Google BigQuery Training

• Understand the fundamentals of Google BigQuery, its features, and its role in the Google Cloud Platform ecosystem.
• Load and ingest data into BigQuery from various sources, including CSV files, JSON files, and Google Cloud Storage.
• Perform advanced querying techniques using BigQuery SQL, including using SQL functions, joins, aggregations, and window functions.
• Transform and analyze data in BigQuery, including data cleansing, enrichment, and aggregation.
• Optimize query performance in BigQuery by utilizing query caching, partition pruning, and query optimization techniques.
• Implement security measures to protect data in BigQuery, including access control, data encryption, and audit logging.
• Adhere to data governance practices in BigQuery by enforcing policies and compliance with regulatory requirements.

• Recorded Videos After Training
• Digital Learning Material
• Course Completion Certificate
• 24x7 After Training Support

• This course is for IT Professionals who want to learn about Google Cloud Technologies.

• This course is for IT Professionals who want to learn about Google Cloud Technologies.

• Multisoft Systems provides a globally recognized training certificate after successful completion of Google BigQuery Course.

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
• Auto-Detecting Schemas
• Modifying Table Schemas
• Manually Changing Table Schemas

Module 5: Working with Tables

• Creating and Using 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
• Creating Integer Range 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
• Querying Clustered Tables

Module 8: Working with Views

• Introduction to Views
• Creating Views
• Controlling Access to Views
• Creating Authorised Views
• Listing Views
• Updating View Properties
• Managing Views

Module 9: Labeling 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 a Local File
• Streaming Data into BigQuery

Module 11: Querying BigQuery Data

• Running Interactive and Batch Queries
• Performing a Query Dry Run
• Writing Query Results
• Using Cached Results
• Running Parameterised Queries
• Querying Data Using a Wildcard Table
• Saving and Sharing Queries
• Scheduling Queries
• Using the Query Plan Explanation
• Using the BigQuery Connector for Excel

Module 12: Querying External Data Sources

• Creating a Table Definition File
• Querying Externally Partitioned Data
• Federated Queries with Cloud SQL Data
• Querying Cloud Bigtable and Storage Data
• Querying Google Cloud Drive Data

Module 13: Controlling BigQuery Costs

• Introduction to BigQuery Costs
• Estimating Storage and Query Costs
• Custom Cost Controls

Module 14: Securing BigQuery Resources

• Access Controls
• Encryption at Rest
• Using Cloud DLP to Scan BigQuery Data
• Protecting Data with Cloud KMS Keys
• BigQuery Monitoring Using Stackdriver

Module 15: BigQuery API Basics

• Authentication
• Authorising API Requests
• Batch Requests
• Paging through Tables
• API Performance Tips