain how to create and manage networks in projects Identify how to create and manage firewall rules, routes, and IP addresses Lab: Google Compute Engine Networks Create a non-default network Compare default and non-default networks Create firewall-rules with and without tags Review network configuration in Google Cloud Monitoring Module 4: Disks and Images Explain how to create and manage persistent disks Identify how to create and manage disk images Lab: Google Compute Engine Disks and Images Create an instance and install the Java 7 JRE from OpenJDK Create a customized Compute Engine image Launch and test a Compute Engine instance based on your image Module 5: Authorization Explain the purposes of and use cases for Google Compute Engine service accounts Identify the types of service account scopes Lab: Google Compute Engine Authorization Set authorization scopes for a Compute Engine instance Reserve the external IP address for the new instance Install and configure Jenkins on a Compute Engine instance Module 6: Snapshots Identify the purpose of and use cases for disk snapshots Explain the process of creating a snapshot Lab: Google Compute Engine Snapshots Prepare and snapshot a Compute Engine instance Restore and test the snapshot to a different zone Snapshot a data disk without shutting down an instance Module 7: Google Cloud Storage Explain the purpose of and use cases for Google Cloud Storage Identify methods for accessing Google Cloud Storage buckets and objects Explain the security options available for Google Cloud Storage buckets and objects Lab: Google Cloud Storage for Backups Create and configure Nearline and DRA buckets Modify the lifecycle management policy for a bucket Copy data to a bucket using the Cloud SDK Review, modify, and test bucket ACLs Configure Jenkins to perform a backup to Cloud Storage Test and verify that the backups are working Lab: Google Container Registry Create a customized Jenkins build node instance Create an image using the instance's boot persistent disk Create a test build node instance based on the new image Test uploading images to Google Container Registry Module 8: Instance Groups Identify the purpose of and use cases for instance groups Explain the process of creating and using instance groups Lab: Google Compute Engine Instance Groups Create a Compute Engine instance group with instances Define Jenkins build tasks and run them Run the build tasks to create a guestbook image Module 9: Google Cloud SQL Understand how to create and administer Cloud SQL instances Explain how to access Cloud SQL instances from Compute Engine instances Lab: Google Cloud SQL Create a Cloud SQL instance using the Cloud SDK Create a Compute Engine instance from a custom image Deploy and test the Guestbook web application Module 10: Metadata Explain the purpose of metadata and identify the use cases for project and instance metadata Identify how to set and query metadata Lab: Google Compute Engine Metadata Add instance and project metadata Query instance and project metadata using the Cloud SDK Query metadata from inside a Compute Engine instance Module 11: Startup and Shutdown Scripts Identify the purpose of and use cases for startup and shutdown scripts Lab: Google Compute Engine Startup Scripts Create an instance with a startup script in metadata Create an instance with a startup script from Cloud Storage Create an instance with a shutdown script and install the Cloud Logging agent Lab: Google API Client Library Use the API Explorer to query an API request Run sample code that uses the Google API Client Library Test and build a container that uses the Cloud SQL APIs Create a new Compute Engine image Module 12: Autoscaling Explain the use cases for autoscaling and how autoscaling functions Identify the purpose of autoscaling policies Lab: Google Compute Engine Autoscaler Create an instance template and managed instance group Configure the managed instance group for autoscaling Generate an artificial load to trigger scaling of your cluster Module 13: Load Balancing Explain the differences between network load balancing and HTTP load balancing Identify the purpose of and use cases for cross-region and content-based load balancing Lab: HTTP/HTTPS Load Balancing Create multiple autoscaled managed instance groups Configure fault-tolerant HTTP load balancing Test health checks for use with HTTP load balancing Lab: Google Cloud Deployment Manager Create a Guestbook deployment using a plain YAML format Manage a Guestbook deployment using a Jinja template Create a Guestbook deployment using Python templates Lab: Deleting Cloud Platform Projects and Resources Delete Google Cloud Platform resources Test dependencies between resources Delete Google Cloud Platform projects
cp300a CP300A: Google Cloud Platform for Developers 40 hours This 5 day instructor-led class prepares you to develop applications for Google Cloud Platform. Using a combination of lectures, demonstrations, and hands-on activities, you learn how to create cloud-based application environments and how to deploy and manage cloud-based applications. This class is intended for experienced application developers who want to learn how to develop and deploy applications on Google Cloud Platform. At the end of this five-day course, participants will be able to: Identify the value of Google Cloud Platform Identify the purpose and business value of the core Google Cloud Platform services Use Google Compute Engine features including: Instances, images, persistent disks, startup scripts, metadata, snapshots, networks, and load balancers Build and deploy Google App Engine applications that use caching, authentication, storage (Google Cloud Datastore), and queues Store and secure data using Google Cloud Storage Use Google Cloud SQL to store application data Query and analyze data using Google BigQuery Part I: Introduction to Google Cloud Platform Module 1: Introducing Google Cloud Platform Explain the advantages of Google Cloud Platform Define the components of Google's network infrastructure, including: points of presence, regions, and zones Module 1 Lab: Create a Google Cloud Platform Project Create a Google Cloud Platform project Module 2: Google Cloud Platform Overview Explain the difference between infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) Describe Google Cloud Platform's pricing philosophy Module 3: Examples of using Google Cloud Platform Identify ways customers have used Google Cloud Platform to improve their businesses Module 3 Lab: Sign Up for a Google Cloud Platform Account Sign up for a Google Cloud Platform account Module 4: Google Cloud Platform Endpoints and Services Explain the value of the Google Cloud Platform services for compute, big data, storage, networking and management, and applications Module 5: Interacting with GCP Explain the methods of interacting with Google Cloud Platform, including: the Google Developers Console, the service APIs and API Client Libraries, and the Cloud SDK Module 5 Lab: Install the Cloud SDK Install the Google Cloud SDK on your machine Module 6: Support and Resources Identify the Google Cloud Platform partner options: service partners, technology partners, and training partners List options available for staying up-to-date on Google Cloud Platform: the Google Cloud Platform blog, Google Next, Google I/O, and the Google Cloud Platform newsletter Describe the support options available for Google Cloud Platform: Google Support and Google on Stack Overflow Explain the support options available for products at each point in the lifecycle: alpha, beta, generally availabe, and deprecated Part II: Google Compute Engine Module 1: Introducing Google Compute Engine Explain the role of Compute Engine in the computing continuum Identify the business value and use cases for Compute Engine Module 2: Interacting with Google Compute Engine Explain the methods of interacting with Google Compute Engine, including: the Google Developers Console, the service APIs, and the Cloud SDK Identify the monitoring options for Compute Engine resources Module 2 Lab: Interacting with Google Compute Engine Configure the Cloud SDK for Compute Engine Module 3: Instances Define the concept of a Compute Engine instance List and define instance states Explain how to create an instance and connect to it using SSH Module 3 Lab: Creating Compute Engine Instances Start and shut down Google Compute Engine instances Connect to and customize Google Compute Engine instances Module 4: Disks Explain the purpose of boot disks and persistent disks Contrast standard disks with solid-state drives (SSD) Define the process for mounting a persistent disk Module 4 Lab: Creating and Attaching Disks Create a disk resource Attaching a disk to an instance Format and mount the disk using the command-line utility Module 5: Images Explain the concepts of public and private images Define the process of creating a custom image Explain how to share a Compute Engine image Module 5 Lab: Creating Custom Images Create instances using a Compute Engine image Install software on an instance using the Docker platform Create an image using a persistent disk Create an instance based on a custom image Module 6: Snapshots Explain the purpose of and use cases for disk snapshots Define the process of creating snapshots and using them to restore disks Explain the process of moving an instance using snapshots Module 6 Lab: Working with Snapshots Use snapshots to migrate a disk resource across zones Use snapshots to change a disk to a solid-state drive (SSD) Modify a persistent disk before taking a snapshot Module 7: Networks Explain the process of creating networks and firewall rules Contrast public and private IP addresses Define basic networking concepts, including: proxies, VPNs, and routing Module 7 Lab: Working with Networks and IP Addresses Create networks and firewall rules Reserve IP addresses Module 8: Authorization Configure authorization requests to Compute Engine Use service accounts to configure access to Compute Engine Explain the concept of authorization scopes Module 8 Lab: Authorizing Requests from Compute Engine Create a Compute Engine instance that uses a service account to making read only calls to the Compute Engine API Install the Google API Client Libraries for Python Use a Python program to download a service account token that allows your instance to make Compute Engine API requests Module 9: Metadata Explain the concepts of instance and project metadata Set and query metadata Identify use cases for metadata Module 9 Lab: Project and Instance Metadata Set custom metadata for projects and instances Query metadata endpoints for projects and instances List custom metadata associated with an instance Query instance metadata Module 10: Startup scripts Identify how to use startup and shutdown scripts Use startup scripts with metadata Explain the role of startup scripts in configuration management Module 10 Lab: Getting Started with Startup Scripts Use a startup script when creating an instance Monitor the progress of your startup script and examine the results Manually run a startup script Module 11: Network Load Balancing Explain the concepts of network and HTTP load balancing Identify the differences between network and HTTP loan balancing Explain the process of configuring HTTP and network load balancing Part III: Google App Engine and Google Cloud Datastore Module 1: Introducing Google App Engine Identify the business value and use cases for App Engine Explain how to create App Engine applications that are scalable, reliable, and cost effective Identify the components of the App Engine architecture Module 2: Google App Engine Fundamentals Explain the architecture of App Engine instances Define the three types of application scaling: manual, basic, and automatic Explain the process of changing application scaling settings Module 2 Lab: App Engine Fundamentals Configure, deploy, and verify an App Engine sample application Examine an application using the project dashboard Module 3: Building and Managing Your Application Identify the impact of App Engine quotas on your applications Manage application deployment using versioning Module 3 Lab: Building and Managing Your Application Configure, deploy, and verify an App Engine sample application Change and test application scaling settings Module 4: Authenticating Users Explain the options for authenticating users in App Engine applications Identify methods of restricting access to applications Explain the options for authorizing access to applications Module 4 Lab: Authenticating Users Configure settings to restrict access to application components Use the User Service to configure user login and logout and to personalize the user experience Module 5: Caching and State Management Explain the purpose of and use cases for edge caching Explain the purpose of and use cases for Memcache Identify the process of enabling edge caching and Memcache for applications Module 5 Lab: Memcache Use the Memcache API in an App Engine application Use Memcache Viewer to view Memcache data Module 6: Introducing Google Cloud Datastore Identify the business value and use cases for Cloud Datastore Define the basic Cloud Datastore components: kinds, keys, and entities Create and retrieve entries using Cloud Datastore Module 6 Lab: Introduction to Cloud Datastore Use Cloud Datastore to store application data View data using the Datastore viewer Module 7: Cloud Datastore queries and indexes Describe how to query application data in Cloud Datastore Explain how indexes function in Cloud Datastore and how to use them Module 7 Lab: Queries and Indexes Query your application data in Cloud Datastore View indexes in Cloud Datastore Module 8: Cloud Datastore entity groups and transactions Contrast eventual and strong consistency for index entries Explain how to use ancestor and descendant queries Identify the purpose of entity groups Explain how transactions are handled in Cloud Datastore Module 8 Lab: Entity Groups Experiment with eventual and strong consistency for index entries Use entity groups and create ancestor queries Module 9: Decoupling work using Queues & Scheduled Tasks Identify the function of pull queues, push queues, and task queues Explain how to use cron and scheduled tasks Module 9 Lab: Task Queues and Cron Create, configure, and use push queues Setup and administer cron jobs Part IV: Google Cloud Storage Module 1: Introducing Google Cloud Storage Identify the business value and use cases for Cloud Storage Explain the function of the basic Cloud Storage components: buckets, objects, and endpoints Module 2: Google Cloud Storage Components Explain consistency for Cloud Storage data Define the classes of Cloud Storage buckets: standard and durable reduced availability Explain object versioning and object lifecycle management Module 2 Lab: Getting Started with Google Cloud Storage Create a bucket, add an object, and share access to bucket contents Configure permissions Module 3: Interacting with Google Cloud Storage Explain the methods of interacting with Google Cloud Storage, including: the Google Developers Console, the service APIs, the client libraries, and the Cloud SDK Describe the options for uploading and importing data Explain the function of composite objects Module 3 Lab: gsutil Use gsutil to create and list buckets Upload data to your bucket Module 4: Access Control Lists Explain the purpose of access control lists Configure access to objects and buckets using access control lists Module 4 Lab: Access Control Lists Configure a bucket with a custom access control list using gsutil Grant permissions to a specific users and App Engine applications using access control lists Module 5: Signed URLs Explain the purpose of a signed URL Explain the process of creating a signed URL Module 5 Lab: Signed URLs Add an object to a Cloud Storage bucket Configure an access control list for your bucket Created a signed URL using gsutil Access your bucket using the signed URL Module 6: Website hosting Explain the purpose of hosting static web content in Cloud Storage Define the process of configuring a bucket to host web content Explain the role of cross origin resource sharing in hosting web content in Cloud Storage Module 7: Object Change Notification Explain the purpose of object change notifications in Cloud Storage Explain the process of configuring object change notifications Part V: Google Cloud SQL Module 1: Introducing Google Cloud SQL Identify the business value and use cases for Cloud SQL Module 2: Database administration Describe the administrative tasks managed by Cloud SQL and identify tasks available to customers Explain the replication configurations for Cloud SQL Explain the import and export options available for Cloud SQL data Module 3: Interacting with Cloud SQL Explain the methods of interacting with Google Cloud SQL, including: the Google Developers Console, the service APIs, and the Cloud SDK Explain how to create a secure connection to Cloud SQL Module 3 Lab 1: Interacting with Cloud SQL Create and configure a Cloud SQL instance Change Cloud SQL settings using the command line Module 3 Lab 2: Remote SSL Encrypted Connection Create and configure a Cloud SQL instances and install MySQL client software Configure an SSL connection between your instances Module 4: Using Your Instances from Google App Engine Explain the process of accessing Cloud SQL from App Engine Describe how to use the Cloud SQL APIs in App Engine applications Module 4 Lab: Accessing Cloud SQL from App Engine Configure an App Engine application to access Cloud SQL Module 5: Using Your Instances from Google Compute Engine Explain the process of accessing Cloud SQL from Compute Engine Module 5 Lab: Accessing Cloud SQL from Compute Engine Connect to a Cloud SQL instance from a Compute Engine instance Part VI: Google BigQuery Module 1: Introducing Google BigQuery Identify the business value and use cases for BigQuery Module 2: BigQuery Fundamentals Define the core components of BigQuery projects: datasets, tables, and jobs Explain the architecture of BigQuery Contrast columnar storage with row storage Module 2 Lab: Getting Started with BigQuery Run queries against public datasets Module 3: Interacting with BigQuery Explain the methods of interacting with Google Cloud SQL, including: the BigQuery Console, the service APIs, the client libraries, and the Cloud SDK Module 3 Lab: Interacting with BigQuery Use the BigQuery command-line tool Import a dataset Query your data using the command line Module 4: Preparing and loading data with BigQuery Explain how to prepare your data for ingestion into BigQuery Identify options for staging data in Google Cloud Platform List the options available for ingesting data into BigQuery Module 4 Lab: Loading Data into BigQuery Prepare comma-separated data for ingestion into BigQuery Ingest your data into a BigQuery table Query your data Module 5: Query clauses and functions List and define the various types of query clauses and functions available in BigQuery Identify the proper way to use some of the more common query functions Module 6: Working with large datasets Explain the options for working with large datasets in BigQuery and identify any constraints Determine when it is appropriate to use the GROUP BY and GROUP EACH BY functions with large datasets Identify the purpose of using table decorators and wildcard functions Module 7: Nested and repeated fields Explain how to use nested fields, repeated fields, and repeated nested fields in BigQuery Module 7 Lab 1: Nested Fields Create a JSON file containing a schema definition with nested fields Create a JSON file containing data with nested fields Upload the schema and data to BigQuery Query the data containing nested fields Module 7 Lab 2: Repeated Fields Create a JSON file containing a schema definition with repeated fields Create a JSON file containing data with repeated fields Upload the schema and data to BigQuery Query the data containing repeated fields Module 7 Lab 3: Repeated Nested Fields Create a JSON file containing a schema definition with repeated nested fields Create a JSON file containing data with repeated nested fields Upload the schema and data to BigQuery Query the data containing repeated nested fields Module 8: Exporting data Explain how access controls and quotas function in BigQuery List the data export formats supported by BigQuery Explain the process of exporting data from BigQuery Module 8 Lab: Exporting Data Export data from a BigQuery table to a Cloud Storage bucket
cpb200 CPB200: Google BigQuery for Data Analysts 24 hours This 3 day instructor led class introduces participants to Google BigQuery. Through a combination of instructor­led presentations, demonstrations, and hands­on labs, students learn how to store, transform, analyze, and visualize data using Google BigQuery. This class is intended for data analysts and data scientists responsible for: analyzing and visualizing big data, implementing cloud­based big data solutions, deploying or migrating big data applications to the public cloud, implementing and maintaining large­scale data storage environments, and transforming/processing big data. At the end of this one­day course, participants will be able to: Understand the purpose of and use cases for Google BigQuery Describe ways in which customers have used Google BigQuery to improve their businesses Understand the architecture of BigQuery and how queries are processed Interact with BigQuery using the web UI and command­line interface Identify the purpose and structure of BigQuery schemas and data types Understand the purpose of and advantages of BigQuery destinations tables and caching Use BigQuery jobs Transform and load data into BigQuery Export data from BigQuery Store query results in a destination table Create a federated query Export log data to BigQuery and query it Understand the BigQuery pricing structure and evaluate mechanisms for controlling query and storage costs Identify best practices for optimizing query performance Troubleshoot common errors in BigQuery Use various BigQuery functions Use external tools such as spreadsheets to interact with BigQuery Visualize BigQuery data Use access controls to restrict access to BigQuery data Query Google Analytics Premium data exported to BigQuery Module 1: Introducing Google BigQuery ● Understand the purpose of and use cases for Google BigQuery ● Describe ways in which customers have used Google BigQuery to improve their businesses Lab: Sign Up for the Free Trial and Create a Project ● Register for the GCP free trial ● Create a project using the Cloud Platform Console Module 2: BigQuery Functional Overview ● Describe the components of a BigQuery project ● Identify how BigQuery stores data and list the advantages of the storage model ● Understand the architecture of BigQuery and how queries are processed ● Describe the methods of interacting with BigQuery Lab: Explore BigQuery Interfaces ● Explore features of the BigQuery web UI ● Learn how to use the bq shell ● Execute queries using the BigQuery CLI in Cloud Shell Module 3: BigQuery Fundamentals ● Describe the purpose of denormalizing data ● Identify the purpose and structure of BigQuery schemas and data types ● Explain the types of actions available in BigQuery jobs ● Understand the purpose of and advantages of BigQuery destinations tables and caching Lab: BigQuery Components and Jobs ● Explore how data is organized in BigQuery ● Learn about the two types of table schemas ● Learn about jobs, and how to cancel them ● Investigate caching and destination tables Module 4: Ingesting, Transforming, and Storing Data ● Describe the methods for ingesting data, transforming data, and storing data using BigQuery ● Explain the function of BigQuery federated queries Lab 4, Part I: Loading Data into BigQuery and Using Federated Queries ● Load a CSV file into a BigQuery table using the web UI ● Load a JSON file into a BigQuery table using the CLI ● Transform data and join tables using the web UI ● Store query results in a destination table ● Query a destination table using the web UI to confirm your data was transformed and loaded correctly ● Export query results from a destination table to Google Cloud Storage ● Create a federated query that queries data in Cloud Storage Lab 4, Part II: Exporting App Engine Logs to BigQuery ● Set up Google Cloud Logging to export App Engine log data from the Guestbook application ● Use the BigQuery web UI to query the log data Module 5: Pricing and Quotas ● Explain the advantages of the BigQuery pricing model ● Use the pricing calculator to calculate storage and query costs ● Identify the quotas that apply to BigQuery projects Lab: BigQuery Pricing ● Evaluate the size of a query within BigQuery using the BigQuery web UI ● Use the Pricing Calculator and the total size of the query to estimate the query cost ● Examine how changing a query affects query cost Module 6: Clauses and Functions ● Explain the differences between BigQuery SQL and ANSI SQL ● Identify the purpose of and use cases for user­defined functions ● Explain the purpose of various BigQuery functions Lab: BigQuery Clauses and Functions ● Create and run a query using a wildcard function ● Create and run a query using a window function ● Create and run a query using a user­defined function Module 7: Nested and Repeated Fields ● Identify the purpose and structure of BigQuery nested, repeated, and nested repeated fields ● Describe the use cases for nested, repeated, and nested repeated fields Lab: Nested Fields ● Create a BigQuery table using nested data ● Run queries to explore the structure of the nested data Lab: Repeated Fields ● Create a BigQuery table using repeated data ● Run queries to explore the structure of the repeated data Lab: Nested Repeated Fields ● Create a BigQuery table using nested repeated data ● Run queries to explore the structure of the nested repeated data Module 8: Query Performance ● Explain the impact of the following in query performance: JOIN and GROUP BY, table wildcards, and table decorators ● Identify various best practices for optimizing query performance Lab: BigQuery Best Practices and Optimization Techniques ● Use denormalization to improve query performance ● Use subselects to improve the performance of queries with JOIN clauses ● Use destination tables to lower costs when running multiple, similar queries ● Use table decorators and table wildcards to improve query performance and to reduce costs Module 9: Troubleshooting Errors ● Describe how to handle the most common BigQuery errors: request encoding errors, resource errors, and HTTP errors Lab: Handling Errors ● Correct queries that produce syntax­related error messages ● Correct an error involving the order of a JOIN clause ● Correct an error involving an invalid table name ● Modify queries that exceed resource constraints Module 10: Access Control ● Describe the purpose of access control lists in BigQuery ● List and explain the project and dataset roles available in BigQuery ● Apply views for row­level security Lab: Access Control ● Manage access to datasets using project­level ACLs ● Manage access to datasets using dataset­level ACLs ● Set row­level permissions using views Module 11: Exporting Data ● List the methods of exporting data from BigQuery and the data formats available ● Describe the process of creating a job to export data from BigQuery ● Explain the purpose of wildcard exports to partition export data Lab: Exporting Data ● Export data from BigQuery using the web UI and CLI ● Export large tables using wildcard URIs Module 12: Interfacing with External Tools ● Describe how to use external tools to interface with BigQuery, including: spreadsheets, ODBC and JDBC drivers, the BigQuery encrypted client, and R Lab: Interfacing with External Tools ● Set up the BigQuery Reports add­on for Google Sheets ● Use the Reports add­on to query BigQuery data Module 13: Working with Google Analytics Premium Data ● Describe the schema of the Google Analytics Premium and AdSense data exported to BigQuery Lab: Working with Google Analytics Premium Data ● Build queries to analyze data from Google Analytics Premium Module 14: Data Visualization  ● Describe the options available for visualizing BigQuery data Lab: Visualizing Data ● Use Google Cloud Datalab to visualize data
cpd200 CPD200: Developing Solutions on Google Cloud Platform 24 hours This 3 day instructor-led class introduces participants to Solution Development for Google Cloud Platform. Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn how to develop cloud-based applications using Google App Engine, Google Cloud Datastore, and Google Cloud Endpoints. This class is intended for experienced application developers who want to learn how to develop solutions using Google Cloud Platform to create highly scalable backends for both web and mobile applications. At the end of this one­day course, participants will be able to: Manage Google Cloud Source Repositories using the Google Cloud Platform Console Test an App Engine application using the App Engine SDK Access the App Engine Development Server Console Create an API using Google Cloud Endpoints Test a Cloud Endpoint API using the API Explorer Deploy an application to App Engine using the App Engine SDK Design, structure and configure an App Engine application using multiple services Create Client IDs using the Google Cloud Platform Console Secure App Engine services and Cloud Endpoints APIs using authentication Configure and upload new versions of App Engine services Integrate Google Cloud Logging into App Engine applications Review quota usage in a Google Cloud Platform project Integrate different types of storage with App Engine applications Create and implement a data model for use with Google Cloud Datastore Implement a variety of queries in Google Cloud Datastore Update the index configuration in Google Cloud Datastore Implement transactions using Google Cloud Datastore Review Google Cloud Trace reports in the Google Cloud Platform Console Integrate the Memcache API into an App Engine application to increase performance Configure, run and review the output of Google Cloud Security Scanner Configure the scaling behaviour of individual App Engine Services Create App Engine handlers for Push Task Queues Send email from an App Engine application using the Mail API Schedule Tasks in App Engine using the Cron Service Update the configuration of the Cron Service Secure Task Push, and Cron Service handlers  Export Google Cloud Platform data from a project Delete Google Cloud Platform projects and resources   Module 1: Developing Solutions for Google Cloud Platform Identify the advantages of Google Cloud Platform for solution development Identify services and tools available for solution development using Google Cloud Platform Compare examples of Google Cloud Platform architectures for solution development Lab: Google Cloud Source Repositories Create a project for the course Use Google Cloud Shell to develop and test an application using the App Engine SDK  Configure Google Cloud Source Repositories to remotely host code in Google Cloud Platform Module 2: Google Cloud Endpoints Identify Cloud Endpoints features Explain how to develop APIs using Cloud Endpoints Compare development of Cloud Endpoints APIs using Java and Python Lab: Google Cloud Endpoints Review and edit Cloud Endpoints source code Deploy an application to App Engine Test a Cloud Endpoints  API in the APIs Explorer Module 3: App Engine Services Explain the use cases for App Engine Services and how to use them in structuring an application Identify how to deploy and access individual App Engine services Explain how to route requests to individual services Lab: Google App Engine Services Review the code for a sample application used throughout the remainder of the course Deploy multiple App Engine services to a single project Module 4: User Authentication and Credentials Compare authentication and authorization Identify options for securing App Engine applications Explain the use cases for Application Default Credentials Lab: User Authentication Configure the OAuth consent screen and create a client ID Modify Conference Central to use your client ID Test Conference Central authentication Modify your admin service to require administrator rights Module 5: Managing App Engine Applications Explain the use cases for App Engine versions Identify how to access App Engine monitoring and logging services Explain the use of resource quotas and how to troubleshoot related errors Lab: Managing Google App Engine Applications Review App Engine settings, quotas, instances, and logs Update App Engine services to log to Cloud Logging Deploy new versions of your default and admin services Route all traffic to a new version of the default service Module 6: Storage for Solution Developers Compare storage options for App Engine Solutions Developers Explain the purpose of, and use cases for, Google Cloud Storage Compare Cloud SQL integration with App Engine and Compute Engine Explain basic Cloud Datastore terminology and concepts, including Entity Groups Lab: Google Cloud Datastore Update an existing application to save data persistently Test saving application data to Cloud Datastore List and view Cloud Datastore entities in the Google Cloud Platform Console Module 7: Queries and Indexes Identify available query filters for Cloud Datastore Compare single­property, and composite indexes in Cloud Datastore Configure and optimize indexes for Cloud Datastore Lab: Google Cloud Datastore Queries and Indexes Add support for querying entities by kind and ancestor Add query filters to Cloud Datastore searches Update an index configuration to support composite indexes Module 8: Entity Groups, Consistency, and Transactions Identify the steps of a Cloud Datastore write Compare strong and eventual consistency in Cloud Datastore Identify how to achieve strongly consistent queries Identify best practises for Cloud Datastore transactions Lab: Google Cloud Datastore Transactions Add support for using Cloud Datastore transactions to an application Add a Cloud Endpoint API method to view data from a different service Module 9: App Engine Performance and Optimization Identify Memcache types, use cases, and implementation patterns Compare available scaling behaviours for application services Configure application scaling for individual services Lab: Google App Engine Performance and Optimization Review Cloud Trace reports for an application Configure and run a security scan for an application Update an application to make use of memcache Configure and test application scaling for application services Module 10: Task Queues Compare Push and Pull Queues Explain how to schedule tasks with the Cron Service Configure and securing Push and Pull Queues, as well as the Cron Service Lab: Task Queue API Add a task handler to send an email using the Mail API Add a Cron Service handler and configuration to an existing application Lab: Deleting Google Cloud Platform Projects and Resources Export Google Cloud Platform data from a project Delete Google Cloud Platform resources Shut down a Google Cloud Platform project
cpv200 CPV200: Google Container Engine and Kubernetes 8 hours This 1 day instructor­led class introduces participants to Google Container Engine (GKE) and Kubernetes. Through a combination of instructor­led presentations, demonstrations, and hands-on labs, students learn the key concepts and practices for deploying and maintaining applications using Google Container Engine. This class is intended for solutions developers, systems operations professionals, solution architects, and development operations professionals who develop, migrate, and deploy container­based applications on Google Cloud Platform (GCP). At the end of this one­day course, participants will be able to: Identify the purpose of and use cases for Google Container Engine and Kubernetes Explain the function of the container cluster components: pods, labels, replication controllers, and services Identify the purpose of and use cases for Google Container Registry Create a Docker image and send it to the Google Container Registry Explain the function of the cluster components: the master instance an cluster nodes Use the Cloud Platform Console to create a container cluster Use the Google Cloud SDK command­line tools and the kubectl command­line utility to interact with container clusters Define a pod using a JSON template Deploy the pod to a Container Engine cluster Create a Container Engine cluster to host a dynamically scaled group of Redis instances  Complete a configuration file which defines a replication controller and the pod configuration that it manages After creating a replication controller and pods, rescale the group Deploy a container­based application using YAML files that define services, replication controllers, pods, and so on Define firewall rules and load balancers to expose a service for a container­based application Module 1: Introduction to Google Container Engine Identify the purpose of and use cases for Google Container Engine and Kubernetes Explain the function of the container cluster components: pods, labels, replication controllers, and services Identify the purpose of and use cases for Google Container Registry Lab, Part I: Sign Up for the Free Trial and Create a Project Register for the GCP free trial Create a project using the Cloud Platform Console Lab, Part II: Create a Compute Engine Labs Instance Create a Linux­based Compute Engine VM instance Configure the Cloud SDK on the instance Lab, Part III: Create a Docker Image and Send It to the Registry Create a Docker image and send it to the Google Container Registry Module 2: Google Container Engine Fundamentals Explain the function of the cluster components: the master instance and cluster nodes Lab: Create a Container Cluster Use the Cloud Platform Console to create a container cluster Module 3: Interacting with Google Container Engine Describe the various methods used to interact with a container cluster Lab: Working with Container Engine clusters using the Cloud SDK and kubectl Use the Google Cloud SDK command­line tools and the kubectl command­line utility to interact with container clusters Module 4: Container Clusters Describe the high­level container cluster infrastructure Identify the components of a container cluster node Module 5: Pods Describe the purpose of a pod and how to work with pods Lab: Define and Deploy a Pod Define a pod using a JSON template Deploy the pod to a Container Engine cluster Module 6: Replication Controllers Describe the purpose of a replication controller and how to work with replication controllers Lab: Replication Controllers Create a Container Engine cluster to host a dynamically scaled group of Redis instances  Complete a configuration file which defines a replication controller and the pod configuration that it manages After you creating the replication controller and its pods, rescale the group Module 7: Services Describe the purpose of a service and how to work with services Lab: Deploy an Application Deploy a container­based application using YAML files that define services, replication controllers, pods, and so on Define firewall rules and load balancers to expose a service for a container­based application
cpb100 CPB100: Google Cloud Platform Big Data & Machine Learning Fundamentals 8 hours This 8 hour instructor-led class introduces participants to the Big Data & Machine Learning capabilities of Google Cloud Platform. It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. (For a more general overview of Google Cloud Platform, see CP100A) This class is intended for: Data analysts Data scientists Business analysts It is also suitable for IT decision makers evaluating Google Cloud Platform for use by data scientists. This class is for people who do the following with big data: Extracting, Loading, Transforming, cleaning, and validating data for use in analytics Designing pipelines and architectures for data processing Creating and maintaining machine learning and statistical models Querying datasets, visualizing query results and creating reports At the end of this one­day course, participants will be able to: Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform Employ BigQuery and Cloud Datalab to carry out interactive data analysis Choose between Cloud SQL, BigTable and Datastore Train and use a neural network using TensorFlow Choose between different data processing products on the Google Cloud Platform Module 1: Introduction In this module you will be introduced to Google Cloud Platform and the data handling aspects of the platform. What is the Google Cloud Platform? GCP Big Data Products Usage scenarios Lab: Sign up for Google Cloud Platform Module 2: Foundation of GCP (Compute and Storage) In this module, we introduce the foundations of the Google Cloud Platform: compute and storage and introduce how they work to provide data ingest, storage, and federated analysis. CPUs on demand (Compute Engine) GCE: the value proposition Lab: Start GCE instance, ssh access A global filesystem (Cloud Storage) Google Storage, data centers, zones, regions GS and its role in data processing Lab: Set up a Ingest-Transform-Publish data processing pipeline CloudShell Module 3: Data Analytics on the Cloud In this module we introduce the common Big Data use cases that Google will manage for you. These are the things that are widely done in industry today and for which we provide easy migration to the cloud. Stepping stones to the cloud Where GCP started Towards no-ops CloudSQL: your SQL database on the cloud A no-ops database Lab: importing data into CloudSQL and running queries on rentals data Dataproc Managed Hadoop + Pig + Spark on the cloud Lab: Machine Learning with SparkML Module 4: Scaling data analysis This module is about the more transformational technologies in Google Cloud platform that may not have immediate parallels to technologies that attendees are using (“what's next”). Fast random access Datastore: Key-Entity BigTable: wide-column Datalab Why Datalab? (interactive, iterative) Demo: Sample notebook in datalab BigQuery Interactive queries on petabytes Lab: Build machine learning dataset Machine Learning with TensorFlow TensorFlow Lab: Train and use neural network Fully built models for common needs Vision API Translate API Lab: Translate Genomics API (optional) What is linkage disequilibrium? Finding LD using Dataflow and BigQuery Module 5: Data processing architectures In this module we will introduce you to data processing architectures in Google Cloud Platform. Asynchronous processing with TaskQueues Message-oriented architectures with Pub/Sub Creating pipelines with Dataflow Module 6: Summary Why GCP? Where to go from here Resources
cpb101 CPB101: Serverless Data Analysis with BigQuery and Cloud Dataflow 8 hours This 8 hour instructor led course builds upon the CPB100 (which is a prerequisite). Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn how to carry out no-ops data warehousing, analysis and pipeline processing. This class is intended for data analysts and data scientists responsible for: analyzing and visualizing big data, implementing cloud-based big data solutions, deploying or migrating big data applications to the public cloud, implementing and maintaining large-scale data storage environments, and transforming/processing big data. Objectives Build up a complex BigQuery using clauses, inner selects, built-in functions and joins Load and export data to/from BigQuery Identify need for nested, repeated fields and user-defined functions Understand pipeline processing, terms and concepts Write pipelines in Java or Python and launch them locally or in the Cloud Implement Map,  Reduce  trransforms in Dataflow pipelines. Join datasets as side inputs Interoperate Dataflow, BigQuery and Cloud Pub/Sub for real-time streaming The basic thrust is to cover the foundations in Module 2, workloads they could migrate to GCP immediately (i.e., lift-and-shift) in Module 3, and the more transformational things (i.e., what’s next) in Module 4. Module 0: Welcome  [⅓ hr] We assume that attendees may attended CPB100. Logistics Introductions Module 1: Serverless data analysis with BigQuery [3  hr] A 3 hour (1.5 hours lecture + 1.5 hours hands-on) deep dive into details of BigQuery. What is BigQuery? Queries and functions + lab Load and export data + lab Advanced Capabilities Performance and pricing Module 2: Serverless, autoscaling data pipelines with Dataflow [3  hr] A 3 hour (1.5 hours lecture + 1.5 hours hands-on) deep dive into details of Cloud Dataflow.  What is Dataflow? Data pipeline + lab MapReduce in Dataflow + lab Side inputs + lab Streaming + demo Module 3: Summary [⅓  hr] Where to go from here Resources
cpb102 CPB102: Machine Learning with CloudML 8 hours This 8-hour instructor led course builds upon CPB100 and CPB101 (which are prerequisites). Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn machine learning and Tensorflow concepts and develop hands-on skills in developing, evaluating, and productionizing machine learning models. This class is intended for programmers and data scientists responsible for developing predictive analytics using machine learning.  The typical audience member has experience analyzing and visualizing big data, implementing cloud-based big data solutions, and transforming/processing datasets. Objectives Understand what kinds of problems machine learning can address Build a machine learning model using TensorFlow Build scalable, deployable ML models using Cloud ML Know the importance of preprocessing and combining features Incorporate advanced ML concepts into their models Invoke and customize ML APIs Productionize trained ML model   Module 0: Welcome  [⅓ hr] We assume that attendees attended CPB100. Logistics Introductions Module 1: Getting started with Machine Learning [1½ hr] What is machine learning (ML)? Effective ML: concepts, types Evaluating ML ML datasets: generalization Lab: Explore and create ML datasets Module 2: Building ML models with Tensorflow [2 hr] Getting started with TensorFlow Lab: Using tf.learn TensorFlow graphs and loops + lab Lab: Using low-level TensorFlow + early stopping Monitoring ML training Lab: Charts and graphs of TensorFlow training Module 3: Scaling ML models with CloudML [1 hr] Why Cloud ML? Packaging up a TensorFlow model End-to-end training Lab: Run a ML model locally and on cloud Module 4: Feature Engineering [1.5 hr] Creating good features Transforming inputs Synthetic features Preprocessing with Cloud ML Lab:   Feature engineering Module 5: ML architectures [optional] Wide and deep Image analysis Embeddings and sequences Recommendation systems Summary
cpa200 CPA200: Google Cloud Platform Architect Fundamentals 8 hours The intended audience is people who will be designing solutions using Google Cloud Platform but who do not have a lot of experience with designing cloud-based solutions. At the end of this one-day class, participants will be able to: Understand the core tenants to be considered when designing & deploying to a cloud Be confident enough to leverage what Google Cloud Platform offers without focusing on undifferentiated heavy lifting Understand how to get started on Google Cloud Platform Be able to identify the appropriate Google Cloud Platform products to use for popular architectural patterns   Module 1 - Keeping things simple Managing applications at scale Describe the problems that Google addressed to allow them to deploy Google scale applications Explain how using Google Cloud addresses each of the problems faced when designing for distributed scalable applications that are deployed across regions Micro services Security & compliance Module 2 - Focusing on Your Business Managing applications at scale Describe the problems that Google addressed to allow them to deploy Google scale applications Explain how using Google Cloud addresses each of the problems faced when designing for distributed scalable applications that are deployed across regions Micro services Security & compliance Module 3 - Embrace Failure Decoupling Self healing Testing Module 4 - Moving to the Cloud Migrating applications to Google Cloud Platform Off site disaster recovery and archival with Google Cloud Platform Hybrid architectures and multi cloud deployments Lock in is not an issue using Google Cloud Platform Module 5 - Architectural patterns using Google Cloud Platform Cloud Deployment manager Image processing Mobile applications Big Data Virtual network environments
cp100a CP100A: Google Cloud Platform Fundamentals 8 hours This 1 day instructor-led class provides an overview of Google Cloud Platform products and services. Through a combination of presentations and hands-on labs, participants learn the value of Google Cloud Platform and how to incorporate cloud-based solutions into business strategies. This class is intended for solutions developers, systems operations professionals, and solution architects planning to deploy applications and create application environments on Google Cloud Platform. This class is also suitable for executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs. At the end of this one-day course, participants will be able to: Identify the purpose and value of each of the Google Cloud Platform products and services Explain the difference between Iaas and PaaS List the methods of interacting with Google Cloud Platform services Describe ways in which customers have used Google Cloud Platform to improve their businesses Understand how to choose an appropriate application deployment environment on Google Cloud Platform: Google App Engine, Google Container Engine, or Google Compute Engine Deploy an application to: Google App Engine, Google Container Engine, and Google Compute Engine Compare the Google Cloud Platform storage options: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore Deploy an application that uses Google Cloud Datastore and Google Cloud Storage to store data Load data into BigQuery and query it Module 1: Introducing Google Cloud Platform Explain the advantages of Google Cloud Platform Define the components of Google's network infrastructure, including: Points of presence, regions, and zones Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) Lab: Sign Up for the Free Trial and Create a Project Register for the GCP free trial Create a project using the Cloud Platform Console Module 2: Getting Started with Google Cloud Platform Identify the purpose of projects on Google Cloud Platform Understand the purpose of and use cases for Identity and Access Management List the methods of interacting with Google Cloud Platform Lab: Getting Started with Google Cloud Platform Deploy a LAMP stack using Google Cloud Launcher Module 3: Google App Engine and Google Cloud Datastore Understand the purpose of and use cases for Google App Engine and Google Cloud Datastore Contrast the App Engine Standard environment with the App Engine Flexible environment Understand the purpose of and use cases for Google Cloud Endpoints ​Lab: Deploying Applications Using App Engine and Cloud Datastore Deploy a sample Python application called Bookshelf to the App Engine standard runtime environment Test the Bookshelf application and inspect data saved to Cloud Datastore Module 4: Google Cloud Platform Storage Options Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, and Google Cloud Bigtable Learn how to choose between the various storage options on Google Cloud Platform Lab: Integrating Applications with Google Cloud Storage Create a Google Cloud Storage bucket to store images Deploy an App Engine application that uses Cloud Storage Use the Cloud Storage Browser to view objects Module 5: Google Container Engine Define the concept of a container and identify uses for containers Identify the purpose of and use cases for Google Container Engine and Kubernetes Lab: Deploying Applications Using Google Container Engine Create a container cluster using the Cloud SDK Build and push a Bookshelf image to Container Registry Use kubectl to deploy the Bookshelf container Module 6: Google Compute Engine and Networking Identify the purpose of and use cases for Google Compute Engine Understand the various Google Cloud Platform networking and operational tools and services Lab: Deploying Applications Using Google Compute Engine Create a Google Compute Engine instance Deploy the Bookshelf application using a startup script Add a firewall rule to allow HTTP traffic to the application Module 7: Big Data and Machine Learning Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms Lab: Getting Started with BigQuery Load a CSV file into a BigQuery table using the web UI Query the data using the BigQuery web UI Query the data using the CLI and the BigQuery shell

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