The key objectives of this Google BigQuery course include:

• Understanding BigQuery Fundamentals: Gaining a solid grasp of BigQuery's architecture, storage, and processing capabilities. Understanding how it fits into the broader Google Cloud Platform ecosystem.

• Mastering SQL for BigQuery: Learning how to write efficient SQL queries to extract insights from large datasets stored in BigQuery. Understanding the specific SQL dialect and functions used in BigQuery.

• Loading and Managing Data: Acquiring skills in loading data into BigQuery from various sources, including Google Cloud Storage, external databases, and streaming data. Learning how to organize and manage data within BigQuery datasets and tables.

• Optimizing Query Performance: Exploring techniques for optimizing query performance and reducing costs in BigQuery. Understanding how to use partitioning, clustering, and materialized views effectively.

• Leveraging BigQuery ML: Discovering how to create and deploy machine learning models directly within BigQuery using SQL. Understanding the basics of BigQuery ML capabilities and use cases.

• Data Visualization and Reporting: Integrating BigQuery with data visualization tools like Looker or Data Studio to create dashboards and reports for business intelligence.

• Security and Access Control: Learning how to implement robust security measures and manage access controls for BigQuery data and resources.

• Best Practices: Gaining knowledge of best practices for designing efficient data pipelines, managing BigQuery projects, and troubleshooting common issues.

Google Cloud BigQuery - Course Curriculum

This course is designed to introduce learners to Google BigQuery, a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. The curriculum covers fundamental concepts, hands-on exercises, and practical use cases to provide a comprehensive understanding of BigQuery.

Module 1: Introduction to Google Cloud Platform (GCP)

• Overview of GCP

  • What is Google Cloud Platform?

  • Key services and features

  • Setting up a GCP account

• Navigating the GCP Console

  • Understanding the GCP Console interface

  • Introduction to Cloud Shell

  • Introduction to Google Cloud SDK

Module 2: Introduction to BigQuery

• What is BigQuery?

  • Overview of BigQuery

  • Key features and benefits

  • Working of BigQuery

  • Use cases for BigQuery

• BigQuery Sandbox

• Setting Up BigQuery

  • Creating a GCP project

  • Enabling the BigQuery API

  • Understanding BigQuery datasets and tables

Module 3: Working with BigQuery

• BigQuery Interface

  • Navigating the BigQuery Console

  • Using the BigQuery command-line tool

  • Google Cloud SDK

· Introduction to BigQuery client libraries

• Loading and Exporting Data

  • Data formats supported by BigQuery

  • Loading data into BigQuery from various sources (CSV, JSON, Cloud Storage)

  • Google Cloud Storage (GCS) bucket

Module 4: Querying Data in BigQuery

• BigQuery SQL Basics

  • Introduction to SQL

  • Understanding SQL syntax in BigQuery

  • Writing and running queries in BigQuery

• Advanced SQL Queries

  • Using joins and subqueries

  • Aggregations and window functions

  • Partitioning and clustering for performance

Module 5: BigQuery Data Management

• Managing Datasets and Tables

  • Creating and managing datasets

  • Managing Table Schemas

• Move a BigQuery Public Dataset Under Your Project

• Data Transformation and Cleaning

  • Using SQL for data transformation

  • Data cleaning techniques

Module 6: BigQuery Performance Optimization

• Optimizing Queries

  • Query performance best practices

  • Using query execution plans

  • Caching and materialized views

• Cost Management

  • Understanding BigQuery pricing

  • Cost optimization strategies

  • Monitoring and managing BigQuery costs

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