Google BigQuery Training

• Educational Background: A basic understanding of data analysis and database management is recommended. 

• Technical Proficiency: Familiarity with SQL and Google Cloud tools will enhance understanding. 

• Interest in Data Analytics: Ideal for professionals and learners aiming to leverage BigQuery for large-scale data analysis. 

• Master BigQuery Basics: Gain a comprehensive understanding of BigQuery’s architecture and functionality. 

• Develop Querying Skills: Learn to write efficient queries and manage datasets in BigQuery. 

• Optimise Performance: Build expertise in query performance tuning and cost management. 

• Integrate with Cloud Services: Use BigQuery with other Google Cloud tools for seamless data processing and analytics. 

The Google BigQuery Training course is designed for professionals seeking to enhance their data analysis skills. It provides practical knowledge to efficiently query, manage, and analyse large datasets using Google BigQuery’s powerful tools. Below are the individuals who will benefit from this course:

• Data Analysts
• Business Intelligence Professionals
• Data Scientists
• Cloud Architects
• Database Administrators
• Technology Consultants
• Software Developers 

Module 1: Interacting with BigQuery 

• Introduction to BigQuery 

• BigQuery Sandbox and Web UI 

• Command-Line Tools 

• BigQuery Classic Web UI 

Module 2: Running and Managing Jobs 

• Introduction 

• Running Jobs Programmatically 

• Managing Jobs 

Module 3: Working with Datasets 

• Define Datasets 

• Dataset Locations 

• Creating and Copying Datasets 

• Controlling Access to Datasets 

• Listing Datasets 

• Updating Dataset Properties 

• Managing Datasets 

• Availability and Durability 

Module 4: Working with Table Schemas 

• Specifying a Schema 

• Specifying Nested and Repeated Columns 

• Modifying Table Schemas 

• Manually Changing Table Schemas 

Module 5: Working with Tables 

• Managing Tables and Table Data 

• Exporting Table Data 

• Updating Table Data Using DML 

Module 6: Working with Partitioned Tables 

• What are Partitioned Tables? 

• Creating Ingestion-Time Partitioned Tables 

• Creating Date/Time Partitioned Tables 

• Managing and Querying Partitioned Tables 

• Using DML with Partitioned Tables 

Module 7: Working with Clustered Tables 

• Define Clustered Tables 

• Creating and Using Clustered Tables 

Module 8: Working with Views 

• Introduction to Views 

• Creating a View 

• Controlling Access to Views 

• Creating Authorised Views 

• Listing Views 

• Updating View Properties 

• Managing Views 

Module 9: Labelling BigQuery Resources 

• Adding Labels 

• Viewing Labels 

• Updating Labels 

• Filtering Using Labels 

• Deleting Labels 

Module 10: Loading Data into BigQuery 

• Loading Data from Cloud Storage  

• Loading Data from Local File  

Module 11: Querying BigQuery Data 

• Running Interactive and Batch Queries 

• Writing Query Results 

• Using Cached Results 

• Querying Data Using a Wildcard Table 

• Saving and Sharing Queries 

• Scheduling Queries 

• Using the Query Plan Explanation 

Module 12: Querying External Data Sources 

• Querying Cloud Bigtable 

• Querying Google Cloud Drive Data 

Module 13: Controlling BigQuery Costs 

• Estimating Storage and Query Costs 

• Custom Cost Controls 

Module 14: Securing BigQuery Resources 

• Encryption at Rest 

• Using Cloud DLP to Scan BigQuery Data 

Module 15: BigQuery API Basics 

• Authentication 

• Batch Requests 

• Paging Through Tables 

• API Performance Tips