Google Cloud Platform Training Courses

Google Cloud Platform Training

Google Cloud Platform

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Google Cloud Platform Course Outlines

Code Name Duration Overview
gcpdespr Architecting with Google Cloud Platform: Design and Process 16 hours This two-day instructor-led class equips students to build highly reliable and efficient solutions on Google Cloud Platform. It is a continuation of theArchitecting with Google Cloud Platform: Infrastructure course and assumes hands-on experience with the technologies covered in that course. Through a combination of presentations, demos, and hands-on labs, participants learn to design GCP deployments that are highly reliable and secure; and how to operate GCP deployments in a highly available and cost-effective manner. 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. This class is intended for the following participants: Cloud Solutions Architects, Systems Operations professionals, DevOps Engineers, IT managers. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform.   COMING SOON!
archgcp Architecting with Google Cloud Platform: Infrastructure 24 hours This three-day instructor-led class introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems and applications services. This course also covers deploying practical solutions including securely interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring. 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. This class is intended for the following participants: Cloud Solutions Architects, DevOps Engineers. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform. The course includes presentations, demonstrations, and hands-on labs. Essential Cloud Infrastructure: Foundation  Module 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. Module 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. Module 3: Virtual Machines GCE, tags, VM options, vCPUs, disk options, images, and special features of persistent disks for VMs. Essential Cloud Infrastructure: Core Services  Module 4: Cloud IAM Members, roles, organizations, account administration, service accounts. Lab: Cloud IAM. Module 5: Resource Management Billing, Quotas, Labels, Names, Cloud Resource Manager. Lab: Lab Billing. Module 6: Data Services Cloud Storage, Datastore, Bigtable, Cloud SQL. Lab: Cloud Storage. Lab: Cloud SQL. Module 7: Interconnecting Networks VPNs, Cloud Router, Cloud Interconnect, Direct Peering, Cloud DNS. Lab: VPN and Cloud Router. Elastic Cloud Infrastructure: Scaling and Automation  Module 8: Infrastructure Automation Infrastructure automation, custom images, startup and shutdown scripts and metadata, Deployment Manager, Cloud Launcher. Lab: Hadoop Cluster Maker. Lab: Virtual Machine. Module 9: Autoscaling Load Balancing, Instance Groups, Autoscaler. Lab: Autoscaling. Module 10: Resource Monitoring Stackdriver, Monitoring, Logging, Error Reporting, Tracing, Debugging. Lab: Resource Monitoring (Stackdriver). Module 11: Containers Containers, Google Container Engine (GKE), and Container Registry. Module 12: Platform Security Learn about Google's layered security strategy that uses a multi-faceted approach to provide platform security services and benefits. Module 13: Managed Services Dataproc, Dataflow, BigQuery, Datalab. Lab: BigQuery and Datalab. Elastic Cloud Infrastructure: Containers and Services  Module 14: Application Development Infrastructure App Engine, Cloud SDK, Dev Tools, Cloud Source Repos, Cloud Pub/Sub, Cloud Endpoints and Apigee, Cloud Functions. Module 15: Application Development Services Google App Engine (GAE), Dev Tools, Cloud Source Repos. Lab: App Engine Development. Module 16: Containers Containers, Google Container Engine (GKE), and Container Registry. Lab: Kubernetes Load Balancing.
cp100a Google Cloud Platform Fundamentals: Core Infrastructure 8 hours This one-day instructor-led class provides an overview of Google Cloud Platform products and services. Through a combination of presentations, demos, and hands-on labs, participants learn the value of Google Cloud Platform and how to incorporate cloud-based solutions into business strategies. 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 This class is intended for the following: Individuals planning to deploy applications and create application environments on Google Cloud Platform. Developers, systems operations professionals, and solution architects getting started with Google Cloud Platform. Executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs. The course includes presentations, demonstrations, and hands-on labs. Module 1: Introducing Google Cloud Platform Explain the advantages of Google Cloud Platform Define the components of Google's network infrastructure, including: Points of presence, regions, and zones Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) Module 2: Getting Started with Google Cloud Platform Identify the purpose of projects on Google Cloud Platform. Understand the purpose of and use cases for Identity and Access Management. List the methods of interacting with Google Cloud Platform. Lab: Getting Started with Google Cloud Platform. Module 3: Google App Engine and Google Cloud Datastore Understand the purpose of and use cases for Google App Engine and Google Cloud Datastore. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand the purpose of and use cases for Google Cloud Endpoints. Lab: Deploying Applications Using App Engine and Cloud Datastore. Module 4: Google Cloud Platform Storage Options Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, and Google Cloud Bigtable. Learn how to choose between the various storage options on Google Cloud Platform. Lab: Integrating Applications with Google Cloud Storage. Module 5: Google Container Engine Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Deploying Applications Using Google Container Engine. Module 6: Google Compute Engine and Networking Identify the purpose of and use cases for Google Compute Engine. Understand the various Google Cloud Platform networking and operational tools and services. Lab: Deploying Applications Using Google Compute Engine. Module 7: Big Data and Machine Learning Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Lab: Getting Started with BigQuery.  
cpo200 Google Cloud Platform for Systems Operations Professionals 32 hours This 4 day instructor-led class introduces participants to the implementation of application environments and public cloud infrastructure using Google Cloud Platform. Through a combination of instructor-led presentations and hands-on labs, students learn how to deploy cloud infrastructure components such as networks, systems, and applications. This class is intended for Systems Operations professionals and Systems Administrators; Cloud Architects and DevOps personnel should enroll instead in the Architecting with Google Cloud Platform course sequence.   At the end of this four-day course, participants will be able to: Understand the core tenets to be considered when designing & deploying to a cloud Use the Google Developers Console to create and manage multiple projects Use service accounts and permissions to share view-level access between projects Create Google Compute Engine instances Create a non-default network and review your network configuration Compare default and non-default networks Create firewall-rules with and without tags Create and use a customized Compute Engine image Set authorization scopes for a Compute Engine instance Reserve an external IP address for an instance Snapshot a Compute Engine instance Snapshot a data disk Create an image using a boot persistent disk Upload an image to Google Container Registry Create a Compute Engine instance group with instances Create a Cloud SQL instance using the Cloud SDK Deploy and test a web application Add instance and project metadata Query instance and project metadata using the Cloud SDK Create an instance using a startup script in metadata and Google Cloud Storage Create an instance with a shutdown script and install the Cloud Logging agent Use the API Explorer to query an API request Run sample code that uses the Google API Client Library Test and build a container that uses the Cloud SQL APIs Create an instance template and managed instance group Configure a managed instance group for autoscaling Create multiple autoscaled managed instance groups Configure fault-tolerant HTTP load balancing Test health checks for use with HTTP load balancing Manage application deployment using Jinja and Python templates with Google Cloud Deployment Manager Delete Google Cloud Platform projects and resources Module 1: Google Cloud Platform Projects Identify project resources and quotas Explain the purpose of Google Cloud Resource Manager and Identity and Access Management Lab: Google Cloud Platform Projects Use the Google Developers Console to create and manage multiple projects Use service accounts and permissions to share view-level access between projects Module 2:Instances Explain how to create and move instances Identify how to connect to and manage instances Lab: Google Compute Engine Instances and Machine Types Create an instance using the Google Developers Console Configure the Cloud SDK on the Compute Engine instance Initialize Cloud Source Repositories using Git Module 3: Networks Explain how to create and manage networks in projects Identify how to create and manage firewall rules, routes, and IP addresses Lab: Google Compute Engine Networks Create a non-default network Compare default and non-default networks Create firewall-rules with and without tags Review network configuration in Google Cloud Monitoring Module 4: Disks and Images Explain how to create and manage persistent disks Identify how to create and manage disk images Lab: Google Compute Engine Disks and Images Create an instance and install the Java 7 JRE from OpenJDK Create a customized Compute Engine image Launch and test a Compute Engine instance based on your image Module 5: Authorization Explain the purposes of and use cases for Google Compute Engine service accounts Identify the types of service account scopes Lab: Google Compute Engine Authorization Set authorization scopes for a Compute Engine instance Reserve the external IP address for the new instance Install and configure Jenkins on a Compute Engine instance Module 6: Snapshots Identify the purpose of and use cases for disk snapshots Explain the process of creating a snapshot Lab: Google Compute Engine Snapshots Prepare and snapshot a Compute Engine instance Restore and test the snapshot to a different zone Snapshot a data disk without shutting down an instance Module 7: Google Cloud Storage Explain the purpose of and use cases for Google Cloud Storage Identify methods for accessing Google Cloud Storage buckets and objects Explain the security options available for Google Cloud Storage buckets and objects Lab: Google Cloud Storage for Backups Create and configure Nearline and DRA buckets Modify the lifecycle management policy for a bucket Copy data to a bucket using the Cloud SDK Review, modify, and test bucket ACLs Configure Jenkins to perform a backup to Cloud Storage Test and verify that the backups are working Lab: Google Container Registry Create a customized Jenkins build node instance Create an image using the instance's boot persistent disk Create a test build node instance based on the new image Test uploading images to Google Container Registry Module 8: Instance Groups Identify the purpose of and use cases for instance groups Explain the process of creating and using instance groups Lab: Google Compute Engine Instance Groups Create a Compute Engine instance group with instances Define Jenkins build tasks and run them Run the build tasks to create a guestbook image Module 9: Google Cloud SQL Understand how to create and administer Cloud SQL instances Explain how to access Cloud SQL instances from Compute Engine instances Lab: Google Cloud SQL Create a Cloud SQL instance using the Cloud SDK Create a Compute Engine instance from a custom image Deploy and test the Guestbook web application Module 10: Metadata Explain the purpose of metadata and identify the use cases for project and instance metadata Identify how to set and query metadata Lab: Google Compute Engine Metadata Add instance and project metadata Query instance and project metadata using the Cloud SDK Query metadata from inside a Compute Engine instance Module 11: Startup and Shutdown Scripts Identify the purpose of and use cases for startup and shutdown scripts Lab: Google Compute Engine Startup Scripts Create an instance with a startup script in metadata Create an instance with a startup script from Cloud Storage Create an instance with a shutdown script and install the Cloud Logging agent Lab: Google API Client Library Use the API Explorer to query an API request Run sample code that uses the Google API Client Library Test and build a container that uses the Cloud SQL APIs Create a new Compute Engine image Module 12: Autoscaling Explain the use cases for autoscaling and how autoscaling functions Identify the purpose of autoscaling policies Lab: Google Compute Engine Autoscaler Create an instance template and managed instance group Configure the managed instance group for autoscaling Generate an artificial load to trigger scaling of your cluster Module 13: Load Balancing Explain the differences between network load balancing and HTTP load balancing Identify the purpose of and use cases for cross-region and content-based load balancing Lab: HTTP/HTTPS Load Balancing Create multiple autoscaled managed instance groups Configure fault-tolerant HTTP load balancing Test health checks for use with HTTP load balancing Lab: Google Cloud Deployment Manager Create a Guestbook deployment using a plain YAML format Manage a Guestbook deployment using a Jinja template Create a Guestbook deployment using Python templates Lab: Deleting Cloud Platform Projects and Resources Delete Google Cloud Platform resources Test dependencies between resources Delete Google Cloud Platform projects
cpb200 CPB200: Google BigQuery for Data Analysts 24 hours This 3 day instructor led class introduces participants to Google BigQuery. Through a combination of instructor­led presentations, demonstrations, and hands­on labs, students learn how to store, transform, analyze, and visualize data using Google BigQuery. This class is intended for data analysts and data scientists responsible for: analyzing and visualizing big data, implementing cloud­based big data solutions, deploying or migrating big data applications to the public cloud, implementing and maintaining large­scale data storage environments, and transforming/processing big data. At the end of this one­day course, participants will be able to: Understand the purpose of and use cases for Google BigQuery Describe ways in which customers have used Google BigQuery to improve their businesses Understand the architecture of BigQuery and how queries are processed Interact with BigQuery using the web UI and command­line interface Identify the purpose and structure of BigQuery schemas and data types Understand the purpose of and advantages of BigQuery destinations tables and caching Use BigQuery jobs Transform and load data into BigQuery Export data from BigQuery Store query results in a destination table Create a federated query Export log data to BigQuery and query it Understand the BigQuery pricing structure and evaluate mechanisms for controlling query and storage costs Identify best practices for optimizing query performance Troubleshoot common errors in BigQuery Use various BigQuery functions Use external tools such as spreadsheets to interact with BigQuery Visualize BigQuery data Use access controls to restrict access to BigQuery data Query Google Analytics Premium data exported to BigQuery Module 1: Introducing Google BigQuery ● Understand the purpose of and use cases for Google BigQuery ● Describe ways in which customers have used Google BigQuery to improve their businesses Lab: Sign Up for the Free Trial and Create a Project ● Register for the GCP free trial ● Create a project using the Cloud Platform Console Module 2: BigQuery Functional Overview ● Describe the components of a BigQuery project ● Identify how BigQuery stores data and list the advantages of the storage model ● Understand the architecture of BigQuery and how queries are processed ● Describe the methods of interacting with BigQuery Lab: Explore BigQuery Interfaces ● Explore features of the BigQuery web UI ● Learn how to use the bq shell ● Execute queries using the BigQuery CLI in Cloud Shell Module 3: BigQuery Fundamentals ● Describe the purpose of denormalizing data ● Identify the purpose and structure of BigQuery schemas and data types ● Explain the types of actions available in BigQuery jobs ● Understand the purpose of and advantages of BigQuery destinations tables and caching Lab: BigQuery Components and Jobs ● Explore how data is organized in BigQuery ● Learn about the two types of table schemas ● Learn about jobs, and how to cancel them ● Investigate caching and destination tables Module 4: Ingesting, Transforming, and Storing Data ● Describe the methods for ingesting data, transforming data, and storing data using BigQuery ● Explain the function of BigQuery federated queries Lab 4, Part I: Loading Data into BigQuery and Using Federated Queries ● Load a CSV file into a BigQuery table using the web UI ● Load a JSON file into a BigQuery table using the CLI ● Transform data and join tables using the web UI ● Store query results in a destination table ● Query a destination table using the web UI to confirm your data was transformed and loaded correctly ● Export query results from a destination table to Google Cloud Storage ● Create a federated query that queries data in Cloud Storage Lab 4, Part II: Exporting App Engine Logs to BigQuery ● Set up Google Cloud Logging to export App Engine log data from the Guestbook application ● Use the BigQuery web UI to query the log data Module 5: Pricing and Quotas ● Explain the advantages of the BigQuery pricing model ● Use the pricing calculator to calculate storage and query costs ● Identify the quotas that apply to BigQuery projects Lab: BigQuery Pricing ● Evaluate the size of a query within BigQuery using the BigQuery web UI ● Use the Pricing Calculator and the total size of the query to estimate the query cost ● Examine how changing a query affects query cost Module 6: Clauses and Functions ● Explain the differences between BigQuery SQL and ANSI SQL ● Identify the purpose of and use cases for user­defined functions ● Explain the purpose of various BigQuery functions Lab: BigQuery Clauses and Functions ● Create and run a query using a wildcard function ● Create and run a query using a window function ● Create and run a query using a user­defined function Module 7: Nested and Repeated Fields ● Identify the purpose and structure of BigQuery nested, repeated, and nested repeated fields ● Describe the use cases for nested, repeated, and nested repeated fields Lab: Nested Fields ● Create a BigQuery table using nested data ● Run queries to explore the structure of the nested data Lab: Repeated Fields ● Create a BigQuery table using repeated data ● Run queries to explore the structure of the repeated data Lab: Nested Repeated Fields ● Create a BigQuery table using nested repeated data ● Run queries to explore the structure of the nested repeated data Module 8: Query Performance ● Explain the impact of the following in query performance: JOIN and GROUP BY, table wildcards, and table decorators ● Identify various best practices for optimizing query performance Lab: BigQuery Best Practices and Optimization Techniques ● Use denormalization to improve query performance ● Use subselects to improve the performance of queries with JOIN clauses ● Use destination tables to lower costs when running multiple, similar queries ● Use table decorators and table wildcards to improve query performance and to reduce costs Module 9: Troubleshooting Errors ● Describe how to handle the most common BigQuery errors: request encoding errors, resource errors, and HTTP errors Lab: Handling Errors ● Correct queries that produce syntax­related error messages ● Correct an error involving the order of a JOIN clause ● Correct an error involving an invalid table name ● Modify queries that exceed resource constraints Module 10: Access Control ● Describe the purpose of access control lists in BigQuery ● List and explain the project and dataset roles available in BigQuery ● Apply views for row­level security Lab: Access Control ● Manage access to datasets using project­level ACLs ● Manage access to datasets using dataset­level ACLs ● Set row­level permissions using views Module 11: Exporting Data ● List the methods of exporting data from BigQuery and the data formats available ● Describe the process of creating a job to export data from BigQuery ● Explain the purpose of wildcard exports to partition export data Lab: Exporting Data ● Export data from BigQuery using the web UI and CLI ● Export large tables using wildcard URIs Module 12: Interfacing with External Tools ● Describe how to use external tools to interface with BigQuery, including: spreadsheets, ODBC and JDBC drivers, the BigQuery encrypted client, and R Lab: Interfacing with External Tools ● Set up the BigQuery Reports add­on for Google Sheets ● Use the Reports add­on to query BigQuery data Module 13: Working with Google Analytics Premium Data ● Describe the schema of the Google Analytics Premium and AdSense data exported to BigQuery Lab: Working with Google Analytics Premium Data ● Build queries to analyze data from Google Analytics Premium Module 14: Data Visualization  ● Describe the options available for visualizing BigQuery data Lab: Visualizing Data ● Use Google Cloud Datalab to visualize data
cpd200 CPD200: Developing Solutions on Google Cloud Platform 24 hours This 3 day instructor-led class introduces participants to Solution Development for Google Cloud Platform. Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn how to develop cloud-based applications using Google App Engine, Google Cloud Datastore, and Google Cloud Endpoints. This class is intended for experienced application developers who want to learn how to develop solutions using Google Cloud Platform to create highly scalable backends for both web and mobile applications. At the end of this one­day course, participants will be able to: Manage Google Cloud Source Repositories using the Google Cloud Platform Console Test an App Engine application using the App Engine SDK Access the App Engine Development Server Console Create an API using Google Cloud Endpoints Test a Cloud Endpoint API using the API Explorer Deploy an application to App Engine using the App Engine SDK Design, structure and configure an App Engine application using multiple services Create Client IDs using the Google Cloud Platform Console Secure App Engine services and Cloud Endpoints APIs using authentication Configure and upload new versions of App Engine services Integrate Google Cloud Logging into App Engine applications Review quota usage in a Google Cloud Platform project Integrate different types of storage with App Engine applications Create and implement a data model for use with Google Cloud Datastore Implement a variety of queries in Google Cloud Datastore Update the index configuration in Google Cloud Datastore Implement transactions using Google Cloud Datastore Review Google Cloud Trace reports in the Google Cloud Platform Console Integrate the Memcache API into an App Engine application to increase performance Configure, run and review the output of Google Cloud Security Scanner Configure the scaling behaviour of individual App Engine Services Create App Engine handlers for Push Task Queues Send email from an App Engine application using the Mail API Schedule Tasks in App Engine using the Cron Service Update the configuration of the Cron Service Secure Task Push, and Cron Service handlers  Export Google Cloud Platform data from a project Delete Google Cloud Platform projects and resources   Module 1: Developing Solutions for Google Cloud Platform Identify the advantages of Google Cloud Platform for solution development Identify services and tools available for solution development using Google Cloud Platform Compare examples of Google Cloud Platform architectures for solution development Lab: Google Cloud Source Repositories Create a project for the course Use Google Cloud Shell to develop and test an application using the App Engine SDK  Configure Google Cloud Source Repositories to remotely host code in Google Cloud Platform Module 2: Google Cloud Endpoints Identify Cloud Endpoints features Explain how to develop APIs using Cloud Endpoints Compare development of Cloud Endpoints APIs using Java and Python Lab: Google Cloud Endpoints Review and edit Cloud Endpoints source code Deploy an application to App Engine Test a Cloud Endpoints  API in the APIs Explorer Module 3: App Engine Services Explain the use cases for App Engine Services and how to use them in structuring an application Identify how to deploy and access individual App Engine services Explain how to route requests to individual services Lab: Google App Engine Services Review the code for a sample application used throughout the remainder of the course Deploy multiple App Engine services to a single project Module 4: User Authentication and Credentials Compare authentication and authorization Identify options for securing App Engine applications Explain the use cases for Application Default Credentials Lab: User Authentication Configure the OAuth consent screen and create a client ID Modify Conference Central to use your client ID Test Conference Central authentication Modify your admin service to require administrator rights Module 5: Managing App Engine Applications Explain the use cases for App Engine versions Identify how to access App Engine monitoring and logging services Explain the use of resource quotas and how to troubleshoot related errors Lab: Managing Google App Engine Applications Review App Engine settings, quotas, instances, and logs Update App Engine services to log to Cloud Logging Deploy new versions of your default and admin services Route all traffic to a new version of the default service Module 6: Storage for Solution Developers Compare storage options for App Engine Solutions Developers Explain the purpose of, and use cases for, Google Cloud Storage Compare Cloud SQL integration with App Engine and Compute Engine Explain basic Cloud Datastore terminology and concepts, including Entity Groups Lab: Google Cloud Datastore Update an existing application to save data persistently Test saving application data to Cloud Datastore List and view Cloud Datastore entities in the Google Cloud Platform Console Module 7: Queries and Indexes Identify available query filters for Cloud Datastore Compare single­property, and composite indexes in Cloud Datastore Configure and optimize indexes for Cloud Datastore Lab: Google Cloud Datastore Queries and Indexes Add support for querying entities by kind and ancestor Add query filters to Cloud Datastore searches Update an index configuration to support composite indexes Module 8: Entity Groups, Consistency, and Transactions Identify the steps of a Cloud Datastore write Compare strong and eventual consistency in Cloud Datastore Identify how to achieve strongly consistent queries Identify best practises for Cloud Datastore transactions Lab: Google Cloud Datastore Transactions Add support for using Cloud Datastore transactions to an application Add a Cloud Endpoint API method to view data from a different service Module 9: App Engine Performance and Optimization Identify Memcache types, use cases, and implementation patterns Compare available scaling behaviours for application services Configure application scaling for individual services Lab: Google App Engine Performance and Optimization Review Cloud Trace reports for an application Configure and run a security scan for an application Update an application to make use of memcache Configure and test application scaling for application services Module 10: Task Queues Compare Push and Pull Queues Explain how to schedule tasks with the Cron Service Configure and securing Push and Pull Queues, as well as the Cron Service Lab: Task Queue API Add a task handler to send an email using the Mail API Add a Cron Service handler and configuration to an existing application Lab: Deleting Google Cloud Platform Projects and Resources Export Google Cloud Platform data from a project Delete Google Cloud Platform resources Shut down a Google Cloud Platform project
cpb100 Google Cloud Platform Fundamentals: Big Data & Machine Learning 8 hours This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform. 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 Cloud SQL 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. This class is intended for the following: Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform. Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports. Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists. The course includes presentations, demonstrations, and hands-on labs. Module 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. Module 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. Module 3: Data Analytics on the Cloud Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with SparkML. Module 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 Module 5: Data Processing Architectures Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing. Module 6: Summary Why GCP? Where to go from here Additional Resources
develgcp Developing Solutions with Google Cloud Platform 24 hours This three-day instructor-led course introduces you to concepts and best practices for developing applications with Google Cloud Platform. Through a combination of presentations, demos and hands-on labs, participants learn how to develop cloud-based applications using Google App Engine Standard Environment, Google Cloud Datastore, Google Cloud Endpoints, and Google Cloud Repositories. This course teaches participants the following skills: 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. This class is intended for experienced application developers who want to learn how to migrate applications to the cloud or create native, cloud-based applications on Google Cloud App Engine. Module 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. Module 2: Google Cloud Endpoints Cloud Endpoints features. Developing APIs using Cloud Endpoints. Accessing Cloud Endpoints APIs using JavaScript clients. Lab: Google Cloud Endpoints. Module 3: App Engine Services Modular application design and App Engine services. Deploying services. Accessing App Engine services. Lab: Google App Engine Services. Module 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. Module 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. Module 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. Module 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. Module 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. Module 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. Module 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.
cpde Data Engineering on Google Cloud Platform 32 hours This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data. 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 This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, Loading, Transforming, cleaning, and validating data Designing pipelines and architectures for data processing Creating and maintaining machine learning and statistical models Querying datasets, visualizing query results and creating reports The course includes presentations, demonstrations, and hands-on labs. Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform Module 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. Module 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. Module 3: Integrating Dataproc with Google Cloud Platform Customize cluster with initialization actions. BigQuery Support. Lab: Leveraging Google Cloud Platform Services. Module 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. Serverless Data Analysis with Google BigQuery and Cloud Dataflow Module 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. Module 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. Serverless Machine Learning with TensorFlow on Google Cloud Platform Module 7: Getting started with Machine Learning What is machine learning (ML). Effective ML: concepts, types. ML datasets: generalization. Lab: Explore and create ML datasets. Module 8: Building ML models with Tensorflow Getting started with TensorFlow. Lab: Using tf.learn. TensorFlow graphs and loops + lab. Lab: Using low-level TensorFlow + early stopping. Monitoring ML training. Lab: Charts and graphs of TensorFlow training. Module 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. Module 10: Feature Engineering Creating good features. Transforming inputs. Synthetic features. Preprocessing with Cloud ML. Lab: Feature engineering. Building Resilient Streaming Systems on Google Cloud Platform Module 11: Architecture of streaming analytics pipelines Stream data processing: Challenges. Handling variable data volumes. Dealing with unordered/late data. Lab: Designing streaming pipeline. Module 12: Ingesting Variable Volumes What is Cloud Pub/Sub? How it works: Topics and Subscriptions. Lab: Simulator. Module 13: Implementing streaming pipelines Challenges in stream processing. Handle late data: watermarks, triggers, accumulation. Lab: Stream data processing pipeline for live traffic data. Module 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. Module 15: High throughput and low-latency with Bigtable What is Cloud Spanner? Designing Bigtable schema. Ingesting into Bigtable. Lab: streaming into Bigtable.  
gcpfaws Google Cloud Platform Fundamentals for AWS Professionals 6 hours This six-hour course with labs introduces AWS professionals to the core capabilities of Google Cloud Platform (GCP) in the four technology pillars: networking, compute, storage, and database. It is designed for AWS Solution Architects and SysOps Administrators familiar with AWS features and setup and want to gain experience configuring GCP products immediately. With presentations, demos, and hands-on labs, participants get details of similarities, differences, and initial how-tos quickly. 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. This class is intended for the following: AWS Solution Architects just getting started with Google Cloud Platform. AWS SysOps Administrators used to building IaaS solutions. Architects and Engineers operating in multi-cloud environments. The course includes presentations, demonstrations, and hands-on labs. Module 1: Introducing Google Cloud Platform Google Cloud infrastructure. AWS regions, availability zones, and CloudFront. GCP regions, zones, edge caching, and Cloud CDN. GCP services. Module 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. Module 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. Module 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. Module 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. Module 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. Module 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. Module 8: Deployment and Monitoring AWS CloudFormation, Amazon CloudWatch. Google Cloud Deployment Manager, Google StackDriver. Lab: Deploy your infrastructure using Deployment Manager.

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Course Discounts

Course Venue Course Date Course Price [Remote / Classroom]
Programming in C# Wrocław, ul.Ludwika Rydygiera 2a/22 Wed, 2017-10-18 09:00 4851PLN / 1870PLN
Stress management Gdynia, ul. Ejsmonda 2 Wed, 2017-10-18 09:00 5148PLN / 1530PLN
Nginx Setup, Configuration and Administration Wrocław, ul.Ludwika Rydygiera 2a/22 Wed, 2017-10-18 09:00 6930PLN / 2700PLN
Business Analysis Kraków, ul. Rzemieślnicza 1 Wed, 2017-10-18 09:00 7722PLN / 3774PLN
MATLAB Programming Bydgoszcz, ul. Dworcowa 94 Thu, 2017-10-19 09:00 4356PLN / 1952PLN
Projektowanie stron na urządzenia mobilne Kielce, ul. Warszawska 19 Thu, 2017-10-19 09:00 2624PLN / 1305PLN
Adobe InDesign Wrocław, ul.Ludwika Rydygiera 2a/22 Mon, 2017-10-23 09:00 1881PLN / 1027PLN
Adobe Premiere Pro Gdynia, ul. Ejsmonda 2 Mon, 2017-10-23 09:00 3960PLN / 2480PLN
Administration of Linux System Gdynia, ul. Ejsmonda 2 Tue, 2017-10-24 09:00 4950PLN / 3225PLN
Adobe Photoshop Elements Lublin, ul. Spadochroniarzy 9 Wed, 2017-10-25 09:00 1881PLN / 1127PLN
Business Analysis, BABOK V3.0 and IIBA Certification Preparation Kraków, ul. Rzemieślnicza 1 Wed, 2017-10-25 09:00 9405PLN / 5903PLN
Zaawansowana administracja MySQL Poznan, Garbary 100/63 Thu, 2017-10-26 09:00 3416PLN / 2108PLN
Effective working with spreadsheet in Excel Warszawa, ul. Złota 3/11 Thu, 2017-10-26 09:00 2475PLN / 1225PLN
Node.js Olsztyn, ul. Kajki 3/1 Thu, 2017-10-26 09:00 3861PLN / 2431PLN
Advisory & Leadership Skills Wrocław, ul.Ludwika Rydygiera 2a/22 Mon, 2017-10-30 09:00 8524PLN / 2983PLN
Excel For Statistical Data Analysis Warszawa, ul. Złota 3/11 Thu, 2017-11-02 09:00 2673PLN / 1291PLN
SQL Advanced in MySQL Warszawa, ul. Złota 3/11 Thu, 2017-11-02 09:00 1881PLN / 1141PLN
Projektowanie stron na urządzenia mobilne Bielsko-Biała, Al. Armii Krajowej 220 Thu, 2017-11-02 09:00 2624PLN / 1605PLN
Symfony 3 Kraków, ul. Rzemieślnicza 1 Mon, 2017-11-06 09:00 6930PLN / 3300PLN
Oracle 11g - SQL language for developers - Workshop Bielsko-Biała, Al. Armii Krajowej 220 Mon, 2017-11-06 09:00 6930PLN / 4140PLN
Programowanie Aplikacji Webowych z Java EE 6 / 7 Zielona Góra, ul. Reja 6 Mon, 2017-11-06 09:00 7722PLN / 3340PLN
Android - The Basics Wrocław, ul.Ludwika Rydygiera 2a/22 Mon, 2017-11-06 09:00 9801PLN / 4180PLN
Java Spring Wrocław, ul.Ludwika Rydygiera 2a/22 Mon, 2017-11-06 09:00 14414PLN / 5970PLN
Test Automation with Selenium Łódź, ul. Tatrzańska 11 Mon, 2017-11-06 09:00 7722PLN / 3474PLN
Visual Basic for Applications (VBA) in Excel - Introduction to programming Warszawa, ul. Złota 3/11 Tue, 2017-11-07 09:00 3564PLN / 1691PLN
Programming for Biologists Warszawa, ul. Złota 3/11 Tue, 2017-11-07 09:00 11781PLN / 3745PLN
Quality Assurance and Continuous Integration Wrocław, ul.Ludwika Rydygiera 2a/22 Tue, 2017-11-07 09:00 2673PLN / 1737PLN
Oracle Service Bus 11g - Design and Integration Gdańsk, ul. Powstańców Warszawskich 45 Tue, 2017-11-07 09:00 15315PLN / 5391PLN
UML in Enterprise Architect (workshops) Warszawa, ul. Złota 3/11 Wed, 2017-11-08 09:00 5940PLN / 3570PLN
Managing Configuration with Ansible Warszawa, ul. Złota 3/11 Wed, 2017-11-08 09:00 16612PLN / 5634PLN
Tableau Advanced Gdynia, ul. Ejsmonda 2 Wed, 2017-11-08 09:00 7425PLN / 2975PLN
JMeter Fundamentals Warszawa, ul. Złota 3/11 Wed, 2017-11-08 09:00 1871PLN / 824PLN
Adobe Premiere Pro Gdańsk, ul. Powstańców Warszawskich 45 Thu, 2017-11-09 09:00 3960PLN / 2480PLN
Financial analysis in Excel Warszawa, ul. Złota 3/11 Thu, 2017-11-09 09:00 2079PLN / 1093PLN
DTP (InDesign, Photoshop, Illustrator, Acrobat) Bielsko-Biała, Al. Armii Krajowej 220 Mon, 2017-11-13 09:00 5940PLN / 3730PLN
Data Analysis with Oracle 11g - workshop Gdynia, ul. Ejsmonda 2 Mon, 2017-11-13 09:00 9900PLN / 4664PLN
Nagios Core Gdańsk, ul. Powstańców Warszawskich 45 Mon, 2017-11-13 09:00 13919PLN / 4968PLN
Visual Basic for Applications (VBA) in Excel - Advanced Gdańsk, ul. Powstańców Warszawskich 45 Mon, 2017-11-13 09:00 3069PLN / 1773PLN
Website Development in PHP Szczecin, ul. Sienna 9 Mon, 2017-11-13 09:00 2970PLN / 1344PLN
ADO.NET 4.0 Development Warszawa, ul. Złota 3/11 Tue, 2017-11-14 09:00 20176PLN / 6914PLN
Adobe Illustrator Lublin, ul. Spadochroniarzy 9 Tue, 2017-11-14 09:00 2871PLN / 1648PLN
Linux Fundamentals Kraków, ul. Rzemieślnicza 1 Tue, 2017-11-14 09:00 10128PLN / 3869PLN
Visual Basic for Applications (VBA) in Excel - Advanced Warszawa, ul. Złota 3/11 Wed, 2017-11-15 09:00 3069PLN / 1623PLN
Access Basics Szczecin, ul. Sienna 9 Mon, 2017-11-20 09:00 3465PLN / 1550PLN
OCEB2 OMG Certified Expert in BPM - Fundamental Exam Preparation Warszawa, ul. Złota 3/11 Mon, 2017-11-20 09:00 11880PLN / 4760PLN
Introduction to Selenium Poznan, Garbary 100/63 Wed, 2017-12-20 09:00 1871PLN / 824PLN
Adobe Photoshop Warszawa, ul. Złota 3/11 Wed, 2017-12-20 09:00 1881PLN / 1152PLN

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