Fundación Tripartita
Subsidized in Spain by Fundación Tripartita These courses can be subsidized in Spain by Fundación Tripartita. Other countries may have other conditions, please contact us for details.
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Google Cloud Fundamentals: Core Infrastructure

Register for Google Cloud Fundamentals: Core Infrastructure

Objectives

This course teaches participants the following skills:

  • Identify the purpose and value of Google Cloud Platform products and services
  • Interact with Google Cloud Platform services
  • Describe ways in which customers have used Google Cloud Platform
  • Choose among and use application deployment environments on Google Cloud Platform: Google App Engine, Google Container Engine, and Google Compute Engine
  • Choose among and use Google Cloud Platform storage options: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore
  • Make basic use of BigQuery, Google’s managed data warehouse for analytics

Syllabus

  1. Introducing Google Cloud Platform
    • Explain the advantages of Google Cloud Platform
    • Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones
    • Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS)
  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
  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
  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
  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
  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
  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

From Data to Insights with Google Cloud Platform

Register for From Data to Insights with Google Cloud Platform

Objectives

This course teaches participants the following skills:

  • Derive insights from data using the analysis and visualization tools on Google Cloud Platform
  • Interactively query datasets using Google BigQuery
  • Load, clean, and transform data at scale
  • Visualize data using Google Data Studio and other third-party platforms
  • Distinguish between exploratory and explanatory analytics and when to use each approach
  • Explore new datasets and uncover hidden insights quickly and effectively
  • Optimizing data models and queries for price and performance

Syllabus

  1. Introduction to Data on the Google Cloud Platform
    • Highlight Analytics Challenges Faced by Data Analysts
    • Compare Big Data On-Premise vs on the Cloud
    • Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud
    • Navigate Google Cloud Platform Project Basics
    • Lab: Getting started with Google Cloud Platform
  2. Big Data Tools Overview
    • Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools
    • Demo: Analyze 10 Billion Records with Google BigQuery
    • Explore 9 Fundamental Google BigQuery Features
    • Compare GCP Tools for Analysts, Data Scientists, and Data Engineers
    • Lab: Exploring Datasets with Google BigQuery
  3. Exploring your Data with SQL
    • Compare Common Data Exploration Techniques
    • Learn How to Code High Quality Standard SQL
    • Explore Google BigQuery Public Datasets
    • Visualization Preview: Google Data Studio
    • Lab: Troubleshoot Common SQL Errors
  4. Google BigQuery Pricing
    • Walkthrough of a BigQuery Job
    • Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
    • Optimize Queries for Cost
    • Lab: Calculate Google BigQuery Pricing
  5. Cleaning and Transforming your Data
    • Examine the 5 Principles of Dataset Integrity
    • Characterize Dataset Shape and Skew
    • Clean and Transform Data using SQL
    • Clean and Transform Data using a new UI: Introducing Cloud Dataprep
    • Lab: Explore and Shape Data with Cloud Dataprep
  6. Storing and Exporting Data
    • Compare Permanent vs Temporary Tables
    • Save and Export Query Results
    • Performance Preview: Query Cache
    • Lab: Creating new Permanent Tables
  7. Ingesting New Datasets into Google BigQuery
    • Query from External Data Sources
    • Avoid Data Ingesting Pitfalls
    • Ingest New Data into Permanent Tables
    • Discuss Streaming Inserts
    • Lab: Ingesting and Querying New Datasets
  8. Data Visualization
    • Overview of Data Visualization Principles
    • Exploratory vs Explanatory Analysis Approaches
    • Demo: Google Data Studio UI
    • Connect Google Data Studio to Google BigQuery
    • Lab: Exploring a Dataset in Google Data Studio
  9. Joining and Merging Datasets
    • Merge Historical Data Tables with UNION
    • Introduce Table Wildcards for Easy Merges
    • Review Data Schemas: Linking Data Across Multiple Tables
    • Walkthrough JOIN Examples and Pitfalls
    • Lab: Join and Union Data from Multiple Tables
  10. Advanced Functions and Clauses
    • Review SQL Case Statements
    • Introduce Analytical Window Functions
    • Safeguard Data with One-Way Field Encryption
    • Discuss Effective Sub-query and CTE design
    • Compare SQL and Javascript UDFs
    • Lab: Deriving Insights with Advanced SQL Functions
  11. Schema Design and Nested Data Structures
    • Compare Google BigQuery vs Traditional RDBMS Data Architecture
    • Normalization vs Denormalization: Performance Tradeoffs
    • Schema Review: The Good, The Bad, and The Ugly
    • Arrays and Nested Data in Google BigQuery
    • Lab: Querying Nested and Repeated Data
  12. More Visualization with Google Data Studio
    • Create Case Statements and Calculated Fields
    • Avoid Performance Pitfalls with Cache considerations
    • Share Dashboards and Discuss Data Access considerations
  13. Optimizing for Performance
    • Avoid Google BigQuery Performance Pitfalls
    • Prevent Hotspots in your Data
    • Diagnose Performance Issues with the Query Explanation map
    • Lab: Optimizing and Troubleshooting Query Performance
  14. Advanced Insights
    • Introducing Cloud Datalab
    • Cloud Datalab Notebooks and Cells
    • Benefits of Cloud Datalab
  15. Data Access
    • Compare IAM and BigQuery Dataset Roles
    • Avoid Access Pitfalls
    • Review Members, Roles, Organizations, Account Administration, and Service Accounts

Google Cloud Fundamentals: Big Data & Machine Learning

Register for Google Cloud Fundamentals: Big Data & Machine Learning

Objectives

This course teaches participants the following skills:

  • 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
  • Train and use a neural network using TensorFlow
  • Employ ML APIs
  • Choose between different data processing products on the Google Cloud Platform

Syllabus

  1. Introducing Google Cloud Platform
    • Google Platform Fundamentals Overview
    • Google Cloud Platform Data Products and Technology
    • Usage scenarios
    • Lab: Sign up for Google Cloud Platform
  2. Compute and Storage Fundamentals
    • CPUs on demand (Compute Engine)
    • A global filesystem (Cloud Storage)
    • CloudShell
    • Lab: Set up a Ingest-Transform-Publish data processing pipeline
  3. Data Analytics on the Cloud
    • Stepping-stones to the cloud
    • CloudSQL: your SQL database on the cloud
    • Lab: Importing data into CloudSQL and running queries
    • Spark on Dataproc
    • Lab: Machine Learning Recommendations with SparkML
  4. Scaling data analysis
    • Fast random access
    • Datalab
    • BigQuery
    • Lab: Build machine learning dataset
    • Machine Learning with TensorFlow
    • Lab: Train and use neural network
    • Fully built models for common needs
    • Lab: Employ ML APIs
  5. Data processing architectures
    • Message-oriented architectures with Pub/Sub
    • Creating pipelines with Dataflow
    • Reference architecture for real-time and batch data processing
  6. Summary
    • Why GCP?
    • Where to go from here
    • Additional Resources

Data Engineering on Google Cloud Platform

Register for Data Engineering on Google Cloud Platform

Objectives

This course teaches participants the following skills:

  • Design and build data processing systems on Google Cloud Platform
  • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
  • Derive business insights from extremely large datasets using Google BigQuery
  • Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
  • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
  • Enable instant insights from streaming data

Syllabus

  1. Google Cloud Dataproc Overview
    • Creating and managing clusters
    • Leveraging custom machine types and preemptible worker nodes
    • Scaling and deleting Clusters
    • Lab: Creating Hadoop Clusters with Google Cloud Dataproc
  2. Running Dataproc Jobs
    • Running Pig and Hive jobs
    • Separation of storage and compute
    • Lab: Running Hadoop and Spark Jobs with Dataproc
    • Lab: Submit and monitor jobs
  3. Integrating Dataproc with Google Cloud Platform
    • Customize cluster with initialization actions
    • BigQuery Support
    • Lab: Leveraging Google Cloud Platform Services
  4. Making Sense of Unstructured Data with Google’s Machine Learning APIs
    • Google’s Machine Learning APIs
    • Common ML Use Cases
    • Invoking ML APIs
    • Lab: Adding Machine Learning Capabilities to Big Data Analysis
  5. Serverless data analysis with BigQuery
    • What is BigQuery
    • Queries and Functions
    • Lab: Writing queries in BigQuery
    • Loading data into BigQuery
    • Exporting data from BigQuery
    • Lab: Loading and exporting data
    • Nested and repeated fields
    • Querying multiple tables
    • Lab: Complex queries
    • Performance and pricing
  6. Serverless, autoscaling data pipelines with Dataflow
    • The Beam programming model
    • Data pipelines in Beam Python
    • Data pipelines in Beam Java
    • Lab: Writing a Dataflow pipeline
    • Scalable Big Data processing using Beam
    • Lab: MapReduce in Dataflow
    • Incorporating additional data
    • Lab: Side inputs
    • Handling stream data
    • GCP Reference architecture
  7. Getting started with Machine Learning
    • What is machine learning (ML)
    • Effective ML: concepts, types
    • ML datasets: generalization
    • Lab: Explore and create ML datasets
  8. Building ML models with Tensorflow
    • Getting started with TensorFlow
    • Lab: Using tf.learn
    • TensorFlow graphs and loops
    • Lab: Using low-level TensorFlow + early stopping
    • Monitoring ML training
    • Lab: Charts and graphs of TensorFlow training
  9. Scaling ML models with CloudML
    • Why Cloud ML?
    • Packaging up a TensorFlow model
    • End-to-end training
    • Lab: Run a ML model locally and on cloud
  10. Feature Engineering
    • Creating good features
    • Transforming inputs
    • Synthetic features
    • Preprocessing with Cloud ML
    • Lab: Feature engineering
  11. Architecture of streaming analytics pipelines
    • Stream data processing: Challenges
    • Handling variable data volumes
    • Dealing with unordered/late data
    • Lab: Designing streaming pipeline
  12. Ingesting Variable Volumes
    • What is Cloud Pub/Sub?
    • How it works: Topics and Subscriptions
    • Lab: Simulator
  13. Implementing streaming pipelines
    • Challenges in stream processing
    • Handle late data: watermarks, triggers, accumulation
    • Lab: Stream data processing pipeline for live traffic data
  14. Streaming analytics and dashboards
    • Streaming analytics: from data to decisions
    • Querying streaming data with BigQuery
    • What is Google Data Studio?
    • Lab: build a real-time dashboard to visualize processed data
  15. High throughput and low-latency with Bigtable
    • What is Cloud Spanner?
    • Designing Bigtable schema
    • Ingesting into Bigtable
    • Lab: streaming into Bigtable

Architecting with Google Cloud Platform: Infrastructure

Register for Architecting with Google Cloud Platform: Infrastructure

Objectives

This course teaches participants the following skills:

  • Consider the entire range of Google Cloud Platform technologies in their plans.
  • Learn methods to develop, implement, and deploy solutions.
  • Distinguish between features of similar or related products and technologies.
  • Recognize a wide variety of solution domains, use cases, and applications.
  • Develop essential skills for managing and administering solutions.
  • Develop knowledge of solution patterns -- methods, technologies, and designs that are used to implement security, scalability, high availability, and other desired qualities.

Syllabus

  1. Introduction to Google Cloud Platform
    • Role of the Cloud Architect
    • Learn about Solution Domains as an approach to design
    • Lab: Console and Cloud Shell
    • Lab: Projects
    • Lab: Infrastructure Preview
  2. Virtual Networks
    • Cloud Virtual Networks (CVN), Projects, Networks, Subnetworks, IP addresses, Routes, Firewall rules
    • Subnetworks for resource management instead of physical network topology
    • Lab: Virtual Networking
    • Lab: Bastion Host
  3. Virtual Machines
    • GCE, tags, VM options, vCPUs, disk options, images, and special features of persistent disks for VMs
    • Lab: Creating Virtual Machines
    • Lab: Working with Virtual Machines
  4. Cloud IAM
    • Members, roles, organizations, account administration, service accounts
    • Lab: Cloud IAM
  5. Data Storage Services
    • Cloud Storage, Datastore, Bigtable, Cloud SQL
    • Lab: Cloud Storage
    • Lab: Cloud SQL
    • Lab: Cloud Datastore
  6. Resource Management
    • Billing, Quotas, Labels, Names, Cloud Resource Manager
    • Lab: Billing Administration
    • Lab: Examining Billing Data with BigQuery
  7. Resource Monitoring
    • Stackdriver, Monitoring, Logging, Error Reporting, Tracing, Debugging
    • Lab: Resource Monitoring (Stackdriver)
    • Lab: Error Reporting and Debugging (Stackdriver)
  8. Interconnecting Networks
    • VPNs, Cloud Router, Cloud Interconnect, Direct Peering, Cloud DNS
    • Lab: Virtual Private Networks (VPN)
    • Lab: Dynamic VPN with Cloud Routers
  9. Load Balancing
    • Network Load Balancing, HTTP(S) Load Balancing, SSL Load Balancing and Internal Load Balancing, Managed Instance Groups
    • Lab: VM Automation and Load Balancing
  10. Autoscaling
    • Autoscaler
    • Lab: Autoscaling
  11. Infrastructure Automation with Cloud API
    • Infrastructure automation, custom images, startup and shutdown scripts and metadata
    • Lab: Cloud API Infrastructure Automation
  12. Infrastructure Automation with Deployment Manager
    • Deployment Manager templates
    • Lab: Deployment Manager
  13. Managed Services
    • Dataproc, Dataflow, BigQuery, Datalab
  14. Application Infrastructure Services
    • Cloud Pub/Sub, Cloud Endpoints and Apigee, Cloud Functions
  15. Application Development Services
    • Google App Engine (GAE), Dev Tools, Cloud Source Repos
    • Lab: App Engine Development
  16. Containers
    • Containers, Google Container Engine (GKE), and Container Registry
    • Lab: Kubernetes Load Balancing

Architecting with Google Cloud Platform: Design and Process

Register for Architecting with Google Cloud Platform: Design and Process

Objectives

This course teaches participants the following skills:

  • Design for high availability, scalability, and maintainability.
  • Assess tradeoffs and make sound choices among Google Cloud Platform products.
  • Integrate on-premises and cloud resources.
  • Identify ways to optimize resources and minimize cost.
  • Implement processes that minimize downtime, such as monitoring and alarming, unit and integration testing, production resilience testing, and incident post-mortem analysis.
  • Implement policies that minimize security risks, such as auditing, separation of duties and least privilege.
  • Implement technologies and processes that assure business continuity in the event of a disaster.

Syllabus

  1. Defining the Service
    • Design in this class.
    • State and solution.
    • Measurement.
    • Gathering requirements, SLOs, SLAs, and SLIs (key performance indicators).
  2. Business-logic layer design
    • Microservices architecture.
    • GCP 12-factor support.
    • Mapping compute needs to Google Cloud Platform processing services.
    • Compute system provisioning.
  3. Data layer design
    • Classifying and characterizing data.
    • Data ingest and data migration.
    • Identification of storage needs and mapping to Google Cloud Platform storage systems.
  4. Presentation layer design
    • Network edge configuration.
    • Network configuration for data transfer within the service, including load balancing and network location.
    • Network integration with other environments, including on premise and multi-cloud.
  5. Design for resiliency, scalability, and disaster recovery
    • Failure due to loss of resources.
    • Failure due to overload.
    • Strategies for coping with failure.
    • Business continuity and disaster recovery, including restore strategy and data lifecycle management.
    • Scalable and resilient design.
  6. Design for security
    • Google Cloud Platform security.
    • Network access control and firewalls.
    • Protections against denial of service.
    • Resource sharing and isolation.
    • Data encryption and key management.
    • Identity access and auditing.
  7. Capacity planning and cost optimization
    • Capacity planning.
    • Pricing.
  8. Deployment, monitoring and alerting, and incident response
    • Deployment.
    • Monitoring and alerting.
    • Incident response.

Developing Applications with Google Cloud Platform

Register for Developing Applications with Google Cloud Platform

Objectives

This course teaches participants the following skills:

  • Use best practices for application development.
  • Choose the appropriate data storage option for application data.
  • Implement federated identity management.
  • Develop loosely coupled application components or microservices.
  • Integrate application components and data sources.
  • Debug, trace, and monitor applications.
  • Perform repeatable deployments with containers and deployment services.
  • Choose the appropriate application runtime environment; use Google Container Engine as a runtime environment and later switch to a no-ops solution with Google App Engine Flex.

Syllabus

  1. Best Practices for Application Development
    • Code and environment management
    • Design and development of secure, scalable, reliable, loosely coupled application components and microservices
    • Continuous integration and delivery
    • Re-architecting applications for the cloud
  2. Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK
    • How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK
    • Lab: Set up Google Client Libraries, Google Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials
  3. Overview of Data Storage Options
    • Overview of options to store application data
    • Use cases for Google Cloud Storage, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner
  4. Best Practices for Using Google Cloud Datastore
    • Queries
    • Built-in and composite indexes
    • Inserting and deleting data (batch operations)
    • Transactions
    • Error handling
    • Bulk-loading data into Cloud Datastore by using Google Cloud Dataflow
    • Lab: Store application data in Cloud Datastore
  5. Performing Operations on Buckets and Objects
    • Operations that can be performed on buckets and objects
    • Consistency model
    • Error handling
  6. Best Practices for Using Google Cloud Storage
    • Naming buckets for static websites and other uses
    • Naming objects (from an access distribution perspective)
    • Performance considerations
    • Setting up and debugging a CORS configuration on a bucket
    • Lab: Store files in Cloud Storage
  7. Handling Authentication and Authorization
    • Cloud Identity and Access Management (IAM) roles and service accounts
    • User authentication by using Firebase Authentication
    • User authentication and authorization by using Cloud Identity-Aware Proxy
    • Lab: Authenticate users by using Firebase Authentication
  8. Using Google Cloud Pub/Sub to Integrate Components of Your Application
    • Topics, publishers, and subscribers
    • Pull and push subscriptions
    • Use cases for Cloud Pub/Sub
    • Lab: Develop a backend service to process messages in a message queue
  9. Adding Intelligence to Your Application
    • Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API
  10. Using Google Cloud Functions for Event-Driven Processing
    • Key concepts such as triggers, background functions, HTTP functions
    • Use cases
    • Developing and deploying functions
    • Logging, error reporting, and monitoring
  11. Managing APIs with Google Cloud Endpoints
    • Open API deployment configuration
    • Lab: Deploy an API for your application
  12. Deploying an Application by Using Google Cloud Container Builder, Google Cloud Container Registry, and Google Cloud Deployment Manager
    • Creating and storing container images
    • Repeatable deployments with deployment configuration and templates
    • Lab: Use Deployment Manager to deploy a web application into Google App Engine flexible environment test and production environments
  13. Execution Environments for Your Application
    • Google Compute Engine
    • Container Engine
    • App Engine flexible environment
    • Cloud Functions
    • Cloud Dataflow
    • Lab: Deploying your application on App Engine flexible environment
  14. Debugging, Monitoring, and Tuning Performance by Using Google Stackdriver
    • Stackdriver Debugger
    • Stackdriver Error Reporting
    • Lab: Debugging an application error by using Stackdriver Debugger and Error Reporting
    • Stackdriver Logging
    • Key concepts related to Stackdriver Trace and Stackdriver Monitoring.
    • Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance

Google Cloud Platform Fundamentals for AWS Professionals

Register for Google Cloud Platform Fundamentals for AWS Professionals

Objectives

This course teaches participants the following skills:

  • Identify GCP counterparts for Amazon VPC, subnets, routes, NACLs, IGW, Amazon EC2, Amazon EBS, auto-scaling, Elastic Load Balancing, Amazon S3, Amazon Glacier, Amazon RDS, Amazon Redshift, AWS IAM, and more.
  • Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing, storage, databases, IAM, and more.
  • Manage and monitor applications.
  • Explain feature and pricing model differences.
  • Locate documentation and training.

Syllabus

  1. Introducing Google Cloud Platform
    • Google Cloud infrastructure.
    • AWS regions, availability zones, and CloudFront.
    • GCP regions, zones, edge caching, and Cloud CDN.
    • GCP services.
  2. Setting up Accounts and Billing
    • AWS accounts, billing, and IAM roles.
    • GCP accounts, billing accounts, projects, and admin setup.
    • Account, billing, project, and admin setup.
    • Lab: Set up projects and billing accounts with a free-trial GCP account.
  3. Networking
    • Amazon VPC, subnets, routes, NACLs, and security groups.
    • GCP networks, subnets, routes, and firewall rules.
    • VMs in networks.
    • Lab: Add VMs, explore the default network, and test connectivity.
  4. Working with VM Instances
    • Amazon EC2 instance types, AMIs, Amazon EBS, ephemeral drives, spot instances.
    • Google Compute Engine machine types, instances, persistent disks, local SSDs, preemptible VMs.
    • VM and web app deployment.
    • Lab: Deploy VMs with an app by console and command line.
  5. Scaling and Load Balancing Apps
    • Amazon EC2 launch configurations, auto-scaling groups, load balancing.
    • Google Compute Engine instance templates, managed instance groups, load balancing.
    • Autoscaling and load balancing setup.
    • Lab: Scale and load balance instances, and test under load.
  6. Isolating Instances and Apps
    • A 3-tier web app in GCP.
    • A custom network with custom subnets and firewall rules.
    • Lab: Build a 3-tier web app with public front-end and private backend.
  7. Using Storage as a Service and Database as a Service
    • Amazon S3, Amazon Glacier, Amazon RDS, Amazon DynamoDB, Amazon Redshift, Amazon Athena.
    • Google Cloud Storage, Google Cloud SQL, Cloud Spanner, Google Cloud Datastore, Google Cloud Bigtable, Google BigQuery.
    • Lab: Use gsutil command-line tool to perform operations on buckets and objects in Cloud Storage.
    • Lab: Load and analyze data in BigQuery.
  8. Deployment and Monitoring
    • AWS CloudFormation, Amazon CloudWatch.
    • Google Cloud Deployment Manager, Google StackDriver.
    • Lab: Deploy your infrastructure using Deployment Manager.

Getting Started with Kubernetes and Google Container Engine

Register for Getting Started with Kubernetes and Google Container Engine

Objectives

This course teaches participants the following skills:

  • Understand container basics.
  • Containerize an existing application.
  • Understand Kubernetes concepts and principles.
  • Deploy applications to Kubernetes using the CLI.
  • Set up a continuous delivery pipeline using Jenkins.

Syllabus

  1. Introduction to Containers and Docker
    • Create a container.
    • Package a container using Docker.
    • Store a container image in Google Container Registry.
    • Launch a Docker container.
  2. Kubernetes and Google Container Engine Basics
    • Provision a complete Kubernetes cluster using Google Container Engine.
    • Deploy and manage Docker containers using kubectl.
    • Break an application into microservices using Kubernetes’ Deployments and Services.
  3. Deploying to Kubernetes
    • Create a Kubernetes deployment.
    • Trigger, pause, resume, and rollback updates.
    • Understand and build canary deployments.
  4. Continuous Deployment with Jenkins
    • Provision Jenkins in your Kubernetes cluster.
    • Create a Jenkins pipeline.
    • Implement a canary deployment using Jenkins.

Serverless Data Analysis with BigQuery and Cloud Dataflow (CPB101)

Register for Serverless Data Analysis with BigQuery and Cloud Dataflow (CPB101)

Objectives

At the end of this course, participants will be able to:

  • 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 transforms in Dataflow pipelines.
  • Join datasets as side inputs
  • Interoperate Dataflow, BigQuery and Cloud Pub/Sub for real-time streaming

Syllabus

  1. Serverless data analysis with BigQuery
    • What is BigQuery?
    • Queries and functions
    • Load and export data
    • Advanced Capabilities
    • Performance and pricing
  2. Serverless, autoscaling data pipelines with Dataflow
    • What is Dataflow?
    • Data pipeline
    • MapReduce in Dataflow
    • Side inputs
    • Streaming

Machine Learning with Cloud ML (CPB102)

Register for Machine Learning with Cloud ML (CPB102)

Objectives

At the end of this course, participants will be able to:

  • 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
  • Employ ML APIs
  • Productionize trained ML model

Syllabus

  1. Getting Started with Machine Learning
    • What is ML?
    • Effective ML
    • Evaluating ML
    • ML datasets: generalization
  2. Building ML models with TensorFlow
    • Getting started
    • TensorFlow graphs and loops
    • Monitoring
  3. Scaling ML models with Cloud ML
    • Why Cloud ML?
    • Packaging up a TensorFlow model
    • End-to-end training
  4. Feature Engineering
    • Creating good features
    • Transforming inputs
    • Synthetic features
    • Preprocessing with Cloud ML
  5. ML architectures
    • Wide and deep
    • Image analysis
    • Embeddings and sequences
    • Recommendation systems

Google Cloud Platform for Architects (CPA200)

Register for Google Cloud Platform for Architects (CPA200)

Objectives

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

Syllabus

  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
  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
  3. Embrace Failure
    • Decoupling
    • Self healing
    • Testing
  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
  5. Architectural patterns using Google Cloud Platform
    • Cloud Deployment manager
    • Image processing
    • Mobile applications
    • Big Data
    • Virtual network environments

Google BigQuery for Data Analysts (CPB200)

Register for Google BigQuery for Data Analysts (CPB200)

Objectives

At the end of this 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 command-line 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

Syllabus

  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
  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
  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
  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
  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
  6. Clauses and Functions
    • Explain the differences between BigQuery SQL and ANSI SQL
    • Identify the purpose of and use cases for user-defined functions
    • Explain the purpose of various BigQuery functions
  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
  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
  9. Troubleshooting Errors
    • Describe how to handle the most common BigQuery errors: request encoding errors, resource errors, and HTTP errors
  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 row-level security
  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
  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
  13. Working with Google Analytics Premium Data
    • Describe the schema of the Google Analytics Premium and AdSense data exported to BigQuery
  14. Data Visualization
    • Describe the options available for visualizing BigQuery data

Developing Solutions with Google Cloud Platform (CPD200)

Register for Developing Solutions with Google Cloud Platform (CPD200)

Objectives

At the end of this course, participants will be able to:

  • Build scalable and reliable applications using Google App Engine Standard Environment.
  • Leverage Google Cloud Endpoints to implement, deploy, and manage API backends.
  • Create microservice-based applications using App Engine services.
  • Manage application security, versioning, deployment, and monitoring.
  • Store application data, optimize query performance, and use transactions in Google Cloud Datastore.
  • Provide improved performance and capacity with Memcache and instance scaling.

Syllabus

  1. Developing Solutions with Google Cloud Platform
    • Benefits of Google Cloud Platform
    • Development tools and services for Google Cloud Platform
    • Google Cloud Platform solution architectures
    • Lab: Google Cloud Source Repositories
  2. Google Cloud Endpoints
    • Cloud Endpoints features
    • Developing APIs using Cloud Endpoints
    • Accessing Cloud Endpoints APIs using JavaScript clients
    • Lab: Google Cloud Endpoints
  3. App Engine Services
    • Modular application design and App Engine services
    • Deploying services
    • Accessing App Engine services
    • Lab: Google App Engine Services
  4. User Authentication and Credentials
    • Authentication and authorization concepts
    • Securing access through application configuration
    • Authentication with the Users service
    • Authorization with API keys, OAuth, and application default credentials
    • Lab: User Authentication
  5. Managing App Engine Applications
    • Deploying and managing multiple application versions
    • Traffic splitting, incremental rollouts, and canary releases
    • Budgets and quotas
    • Stackdriver logging and application tracing
    • Lab: Managing Google App Engine Applications
  6. Storage for Solution Developers
    • Functionality and benefits of Cloud Platform storage options
    • Using Google Cloud Storage for immutable BLOB storage
    • Integrating Google Cloud SQL into App Engine Apps
    • Cloud Datastore fundamentals
    • Lab: Google Cloud Datastore
  7. Queries and Indexes
    • Implementing query filters with Cloud Datastore
    • Single-property and composite indexes
    • Configuring and optimizing indexes
    • Lab: Google Cloud Datastore Queries and Indexes
  8. Entity Groups, Consistency, and Transactions
    • Strong and eventual consistency in Cloud Datastore
    • Ensuring strongly consistent queries
    • Best practices for Cloud Datastore transactions
    • Lab: Google Cloud Datastore Transactions
  9. App Engine Performance and Optimization
    • Memcache use cases and implementation patterns
    • Manual, basic, and automatic scaling behavior
    • Configuring application scaling
    • Lab: Google App Engine Performance and Optimization
  10. Task Queues
    • Push and pull queue capabilities and configuration
    • Adding and consuming tasks with push and pull queues
    • Scheduling tasks with the Cron Service
    • Lab: Task Queue API
    • Lab: Deleting Google Cloud Platform Projects and Resources

Google Cloud Platform for Systems Operations Professionals (CPO200)

Register for Google Cloud Platform for Systems Operations Professionals (CPO200)

Objectives

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

Syllabus

  1. Google Cloud Platform Projects
    • Identify project resources and quotas
    • Explain the purpose of Google Cloud Resource Manager and Identity and Access Management
  2. Instances
    • Explain how to create and move instances
    • Identify how to connect to and manage instances
  3. Networks
    • Explain how to create and manage networks in projects
    • Identify how to create and manage firewall rules, routes, and IP addresses
  4. Disks and Images
    • Explain how to create and manage persistent disks
    • Identify how to create and manage disk images
  5. Authorization
    • Explain the purposes of and use cases for Google Compute Engine service accounts
    • Identify the types of service account scopes
  6. Snapshots
    • Identify the purpose of and use cases for disk snapshots
    • Explain the process of creating a snapshot
  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
  8. Instance Groups
    • Identify the purpose of and use cases for instance groups
    • Explain the process of creating and using instance groups
  9. Google Cloud SQL
    • Understand how to create and administer Cloud SQL instances
    • Explain how to access Cloud SQL instances from Compute Engine instances
  10. Metadata
    • Explain the purpose of metadata and identify the use cases for project and instance metadata
    • Identify how to set and query metadata
  11. Startup and Shutdown Scripts
    • Identify the purpose of and use cases for startup and shutdown scripts
  12. Autoscaling
    • Explain the use cases for autoscaling and how autoscaling functions
    • Identify the purpose of autoscaling policies
  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

Spring Core Official Course

Register for Spring Core Official Course

Objectives

At the end of the training, you should have an understanding of Spring and associated technologies and be able to do the following:

  • Use the Spring Framework to develop Java applications
  • Use dependency injection to set up and configure applications
  • Test Spring-based applications
  • Set up Spring configuration using XML, annotations and Java configuration
  • Use Spring Data JPA and JDBC to rapidly implement relational database access
  • Use Spring support for transactions
  • Use aspect-oriented programming (AOP) to add behavior to objects
  • Develop a basic Web application with Spring MVC
  • Use Spring Security to secure Web applications
  • Use Spring to easily build REST web services
  • Take the Spring Boot shortcut to productivity
  • Start the journey to Microservices and Cloud Native Applications

Syllabus

  1. Introduction to Spring
    • Java configuration and the Spring application context
    • @Configuration and @Bean annotations
    • @Import: working with multiple configuration files
    • Launching a Spring Application and obtaining Beans
  2. Spring Java configuration: A deeper look
    • External properties & Property sources
    • Environment abstraction
    • Bean scope, bean profiles
    • Spring Expression Language (SpEL)
    • How it Works: Inheritance based proxies
  3. Annotation-based dependency injection
    • Autowiring and component scanning
    • Java configuration versus annotations, mixing.
    • Lifecycle annotations: @PostConstruct and @PreDestroy
    • Stereotypes and meta-annotations
  4. XML dependency injection
    • XML syntax, constructor & setter injection
    • Resource prefixes
    • Namespaces and best practices when using XML
    • XML profile selection
  5. The bean lifecycle: How does Spring work internally?
    • The init phase: available interceptors
    • The init phase: what is the difference between XML, annotations and Java configuration?
    • The use and destruction phases
  6. Testing a Spring-based application
    • Spring and Test Driven Development
    • @ContextConfiguration and @RunWith annotations
    • Application context caching and the @DirtiesContext annotation
    • Profile selection with @ActiveProfiles
    • Easy test data setup with @Sql
  7. Aspect-oriented programming
    • What problems does AOP solve?
    • Differences between Spring AOP and AspectJ
    • Defining pointcut expressions
    • Implementing an advice: @Around, @Before, @After
  8. Data access and JDBC with Spring
    • How Spring integrates with existing data access technologies
    • DataAccessException hierarchy
    • Implementing caching using @Cacheable
    • jdbc namespace and Spring‘s JdbcTemplate
  9. Database transactions with Spring
    • @Transactional annotation
    • Transactions configuration: XML versus annotations
    • Isolation levels, transaction propagation and rollback rules
    • Transactions and integration testing
    • Should you use read-only transactions?
  10. JPA with Spring and Spring Data
    • Quick introduction to ORM with JPA
    • Benefits of using Spring with JPA
    • JPA configuration in Spring
    • Spring Data JPA dynamic repositories
  11. Spring in a Web application
    • Configuring Spring in a Web application
    • Introduction to Spring MVC, required configuration
    • Controller method signatures
    • Views and ViewResolvers
    • Using @Controller and @RequestMapping annotations
  12. Spring Boot
    • Using Spring Boot to bypass most configuration
    • Simplified dependency management with starter POMs
    • Packaging options, JAR or WAR
    • Easily overriding Spring Boot defaults
  13. Spring Boot - Going further
    • Going beyond the default settings
    • Customizing Spring Boot configuration
    • Logging control
    • Configuration properties using YAML
    • Boot-driven testing
  14. Spring Security
    • What problems does Spring Security solve?
    • Configuring authentication and intercepting URLs
    • The Spring Security tag library for JSPs
    • Security at the method level
    • Customizing the Spring Security filter chain
  15. REST with Spring MVC
    • An introduction to the REST architectural style
    • Controlling HTTP response codes with @ResponseStatus
    • Implementing REST with Spring MVC, @RequestBody, @ResponseBody
    • Spring MVC’s HttpMessageConverters and automatic content negotiation
  16. Microservices with Spring Cloud
    • Microservice Architectures
    • Challenges with cloud-native applications
    • Using Spring Cloud
    • Developing a simple microservice system

Spring Web Official Course

Register for Spring Web Official Course

Objectives

At the end of the training, you should have an understanding of Spring and associated technologies for web development and be able to do the following:

  • Develop web applications using the Spring Framework
  • Use Spring Tool Suite
  • Implement RESTful servers using Spring MVC
  • Use Spring Boot to build applications quickly with autoconfiguration
  • Secure Web applications with Spring Security
  • Test Web applications for correctness and performance
  • Understand and use Web Sockets with Spring MVC

Syllabus

  1. Spring overview
    • Introduction to Spring configuration
    • Bean life cycle
    • Configuration alternatives
    • Integration testing with Spring
  2. Getting started with Spring Web MVC
    • Spring model-view-controller (MVC) overview
    • DispatcherServlet
    • Controller programming model overview
    • Spring MVC views
    • Simplifying configuration
  3. Spring MVC configuration I
    • XML configuration and the mvc namespace
    • Using Java Configuration as an alternative to XML configuration
    • Running in a Servlet 3 environment without web.xml
    • Interceptors
    • Message sources
  4. Managing layouts in Spring MVC
    • Page layout and structure
    • Creating reusable templates with Apache Tiles
    • Configuring Tiles in Spring MVC
  5. Spring MVC configuration II
    • Resource configuration and the resource pipeline
    • CORS and @CrossOrigin
    • Spring MVC infrastructure Beans
    • URL mappings
    • Handler mappers and handler adapters
  6. Using views in Spring MVC
    • Views and view resolvers
    • Setting up a View resolver chain
    • Alternating views and Content Negotiation
    • JSON and XML Views
  7. Form handling with Spring MVC
    • Form Rendering
    • Type Conversion
    • Data Binding
    • Form submission lifecycle
    • Form validation (using Spring and JSR 330 validation)
    • Form Object management
  8. Site personalization with Spring MVC
    • Working in several languages: internationalization support in Spring MVC
    • Look-and-feel changes using themes and locales
    • Handling Mobile Devices with Spring Mobile
  9. Implementing REST
    • Overview of REST and HATEOAS concepts
    • Using Spring’s RestTemplate for clients access
    • Extending Spring MVC to support RESTful interactions
    • HAL and the Spring HATEAOS project
  10. Exception handling
    • Using @RequestStatus with Exceptions
    • Adding Exception handlers to Controllers
    • Global exception handling using Controller Advices and Exception resolvers
    • Exception handling for RESTful interactions
  11. Building client applications with Ajax
    • Ajax and Spring MVC
    • Using JavaScript frameworks
    • Example: Spring MVC REST and jQuery
    • Creating custom tags to encapsulate JavaScript
  12. Web application security with Spring Security
    • Motivation for Spring Security
    • Spring Security in a Web environment
    • Using Spring Security tag libraries
    • Method security
  13. Debugging and testing Web applications
    • Debugging applications in a browser
    • Testing Web applications using Spring’s Mock MVC framework
    • Using Spring HtmlUnit
  14. Spring Boot
    • Fast development and deployment using Spring Boot
    • Simplified dependency management with starter POMs
    • Packaging options - JAR or WAR
    • Easily overriding Spring Boot defaults
  15. Spring Websockets
    • Overview of Websocket development
    • Using Websockets and Stomp with Spring MVC

Enterprise Spring

Register for Enterprise Spring

Objectives

At the end of the training, you should have an understanding of Spring and associated technologies for enterprise integration and be able to do the following:

  • Create concurrent applications and schedule tasks using Spring
  • Creating and consuming REST Web services
  • Use JMS for asynchronous communication
  • Understand and use distributed transactions
  • Use Spring Batch for Enterprise Integration based on batch processing
  • Use Spring Integration for pipes-and-filters integration
  • Use the configuration DSL
  • Have a basic understanding of Spring XD

Syllabus

  1. Styles of Enterprise Integration
    • Integration Styles Pros/Cons
    • Spring Support
  2. Tasks and Scheduling
    • Introduction to concurrency
    • Java Concurrency support
    • Spring’s task scheduling support
  3. REST Webservices
  4. Spring Integration configuration
    • Using the DSL
  5. Spring Integration advanced features
    • Splitting and aggregating
    • Dispatcher configuration
  6. Introduction to Spring Batch
    • Batch concepts
    • High-Level overview
    • Job parameters and job identity
    • Quick start using Spring Batch
    • Readers, Writers & Processors
    • JDBC Item Readers
  7. Spring Batch restart and recovery
    • ExecutionContext
    • Reading flat files
    • Sharing state between steps
    • Intro to skip, retry, repeat and restart
    • Listeners
    • Business logic delegation
    • Using Java Configuration
  8. Spring Batch admin and parallel processing
    • Spring Batch Admin
    • Scaling and parallel processing
  9. Spring XD
    • Spring XD for Batch Jobs, Integration flow and Data Ingestion
    • Spring XD Installation
    • Working with Streams (Definition, Source vs Sink, deployment, use-cases)
    • Working with Jobs (Definition, deployment, monitoring, use-cases)