Content Pack data sheet

HPE Digital Learner Cloud for Architects (Intermediate) Content Pack

This self-paced eLearning Content Pack provides the required HPE Content Pack CP019 number training to help transition from a traditional IT architect role to a cloud architect role utilizing the Google public cloud Content Pack 22 Hours length environment. This is a comprehensive intermediate training Content Pack Category 2 series that includes many core areas required to plan, design category and implement modern cloud infrastructures, applications and Learn more View now data within the Google public cloud. This training is intended for IT personnel who need to rapidly uplift their IT/cloud skills as part of an ongoing business and operational strategy to migrate IT to the Google public cloud. Why HPE Education Services? •• IDC MarketScape leader 5 years running for IT education and training* Audience •• Recognized by IDC for leading with global coverage, unmatched technical • IT professionals, including managers, Google public cloud domain as part of an expertise, and targeted education engineers and developers, evaluating or ongoing hybrid cloud strategy. Areas of focus consulting services* implementing application environments on include Google cloud introductory •• Key partnerships with industry leaders fundamentals training as well as specific OpenStack®, VMware®, Linux®, Microsoft®, and detailed technology training around a ITIL, PMI, CSA, and SUSE • Data professionals responsible for provisioning and optimizing big data multitude of other relevant topics such as VM •• Complete continuum of training delivery management and configuration, containers, options—self-paced eLearning, custom solutions and data enthusiasts getting education consulting, traditional started with Google Cloud Platform compute and applications engine, networking, classroom, video on-demand instruction, storage and big data, analytics, GCP scaling, live virtual instructor-led with hands-on Content Pack objectives DataProc, APIs and machine learning, identity, lab, dedicated onsite training This Content Pack provides the information access control and security. This training will •• Simplified purchase option with help the student rapidly transition to a cloud HPE Training Credits necessary to transition a traditional IT architect into a cloud architect capable of working within architect role working within a typical Google a typical Google public cloud platform. This public cloud environment and will also assist training will introduce many critical baseline with the path to Google Certification. knowledge areas needed to transition to a

*Realize Technology Value with Training, IDC Infographic 2037, Sponsored by HPE, October 2017 Content Pack data sheet Page 2

Detailed Content Pack outline

Google Cloud •• Explain the value features of Google Cloud Platform •• Differentiate between Cloud Platform’s platform as a service Google Cloud Platform offers a wide array of powerful IaaS products and services (PaaS), software as a aservice (SaaS) and infrastructure as a and SaaS cloud solutions. In this course, the various service service (IaaS) •• Identify the Google Cloud Platform services features and options are explored as well as the necessary components, including Compute Engine, App •• Identify the features of Google Cloud Platform’s storage and steps for getting started with Google Cloud Platform with a Engine, Container Engine, Container Registry and database services Google email account. Cloud Functions •• Identify the features of Google Cloud Platform’s networking •• Identify the Google Cloud Platform storage services and database services components, including Cloud Storage, Cloud SQL, Cloud , Cloud •• Identify the features of Google Cloud Platform’s big data Datastore and Persistent Disk services •• Identify the Google Cloud Platform big data •• Manage platform projects, permissions and resources services components, including BigQuery, Cloud Dataflow, Cloud Dataproc, Cloud Datalab, Cloud •• Register for a free trial with Google Cloud Platform using a Pub/Sub and Genomics account •• Identify the Google Cloud Platform networking •• Create a project using the Google Cloud Platform Console services components, including cloud virtual network, cloud load balancing, cloud CDN, cloud •• Realize options for identity and access management interconnect and cloud DNS •• Deploy a LAMP stack using Google Cloud Launcher •• Define the concepts and components associated with the Google Cloud infrastructure and services •• Recognize different ways to interact with the Google Cloud Platform •• Define Google Cloud regions with examples •• Recognize the basic components, terms and features of •• Define Google Cloud zones with examples Google Cloud Platform

Google Cloud Virtual Machine Deployment •• Describe VM instance configuration details •• Use the GUI to deploy a Linux VM In this course, you will discover Windows and Linux virtual •• Use the GUI to deploy a Windows VM •• Use SSH to remotely manage a Linux VM machine (VM) deployment and explore how to connect to •• Use RDP to remotely manage a Windows VM these VM instances for management purposes.

Google Cloud Virtual Machine Configuration •• Describe VM instance configuration details •• Use the GUI to deploy a Linux VM Discover Windows and Linux virtual machine (VM) •• Use the GUI to deploy a Windows VM •• Use SSH to remotely manage a Linux VM deployment and explore how to connect to these VM instances for management purposes. •• Use RDP to remotely manage a Windows VM

Container, Compute and App Engine with Networking •• Describe Google’s Container Engine and •• Describe networks and route collection Services Kubernetes •• Describe IP forwarding offers virtual machines running •• Define the concept of a container registry •• Describe Network Address Translation gateways in Google’s innovative data centers and worldwide fiber •• Deploy an application by creating a container and network. In this course, you will learn about the fundamental using the Google Cloud SDK •• Describe network load balancing and HTTP load balancing aspects of the Google Cloud Platform Container Engine, •• Create an image and push it to the Container •• Identify the purpose and benefits of Compute Engine and App Engine as well as Google’s basic Registry •• Compare traditional builds with Google App Engine builds networking services. •• Use kubectl to deploy an application to a container •• Survey and define the various App Engine services •• Identify characteristics and purpose of Google Compute Engine Networking Service •• Compare the App Engine Standard and App Engine Flexible environments •• Describe the relationship between networks, instances and firewalls •• Deploy a Python application to the App Engine environment •• Create a network in the Web UI •• Identify GCP attributes of Compute, Container and App Engine as well as networking services •• Create IP addresses in the Web UI •• Create firewall rules in the Web UI

Google Cloud Network Components •• List Google Cloud network components •• Set aside an unchanging public IPv6 address for a VM instance Cloud networking borrows from traditional on-premises •• Explain the role that VPCs play in a cloud network configurations. In this course, you will explore and deployment •• Link two Google Cloud VPCs together configure Google Cloud network components. •• Use the web GUI to manage Google Cloud services •• Use the Google Cloud console to add a new subnet •• Deploy a Google cloud VPC with an automatic •• Explain how Google Cloud firewall rules are processed mode subnet •• Create the appropriate firewall rules to allow or deny network •• Deploy a Google cloud VPC with a manual mode traffic subnet •• Set aside an unchanging public IPv4 address for a VM instance Content Pack data sheet Page 3

Google Cloud Big Data and Machine Learning •• Identify the purpose and characteristics of Google •• View objects using the Cloud Storage Browser is unified object storage for Cloud Datastore • Identify considerations for deployment of Cloud Storage developers and enterprises, from live data serving to data • •• Define various datastore terms, including kind, required Applications analytics/ML to data archival. In this course, you will learn entity, property, keys and entity groups about the fundamentals of the Google Cloud Datastore and •• Identify characteristics and purpose of Google Cloud Big other storage options along with the basics of Big Data and •• Define development libraries, queries, and indexes Data and Machine Learning platforms Machine Learning with Google Cloud Platform. •• Deploy an App Engine application backed by •• Recognize loading a CSV File into a BigQuery Table Google Datastore •• Recognize querying data using the CLI •• Identify the features of Google Cloud Storage •• Recognize querying data using BigQuery Web UI •• Identify the features of Google Cloud SQL •• Recognize querying data using BigQuery Shell •• Identify the features of Google Cloud Bigtable •• Perform interactive queries using BigQuery •• Create a Google Cloud Storage bucket and use it to store images •• Review the basic features of datastore, storage options, big data and machine learning

Google Cloud Platform Fundamentals •• Describe GCP and its use with big data •• Describe the Web Admin UI features of the Google Cloud Google Cloud Platform offers several solutions to streamline Platform console • Recognize the various services offered by GCP any enterprise while keeping costs low. This course covers • •• Demonstrate the steps in creating a project these benefits, including how to navigate GCP and choose •• Identify the benefits of GCP for data engineers between the various data processing products it provides. with use case scenarios •• Demonstrate a GCP process of using BigQuery to query a dataset •• Compare GCP with other models for data engineering •• Recognize the various GCP data products and services •• Perform the steps necessary to create a GCP account

Google Cloud Platform Storage and Analytics •• Identify the purpose and benefits of using the •• Demonstrate how to create a Cloud Storage Bucket using Google Cloud Platform (GCP) offers several services for Compute Engine and Cloud Storage ‘gsutil mb’ in Google Cloud Shell analytics and storage. This course introduces the storage, •• Recognize concepts in highly scalable VM •• Describe GCP concepts in data analytics compute and analytics concepts and how they are tied management with Compute Engine together. •• Recognize concepts in Cloud SQL •• Recognize concepts in each storage option and when to use them •• Identify the process to create a MySQL database and upload data using Cloud SQL •• Demonstrate how to create and manage instances •• Identify the process to create a PostgreSQL database and •• Define concepts and features in Cloud Shell upload data using Cloud SQL •• Learn how to activate and use Google Cloud Shell •• Recognize GCP storage, compute and analytics concepts and how they are tied together •• Demonstrate how to create, start and delete a VM instance using ‘gcloud beta compute’ in Google Cloud Shell

Deeper through GCP Analytics and Scaling •• Recognize the process for importing data into •• Recall concepts related to the use of GCP Cloud Data Lab The big data industry is getting bigger and GCP has Cloud SQL • Describe BigQuery and its benefits in GCP several management tools designed for common use cases. • •• Work with Apache Hadoop and GCP to manage This course introduces common concepts and use cases, Hadoop clusters •• Identify datasets with machine learning in GCP including analytics tools and operations. •• Work with the cloud service to run Apache Spark •• Recognize machine learning concepts with TensorFlow clusters operations •• Use SparkML for machine learning •• Use various machine learning operations to enhance data analysis in common use cases •• Recognize best practices in big data analysis Content Pack data sheet Page 4

GCP Network Data Processing Models •• Define the types of virtual networks and the •• Recognize differences between Pub and Sub message You can create and manage an assortment of data processes benefits of each middleware and when to use them and network models using GCP. This course will go through •• Specify the process for creating a network •• Define the various pipelines for Dataflow processing the various types, including using a GPU and TensorFlow to create and manage GPU and instances. •• Recall the process for using TensorFlow with GPU •• Demonstrate the process of creating Dataflow pipelines in GCP •• Describe the various machine learning APIs and their uses •• Specify the differences between real-time and batch data processing •• Describe Dataflow and how it can be used to create data processing streams •• Recognize more concepts in analysis of data processing in GCP

Google Cloud Data Storage •• Recognize Google Cloud storage options •• Configure VM instance disks Cloud storage is easily and rapidly provisioned in Google • Configure a Google Cloud storage bucket • Describe characteristics of SQL and NoSQL databases Cloud. In this module, storage types are discussed along with • • methods of getting data into the cloud. •• Populate a storage bucket with data •• Use the GUI to deploy Google Cloud MySQL •• Migrate AWS cloud data to Google Cloud •• Identify data lifecycle phases •• List the steps involved in transferring large volumes •• Configure data retention settings of data to Google Cloud •• Describe VM instance disk options

Google Cloud Dataproc •• Recognize big data concepts and solutions using •• Recall the process for deleting a cluster using Dataproc GCP provides fully managed cloud services for running GCP • Define master and worker nodes in Dataproc Apache Spark and Hadoop. This course introduces the • •• Define Cloud Dataproc and its benefits concepts of cluster management with Dataproc, including •• Describe custom machine types and preemptible worker machine types and workers. •• Recall the various ways to access Dataproc nodes •• Describe the various areas of the dashboard and •• Define the processes for identity and access management create a project with permissions and IAM roles •• Recognize the process for creating a cluster in •• Recognize the basic concepts of cluster management in Dataproc Dataproc

Dataproc Architecture •• Describe how to create a cluster with the Dataproc •• Describe how and when to execute Dataproc jobs Dataproc can be used to perform several operations when CLI • Recognize connections between Apache Hadoop HDFS and integrating platforms, including Pig and Hive. This course will • •• Recognize implementations using the Dataproc Cloud Storage dig further into Dataproc architecture while introducing the REST API use of Pig and Hive. •• Describe the use of Pig and Hive •• Describe the various Dataproc architecture types in GCP and common use cases •• Configure and execute a job using Pig and Hive with Dataproc •• Define Dataproc machine types and their uses •• Recall concepts of Dataproc jobs, including implementation •• Configure a custom machine type of Pig and Hive

Continued Dataproc Operations •• Describe the various Spark and Hadoop processes •• Describe the compute and storage processes, the benefits of Executing Dataproc implementations with big data can that can be performed with Dataproc their separation and the virtualized distribution of Hadoop provide a variety of methods. This course will continue the •• Recognize the benefits of separating storage and •• Define BigQuery and its benefits for large-scale analytics study of Dataproc implementations with Spark and Hadoop compute services using Cloud Dataproc using the cloud shell and introduce BigQuery PySpark REPL •• Describe the MapReduce programming model package. •• Recall the process of monitoring and logging Dataproc jobs •• Demonstrate the process of submitting multiple jobs with Dataproc •• Demonstrate the process of using an SSH tunnel to connect to the master and worker nodes in a cluster •• Recognize the various Dataproc and Cloud Shell job operations and implementations •• Define the Spark REPL package and how it is used in Linux Content Pack data sheet Page 5

Implementations with BigQuery for Big Data •• Describe initialization actions for creating Dataproc •• Recall the process for using Jupyter Notebooks with Apache You can query big data using the BigQuery tool in the clusters Spark in Google Cloud Dataproc Google Cloud Platform. In this course, you will be introduced •• Specify how functions, operators and data types are •• Identify the storage options with Google Cloud Storage to the concepts of using BigQuery, including querying data used with BigQuery and using the Google API. •• Query data using Google Bigtable •• Define the steps in installing a storage connector •• Identify the various Google APIs •• Recall the steps to load data with BigQuery •• Describe the concepts of BigQuery and big data using •• Identify the steps in exporting and updating data Datalab with BigQuery •• Describe the various processes in using Google notebooks with Datalab

APIs and Machine Learning •• Define the cloud ML engine and its purpose with •• Demonstrate the use of various APIs Machine learning and ML APIs are part of the Cloud ML GCP • Work with the ML REST API using the Translation API engine. In this course, you will learn about the various ML • •• Describe the machine learning workflow and how APIs and how to implement an app that uses the Vision API. the process works in various scenarios •• Use the Cloud Vision API with a Kubernetes Cluster •• Define the Vision API and its purpose •• Describe the use of machine learning, including the common APIs •• Describe the Natural Language API and its relevance in GCP •• Recall Translation API and Speech API best practices

Cloud Identity Management •• Describe how user identities are used in the Google •• Enable Cloud Identity for centralized user and group In this course, you will discover how to create and manage Cloud management Google Cloud users and groups with Identity and Access •• Describe how on-premises Active Directory links up •• Enable multifactor authentication for Cloud Identity users Management. with a Google Cloud domain •• Identify how storage ACLs are managed •• Configure Google Cloud IAM users •• Set storage ACLs to meet business needs •• Describe how MFA enhances security •• Enable multifactor authentication for a single user

Monitoring and Logging •• Describe the role that SLAs play in cloud computing •• Recognize the importance of log review Cloud resource use must be monitored to ensure proper •• Recognize the importance of monitoring cloud •• Use the Google Cloud console to view and export logs functionality and use. In this course, you will examine resource management and usage monitoring, alerts and notifications through Stackdriver as •• Use the Google Cloud console to export a VM usage report well as how to view and export logs. •• Enable Google Cloud resource monitoring using Stackdriver •• Configure Stackdriver alert policies •• Create a custom dashboard

Google Cloud Troubleshooting •• Describe common troubleshooting steps •• Solve Google Cloud VPN problems Troubleshooting problems with deployed cloud services • Solve Google Cloud VM disk boot problems • Solve Google Cloud permissions issues should be done with a structured approach. In this course, • • you will discover a structured troubleshooting methodology •• Solve Google Cloud network connectivity issues for troubleshooting common problems.

Cloud Solution Management and Testing •• Describe the meaning of each SDLC phase •• Describe the purpose of regression testing Cloud solutions should follow a structured approach •• Recognize how ITIL provides efficient and cost •• Describe how unit testing uses a modular testing approach throughout their lifetime. In this course, you will explore effective IT service delivery SDLC phases, ITIL and various testing techniques. •• Specify how load and stress tests can identify capacity •• Recall how continuous integration and deployment weaknesses within a solution provide updates •• Describe how fuzz testing is used to ensure security and quality Content Pack data sheet Page 6

CLI Cloud Resource Management •• Describe how gcloud is used to manage cloud •• Use gcloud to manage firewall settings The gcloud and gsutil CLI tools can be used to deploy and resources • Use gsutil to create a cloud storage bucket manage Google Cloud resources and automate repetitive • •• Take a snapshot using gcloud administrative tasks. In this course, you will explore these •• Use gsutil to copy data into a bucket Google Cloud tools. •• Deploy a VM using gcloud •• Use gsutil to manage storage ACLs •• Use gcloud to delete a virtual machine instance •• Use gcloud to manage DNS

Google Cloud Programmatic Access •• Describe how Google APIs and the SDK allow •• Use Google Cloud Shell to show VM instances Automation is the key to running repetitive management programmatic access • Describe the PowerShell syntax and use tasks. In this course, you will explore Google APIs, PowerShell • •• Install Google Cloud SDK components on a cmdlets and the gcloud command line tool. Windows computer •• Perform common administrative tasks using Google Cloud PowerShell cmdlets •• List examples of how the Google Cloud Shell allows command line access to resource management

Google Cloud Web Applications and Name Resolution •• Recognize the role that Google Cloud App Engine •• Describe how content delivery networks improve the end In this course, you will explore web application, content plays user experience delivery networks and DNS name resolution. You will also learn about cloud network connections through VPNs •• Deploy a Google Cloud web application •• Use the Google Cloud Console to enable a content delivery configuration and dedicated links. •• Describe the role DNS plays within a TCP/IP network •• Recognize how VPNs are used with cloud computing •• Deploy a Google Cloud DNS configuration •• Describe how Dedicated Interconnect provides a private network link to the Google Cloud

GCP Engineering and Streaming Architecture •• Describe the concepts of feature engineering •• Install Java JDK on Windows 10 Feature engineering can be an essential tool in applied •• Recall the benefits of quality features with feature •• Demonstrate how to install Apache Maven on Windows 10 machine learning when enhancing a dataset. In this course, engineering and feature selection you will learn about concepts of feature engineering, including •• Install Google Cloud SDK and initialize SDK Shell on areas of streaming architecture and implementations. •• Describe the process of input selection in feature Windows 10 engineering •• Demonstrate the process of streaming pipelines using •• Demonstrate feature engineering in use cases Dataflow SDK 2.x and Java in Cloud SDK Shell •• Recall the concepts of streaming data and real-time •• Demonstrate the process of streaming pipelines using stream processing Dataflow SDK 2.x and Python in Google Cloud Shell •• Describe Dataflow triggers and late data •• Describe feature engineering concepts and streaming data architecture

GCP Big Data and Security •• Describe Cloud and its purpose in GCP •• Describe the cloud platform security model Complex operations require more managed and secure • Describe Cloud Spanner replication and instances • Describe the use of Cloud Identity and Access Management systems. This course explores the use of Cloud Spanner and • • Bigtable to create more complex data service configurations •• Describe Cloud Spanner schema, datatypes, and •• Describe the security layers on GCP and manage secure data infrastructure. best practices •• Define the security standards Google works to comply with •• Describe GCP Bigtable •• Describe the use of big data and how to keep data secure •• Specify the steps to design a Bigtable schema on GCP •• Demonstrate the process of creating a Bigtable instance on GCP Detailed Content Pack Page 7

Learn more at www.hpe.com/ww/digitallearner www.hpe.com/ww/digitallearner-contentpack

Follow us:

© Copyright 2019 Hewlett Packard Enterprise Development LP. The information contained herein is subject to change without notice. The only warranties for Hewlett Packard Enterprise products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. Hewlett Packard Enterprise shall not be liable for technical or editorial errors or omissions contained herein. Microsoft is either a registered trademark or trademark of Microsoft Corporation in the United States and/or other countries. The OpenStack Word Mark is either a registered trademark/service mark or trademark/service mark of the OpenStack Foundation, in the United States and other countries and is used with the OpenStack Foundation’s permission. We are not affiliated with, endorsed or sponsored by the OpenStack Foundation or the OpenStack community. Pivotal and Cloud Foundry are trademarks and/or registered trademarks of Pivotal Software, Inc. in the United States and/or other countries. Linux is the registered trademark of Linus Torvalds in the U.S. and other countries. VMware is a registered trademark or trademark of VMware, Inc. in the United States and/or other jurisdictions. CP019 A.00, January 2019