CLI Cloud Resource Management

CLI Cloud Resource Management

• describe how gcloud is used to managed cloud resources
• take a snapshot using gcloud
• deploy a VM using gcloud
• use gcloud to delete a virtual machine instance
• use gcloud to manage DNS
• use gcloud to manage firewall settings
• use gsutil to create a cloud storage bucket
• use gsutil to copy data into a bucket
• use gsutil to manage storage ACLs

The gcloud and gsutil CLI tools can be used to deploy and manage Google Cloud resources and automate repetitive administrative tasks. Explore these Google Cloud tools.

Cloud Identity Management

Cloud Identity Management

• describe how user identities are used in the Google Cloud
• describe how on-premises Active Directory links up with a Google Cloud domain
• configure Google Cloud IAM users
• describe how MFA enhances security
• enable multifactor authentication for a single user
• enable Cloud Identity for centralized user and group management
• enable multifactor authentication for Cloud Identity users
• identify how storage ACLs are managed
• set storage ACLs to meet business needs

Discover how to create and manage Google Cloud users and groups with Identity and Access Management.

Cloud Solution Management & Testing

Cloud Solution Management & Testing

• describe the meaning of each SDLC phase
• recognize how ITIL provides efficient and cost-effective IT service delivery
• recall how continuous integration and deployment provide updates
• describe how fuzz testing is used to ensure security and quality
• describe the purpose of regression testing
• describe how unit testing uses a modular testing approach
• specify how load and stress tests can identify capacity weaknesses within a solution

Cloud solutions should follow a structured approach throughout their lifetime. Explore SDLC phases, ITIL, and various testing techniques.

Google Cloud Basics

Google Cloud Basics

• define which items define cloud computing
• recognize how cloud computing can provide business advantages
• define what public clouds are and when they should be used
• define what private clouds are and when they should be used
• define what community clouds are and when they should be used
• define what hybrid clouds are and when they should be used
• describe SaaS cloud services
• describe IaaS cloud services
• describe PaaS cloud services
• describe DRaaS cloud services

Discover the characteristics that define cloud computing. Explore cloud types such as public and community, and also common cloud service models.

Google Cloud Data Storage

Google Cloud Data Storage

• recognize Google Cloud storage options
• configure a Google Cloud storage bucket
• populate a storage bucket with data
• migrate AWS cloud data to Google Cloud
• list the steps involved in transferring large volumes of data to Google Cloud
• describe VM instance disk options
• configure VM instance disks
• describe characteristics of SQL and NoSQL databases
• use the GUI to deploy Google Cloud MySQL
• identify data lifecycle phases
• configure data retention settings

Cloud storage is easily and rapidly provisioned in Google Cloud. In this module storage types are discussed along with methods of getting data into the cloud.

Google Cloud Design

Google Cloud Design

• define how business continuity relates to Google Cloud
• define recovery time objective (RTO) and recovery point objective (RPO)
• recognize when to use HA configurations
• recognize when load balancing should be used
• plan for the use of Google Cloud backup and archiving solutions
• identify Google Cloud network components required by your organization
• identify Google Cloud storage components required by your organization
• identify Google Cloud virtual machines required by your organization
• determine which cloud migration strategy best fits a given scenario

Explore cloud design considerations related to storage, networking, and virtual machines, as well as high availability, load balancing, and cloud migration options.

Google Cloud Network Components

Google Cloud Network Components

• list Google Cloud network components
• explain the role that VPCs play in a cloud deployment
• use the web GUI to manage Google Cloud services
• deploy a Google cloud VPC with an automatic mode subnet
• deploy a Google cloud VPC with a manual mode subnet
• set aside an unchanging public IPv4 address for a VM instance
• set aside an unchanging public IPv6 address for a VM instance
• link two Google Cloud VPCs together
• use the Google Cloud console to add a new subnet
• explain how Google Cloud firewall rules are processed
• create the appropriate firewall rules to allow or deny network traffic

Cloud networking borrows from traditional on-premises network configurations. Explore and configure Google Cloud network components.

Google Cloud Programmatic Access

Google Cloud Programmatic Access

• describe how Google APIs and the SDK allow programmatic access
• install Google Cloud SDK components on a Windows computer
• list examples of how the Google Cloud Shell allows command line access to resource management
• use Google Cloud Shell to show VM instances
• describe the PowerShell syntax and use
• perform common administrative tasks using Google Cloud PowerShell cmdlets

Automation is the key to running repetitive management tasks. Explore Google APIs, PowerShell cmdlets and the gcloud command line tool.

Google Cloud Security

Google Cloud Security

• recognize how Google Data Centers are secured
• describe how Google Cloud complies with various security standards
• define the ways through which Google Cloud data is protected
• distinguish the difference between intrusion detection and intrusion prevention
• distinguish the difference between vulnerability and penetration testing
• configure firewall rules for Google App Engine
• conduct a Google App Engine security scan
• recall how Google Cloud activity can be tracked
• use the Google Cloud console to view audit data

Discover security concepts and how they apply to Google Cloud. Explore physical data center security, intrusion detection, security testing, and auditing.

Google Cloud Troubleshooting

Google Cloud Troubleshooting

• describe common troubleshooting steps
• solve Google Cloud VM disk boot problems
• solve Google Cloud network connectivity issues
• solve Google Cloud VPN problems
• solve Google Cloud permissions issues

Troubleshooting problems with deployed cloud services should be done with a structured approach. Discover a structured troubleshooting methodology for troubleshooting common problems.

Google Cloud Virtual Machine Configuration

Google Cloud Virtual Machine Configuration

• describe the benefits of VM instance snapshots
• use the Google Cloud console to create a disk snapshot
• use a snapshot as the source for VM deployment
• use the Google Cloud console to clone a VM
• recognize how VM images simplify deployment
• simplify multiple VM deployments with an image
• deploy a custom VM image
• use the Google Cloud console to create a custom VM template
• use a VM template as the source for deploying a new VM
• use Cloud Endure to migrate an on-premises VM to Google Cloud
• recognize the difference between managed and unmanaged VM instance groups
• configure settings within a VM instance group

There are differnet methods for deploying virtual machines (VMs) Discover virtual machine deployment methods such as snapshots and templates, and explore VM cloning and instance groups.

Google Cloud Virtual Machine Deployment

Google Cloud Virtual Machine Deployment

• describe VM instance configuration details
• use the GUI to deploy a Windows VM
• use RDP to remotely manage a Windows VM
• use the GUI to deploy a Linux VM
• use SSH to remotely manage a Linux VM

Discover Windows and Linux virtual machine (VM) deployment and explore how to connect to these VM instances for management purposes.

Google Cloud Web Applications and Name Resolution

Google Cloud Web Applications and Name Resolution

• recognize the role that Google Cloud App Engine plays
• deploy a Google Cloud web application
• describe the role DNS plays within a TCP/IP network
• deploy a Google Cloud DNS configuration
• describe how content delivery networks improve the end user experience
• use the Google Cloud Console to enable a content delivery configuration
• recognize how VPNs are used with cloud computing
• describe how Dedicated Interconnect provides a private network link to the Google Cloud

Explore web application, content delivery networks, and DNS name resolution will be covered. You'll also learn about cloud network connections through VPNs and dedicated links.

Monitoring & Logging

Monitoring & Logging

• describe the role that SLAs play in cloud computing
• recognize the importance of monitoring cloud resource management and usage
• enable Google Cloud resource monitoring using Stackdriver
• create a custom dashboard
• recognize the importance of log review
• use the Google Cloud console to view and export logs
• use the Google Cloud console to export a VM usage report
• configure Stackdriver alert policies

Cloud resource use must be monitored to ensure proper functionality and use. Examine monitoring, alerts, and notifications through Stackdriver as well as how to view and export logs.

APIs and Machine Learning

APIs and Machine Learning

• start the course
• define the cloud ML engine and its purpose with GCP
• describe the machine learning workflow and how the process works in various scenarios
• define the Vision API and its purpose
• describe the Natural Language API and its relevance in GCP
• recall Translation API and Speech API best practices
• demonstrate the use of various APIs
• work with the ML REST API using the Translation API
• use the Cloud Vision API with a Kubernetes Cluster
• describe the use of machine learning, including the common APIs

Continued Dataproc Operations

Continued Dataproc Operations

• start the course
• describe the various Spark and Hadoop processes that can be performed with Dataproc
• recognize the benefits of separating storage and compute services using Cloud Dataproc
• recall the process of monitoring and logging Dataproc jobs
• demonstrate the process of using an SSH tunnel to connect to the master and worker nodes in a cluster
• define the Spark REPL package and how it's used in Linux
• describe the compute and storage processes and the benefits of their separation and the virtualized distribution of Hadoop
• define BigQuery and its benefits for large-scale analytics
• describe the MapReduce programming model
• demonstrate the process of submitting multiple jobs with Dataproc
• recognize the various Dataproc and Cloud Shell job operations and implementations

Dataflow Autoscaling Pipelines

Dataflow Autoscaling Pipelines

• start the course
• define Apache Beam concepts and SDKs
• describe the Python SDK and its connection with data pipelines
• describe the Java SDK and its connection with data pipelines
• initialize Cloud Dataprep
• demonstrate how to ingest data into a pipeline
• create recipes in a Cloud Dataprep pipeline
• work with the import/export process and demonstrate how to run Dataflow jobs in Cloud Dataprep
• describe MapReduce and the benefits of Cloud Dataflow over MapReduce
• outline serverless architecture and some of the GCP products supporting data analytics
• describe the use of Apache Beam, Cloud Dataflow, and Cloud Dataprep in GCP to create and manage pipelines

Dataproc Architecture

Dataproc Architecture

• start the course
• describe how to create a cluster with the Dataproc CLI
• recognize implementations using the Dataproc REST API
• describe the various Dataproc architecture types in GCP and common use cases
• define Dataproc machine types and their uses
• configure a custom machine type
• describe how and when to execute Dataproc jobs
• recognize connections between Apache Hadoop HDFS and Cloud Storage
• describe the use of Pig and Hive
• configure and execute a job using Pig and Hive with Dataproc
• recall concepts of Dataproc jobs, including implementation of Pig and Hive

Deeper through GCP Analytics and Scaling

Deeper through GCP Analytics and Scaling

• start the course
• recognize the process for importing data into Cloud SQL
• work with Apache Hadoop and GCP to manage Hadoop clusters
• work with the cloud service to run Apache Spark clusters
• use SparkML for machine learning
• recognize best practices in big data analysis
• recall concepts related to the use of GCP Cloud Data Lab
• describe BigQuery and its benefits in GCP
• identify datasets with machine learning in GCP
• recognize machine learning concepts with TensorFlow operations
• use various machine learning operations to enhance data analysis in common use cases

Fundamentals of Big Query

Fundamentals of Big Query

• start the course
• Define BigQuery and its use in GCP
• recognize concepts in BigQuery datasets, tables, and views
• describe the process of executing queries
• describe how to load data into BigQuery
• describe how to export data into GCP BigQuery
• define the process of querying nested and repeated fields
• demonstrate query plans and complex queries using BigQuery
• describe the process of querying with multiple tables
• demonstrate how to write query results, use cached queries, use parameterized queries, and save and sharing queries in BigQuery
• recognize the main concepts and operations behind BigQuery

GCP Big Data and Security

GCP Big Data and Security

• start the course
• describe Cloud Spanner and its purpose in GCP
• describe Cloud Spanner replication and Instances
• describe Cloud Spanner schema, datatypes, and best practices
• describe GCP Bigtable
• specify the steps to design a Bigtable schema
• demonstrate the process of creating a Bigtable instance on GCP
• describe the cloud platform security model
• describe the use of Cloud Identity and Access Management
• describe the security layers on GCP
• define the security standards Google works to comply with
• describe the use of Big Data and how to keep data secure on GCP

GCP Engineering and Streaming Architecture

GCP Engineering and Streaming Architecture

• start the course
• describe the concepts of feature engineering
• recall the benefits of quality features with feature engineering and feature selection
• describe the process of input selection in feature engineering
• demonstrate feature engineering in use cases
• recall the concepts of streaming data and real-time stream processing
• describe Dataflow triggers and late data
• install Java JDK on Windows 10
• demonstrate how to install Apache Maven on Windows 10
• install Google Cloud SDK and initialize SDK Shell on Windows 10
• demonstrate the process of streaming pipelines using Dataflow SDK 2.x and Java in Cloud SDK Shell
• demonstrate the process of streaming pipelines using Dataflow SDK 2.x and Python in Google Cloud Shell
• describe feature engineering concepts and streaming data architecture

GCP Network Data Processing Models

GCP Network Data Processing Models

• start the course
• define the types of virtual networks and the benefits of each
• specify the process for creating a network
• recall the process for using TensorFlow with GPU
• describe the various machine learning APIs and their uses
• describe Dataflow and how it can be used to create data processing streams
• recognize differences between Pub and Sub message middleware and when to use them
• define the various pipelines for Dataflow processing
• demonstrate the process of creating Dataflow pipelines in GCP
• specify the differences between real-time and batch data processing
• recognize more concepts in analysis of data processing in GCP

Google Cloud Dataproc

Google Cloud Dataproc

• start the course
• recognize big data concepts and solutions using GCP
• define Cloud Dataproc and its benefits
• recall the various ways to access Dataproc
• describe the various areas of the dashboard and create a project
• recognize the process for creating a cluster in Dataproc
• recall the process for deleting a cluster using Dataproc
• define master and worker nodes in Dataproc
• describe custom machine types and preemptible worker nodes
• define the processes for identity and access management with permissions and IAM roles
• recognize the basic concepts of cluster management in Dataproc

Google Cloud Platform Fundamentals

Google Cloud Platform Fundamentals

• start the course
• describe GCP and its use with big data
• recognize the various services offered by GCP
• identify the benefits of GCP for data engineers with use case scenarios
• compare GCP with other models for data engineering
• perform the steps necessary to create a GCP account
• describe the Web Admin UI features of the Google Cloud Platform console
• demonstrate the steps in creating a project
• demonstrate a GCP process of using BigQuery to query a dataset
• recognize the various GCP data products and services

Google Cloud Platform Storage and Analytics

Google Cloud Platform Storage and Analytics

• start the course
• identify the purpose and benefits of using the Compute Engine and Cloud Storage
• recognize concepts in highly scalable VM management with Compute Engine
• recognize concepts in each storage option and when to use them
• demonstrate how to create and manage instances
• define concepts and features in Cloud Shell
• Activating and Using Google Cloud Shell
• demonstrate how to create, start, and delete a VM instance using 'gcloud beta compute' in Google Cloud Shell
• demonstrate how to create a Cloud Storage Bucket using 'gsutil mb' in Google Cloud Shell
• describe GCP concepts in data analytics
• recognize concepts in Cloud SQL
• identify the process in creating a MySQL database and uploading data using Cloud SQL
• identify the process in creating a PostgreSQL database and uploading data using Cloud SQL
• recognize GCP storage, compute, and analytics concepts and how they are tied together

Implementations with BigQuery for Big Data

Implementations with BigQuery for Big Data

• start the course
• describe initialization actions for creating Dataproc clusters
• specify how functions, operators, and data types are used with BigQuery
• define the steps in installing a storage connector
• recall the steps to load data with BigQuery
• identify the steps in exporting and updating data with BigQuery
• describe the various processes in using Google notebooks with Datalab
• recall the process for using Jupyter Notebooks with Apache Spark in Google Cloud Dataproc
• identify the storage options with Google Cloud Storage
• query data using Google Bigtable
• identify the various Google APIs
• describe the concepts of BigQuery and big data using Datalab

Machine Learning with TensorFlow and Cloud ML

Machine Learning with TensorFlow and Cloud ML

• start the course
• describe concepts of machine learning in relation to GCP
• describe the use of datasets in GCP
• demonstrate how to load a dataset for Cloud ML in GCP
• describe the use of TensorFlow with machine learning
• run a TensorFlow Python program in Google Cloud Shell
• use TensorFlow to run a local trainer
• demonstrate how to use TensorBoard to inspect TensorFlow logs and graphs
• run a local trainer in distributed mode
• demonstrate how to run a single-instance trainer in the cloud
• inspect Stackdriver logs for an ML Engine job run in the cloud
• describe the process of scaling with Cloud ML
• demonstrate how to run distributed training in the cloud
• use hyperparameter tuning to help maximize a model's predictive accuracy
• describe the TensorFlow operations that are used for big data

Streaming Pipelines and Analytics

Streaming Pipelines and Analytics

• start the course
• describe late data use in stream operations
• define watermarks, triggers, and accumulation as they relate to stream operations
• demonstrate the process of configuring Cloud Storage, BigQuery resources, and Cloud Pub/Sub in support of a streaming data pipeline
• configure Cloud Functions in support of a streaming data pipeline
• demonstrate the process of building a streaming data pipeline with Cloud Dataflow
• define the process of batch and stream operations
• describe concepts in streaming analytics in GCP
• specify the relationship between big data streaming and BigQuery
• describe how to use Google Data Studio
• demonstrate how to use Data Studio by building a dashboard
• describe how to use pipelines, PCollections, and transforms
• describe the architecture of stream analytics and how to use Google Data Studio