Data Warehousing for Partners

Data Warehousing for PartnersDWPGOGoogleGO-DWP1.6.2<ul> <li>Discuss key elements of Google Data Warehouse solution portfolio and strategy.</li><li>Map Enterprise Data Warehouses concepts and components to BigQuery and Google data services.</li><li>Identify best practices for migrating Data Warehouses to BigQuery and demonstrate key skills required to perform successful migrations.</li><li>Implement data load and transformation pipelines for a BigQuery Data Warehouse using Google data processing and integration services.</li><li>Implement a streaming analytics solution using Pub/Sub, Dataflow, and BigQuery.</li><li>Use Looker and LookML to generate reports and gain insights.</li><li>Explore the GIS, GIS Visualization, and Machine Learning enhancements to BigQuery.</li></ul>Required: <ul> <li>Have completed the Data Engineering on Google Cloud training.</li><li>Be a Google Cloud Certified Professional Data Engineer or have equivalent expertise in Data Engineering.</li><li>Have access to Cloud Connect - Partners.</li></ul>Recommended: <ul> <li>Experience building data processing pipelines.</li><li>Experience with Apache Beam and Apache Hadoop.</li><li>Java or Python programming expertise.</li></ul>Organizational requirements: <ul> <li>The Cloud Partner organization must have implemented at least one Data Warehouse solution previously on any Data Warehouse platform.</li></ul>The core audience for this course is Google Cloud Partners with the following relevant job roles: <ul> <li>Data Warehouse Deployment Engineers</li><li>Data Warehouse Consultants</li><li>Data Warehouse Architects</li></ul>These roles, though not the core audience, may find the course relevant should they meet the requirements: <ul> <li>Technical Project Leads</li><li>Technical Project Managers</li><li>Data / Business Analysts</li></ul><h4>Module 1 - Data Warehouse Solutions on Google Cloud</h4> Topics: <ul> <li>Implementing Big Data Solutions on Google Cloud</li><li>Customer Needs</li><li>Sample Architectures</li><li>Migration Strategies and Planning</li><li>Working with PSO</li></ul>Objectives: <ul> <li>Describe the Google portfolio of Data Warehouse and Data Processing services</li><li>Identify the Google strategy for Data Warehouse products and services</li><li>Locate technical resources for Data Warehouse partners</li></ul><h4>Module 2 - BigQuery for Data Warehousing Professionals</h4> Topics: <ul> <li>BigQuery Concepts</li><li>BigQuery Permissions and Security</li><li>Monitoring and Auditing</li><li>Schema Design</li><li>Partitioning and Clustering</li><li>Data Capture and Load Jobs</li><li>Handling Change and Slowly Changing Dimensions</li><li>Querying Data</li><li>Managing Workloads and Concurrency</li><li>Analyzing Data</li><li>Sizing and Cost Management</li><li>Query Optimization</li><li>Storage Optimization</li></ul>Objectives: <ul> <li>Describe the key components of a successful Data Warehouse implementation on BigQuery</li><li>Identify best practices for implementing a Data Warehouse with BigQuery</li><li>Use the Google Cloud console to access public datasets</li><li>Perform queries using the console and analyze query results using client libraries</li><li>Combine ecommerce datasets to create enhanced datasets using BigQuery joins and unions</li></ul><h4>Module 3 - Migrating to BigQuery</h4> Topics: <ul> <li>Migration Phases</li><li>Security</li><li>Google Cloud data warehouse Architecture</li><li>Post Migration</li><li>User Adoption</li></ul>Objectives: <ul> <li>Assess an existing data warehouse and develop a strategy to migrate it to BigQuery</li><li>Describe best practices for migrating existing data warehouses to BigQuery</li><li>Identify key resources, tools, and partner assets for migrating to BigQuery</li><li>Migrate sample SQL Server data to BigQuery using Striim</li><li>Identify resources to translate product-specific SQL queries to BigQuery Standard SQL</li></ul><h4>Module 4 - ETL Tools and Positioning</h4> Topics: <ul> <li>Dataproc</li><li>Cloud Data Fushion</li><li>Dataflow</li></ul>Objectives: <ul> <li>Describe the key features of Dataproc, Cloud Data Fusion, and Dataflow</li><li>Migrate Apache Spark Jobs to Dataproc</li><li>Identify best practices for creating Dataflow workflows using Dataflow templates</li><li>Configure Cloud Data Fusion to create a data transformation pipeline joining multiple sources with BigQuery as an output data sink</li><li>Build data pipelines that will ingest data from Cloud Storage into BigQuery using Dataflow</li></ul><h4>Module 5 - Streaming Analytics</h4> Topics: <ul> <li>Why Streaming Analytics?</li><li>The Pub/Sub Service</li><li>Dataflow Windows and Triggers</li><li>Dataflow Sources and Sinks</li><li>Migration and Adoption Challenges</li></ul>Objectives: <ul> <li>Identify the components of a streaming analytics solution on Google Cloud</li><li>Create a streaming IoT pipeline using Pub/Sub and Kafka</li><li>Explore design patterns and optimization considerations for streaming analytics solutions</li><li>Create and run a streaming Dataflow pipeline that ingests data from Pub/Sub to BigQuery using a Dataflow template</li></ul><h4>Module 6 - Introduction to Looker as a Data Platform</h4> Topics: <ul> <li>Looker Platform Overview</li><li>Looker Platform Architecture</li><li>Paradigm Shift: Modeling Language versus Hardcoded SQL</li><li>Core Analytical Concepts</li></ul>Objectives: <ul> <li>Navigate the Looker platform</li><li>Describe the Looker platform architecture</li><li>Discover the advantages of Looker Modeling Language (LookML) over hardcoded SQL</li><li>Describe the four core analytical concepts in Looker</li><li>Analyze and visualize data using Explores in Looker</li></ul><h4>Module 7 - BigQuery Extended Capabilities</h4> Topics: <ul> <li>BigQuery GIS</li><li>BigQuery ML</li></ul>Objectives: <ul> <li>Describe the key features of BigQuery GIS and BigQuery ML</li><li>Analyze data using BigQuery GIS functions and visualize results using BigQuery Geo Viz</li><li>Train and evaluate an ML model with BigQuery ML to predict taxi fares</li></ul>- Discuss key elements of Google Data Warehouse solution portfolio and strategy. - Map Enterprise Data Warehouses concepts and components to BigQuery and Google data services. - Identify best practices for migrating Data Warehouses to BigQuery and demonstrate key skills required to perform successful migrations. - Implement data load and transformation pipelines for a BigQuery Data Warehouse using Google data processing and integration services. - Implement a streaming analytics solution using Pub/Sub, Dataflow, and BigQuery. - Use Looker and LookML to generate reports and gain insights. - Explore the GIS, GIS Visualization, and Machine Learning enhancements to BigQuery.Required: - Have completed the Data Engineering on Google Cloud training. - Be a Google Cloud Certified Professional Data Engineer or have equivalent expertise in Data Engineering. - Have access to Cloud Connect - Partners. Recommended: - Experience building data processing pipelines. - Experience with Apache Beam and Apache Hadoop. - Java or Python programming expertise. Organizational requirements: - The Cloud Partner organization must have implemented at least one Data Warehouse solution previously on any Data Warehouse platform.The core audience for this course is Google Cloud Partners with the following relevant job roles: - Data Warehouse Deployment Engineers - Data Warehouse Consultants - Data Warehouse Architects These roles, though not the core audience, may find the course relevant should they meet the requirements: - Technical Project Leads - Technical Project Managers - Data / Business AnalystsModule 1 - Data Warehouse Solutions on Google Cloud Topics: - Implementing Big Data Solutions on Google Cloud - Customer Needs - Sample Architectures - Migration Strategies and Planning - Working with PSO Objectives: - Describe the Google portfolio of Data Warehouse and Data Processing services - Identify the Google strategy for Data Warehouse products and services - Locate technical resources for Data Warehouse partners Module 2 - BigQuery for Data Warehousing Professionals Topics: - BigQuery Concepts - BigQuery Permissions and Security - Monitoring and Auditing - Schema Design - Partitioning and Clustering - Data Capture and Load Jobs - Handling Change and Slowly Changing Dimensions - Querying Data - Managing Workloads and Concurrency - Analyzing Data - Sizing and Cost Management - Query Optimization - Storage Optimization Objectives: - Describe the key components of a successful Data Warehouse implementation on BigQuery - Identify best practices for implementing a Data Warehouse with BigQuery - Use the Google Cloud console to access public datasets - Perform queries using the console and analyze query results using client libraries - Combine ecommerce datasets to create enhanced datasets using BigQuery joins and unions Module 3 - Migrating to BigQuery Topics: - Migration Phases - Security - Google Cloud data warehouse Architecture - Post Migration - User Adoption Objectives: - Assess an existing data warehouse and develop a strategy to migrate it to BigQuery - Describe best practices for migrating existing data warehouses to BigQuery - Identify key resources, tools, and partner assets for migrating to BigQuery - Migrate sample SQL Server data to BigQuery using Striim - Identify resources to translate product-specific SQL queries to BigQuery Standard SQL Module 4 - ETL Tools and Positioning Topics: - Dataproc - Cloud Data Fushion - Dataflow Objectives: - Describe the key features of Dataproc, Cloud Data Fusion, and Dataflow - Migrate Apache Spark Jobs to Dataproc - Identify best practices for creating Dataflow workflows using Dataflow templates - Configure Cloud Data Fusion to create a data transformation pipeline joining multiple sources with BigQuery as an output data sink - Build data pipelines that will ingest data from Cloud Storage into BigQuery using Dataflow Module 5 - Streaming Analytics Topics: - Why Streaming Analytics? - The Pub/Sub Service - Dataflow Windows and Triggers - Dataflow Sources and Sinks - Migration and Adoption Challenges Objectives: - Identify the components of a streaming analytics solution on Google Cloud - Create a streaming IoT pipeline using Pub/Sub and Kafka - Explore design patterns and optimization considerations for streaming analytics solutions - Create and run a streaming Dataflow pipeline that ingests data from Pub/Sub to BigQuery using a Dataflow template Module 6 - Introduction to Looker as a Data Platform Topics: - Looker Platform Overview - Looker Platform Architecture - Paradigm Shift: Modeling Language versus Hardcoded SQL - Core Analytical Concepts Objectives: - Navigate the Looker platform - Describe the Looker platform architecture - Discover the advantages of Looker Modeling Language (LookML) over hardcoded SQL - Describe the four core analytical concepts in Looker - Analyze and visualize data using Explores in Looker Module 7 - BigQuery Extended Capabilities Topics: - BigQuery GIS - BigQuery ML Objectives: - Describe the key features of BigQuery GIS and BigQuery ML - Analyze data using BigQuery GIS functions and visualize results using BigQuery Geo Viz - Train and evaluate an ML model with BigQuery ML to predict taxi fares4 days2495.002600.002600.002600.002640.002600.003445.003170.002600.00