Introduction

Google BigQuery is a fully-managed, serverless data warehouse solution built for the cloud. It is designed to handle large-scale data analysis in real-time, offering unmatched performance, scalability, and ease of use. BigQuery enables organizations to analyze massive datasets quickly and cost-effectively without the need to manage complex infrastructure.

This course provides in-depth knowledge and practical skills required to effectively work with Google BigQuery. Participants will learn how to design, manage, and optimize data engineering workflows, including data ingestion, transformation, querying, and integration with other Google Cloud services. The training will include hands-on exercises to work with real-world data engineering tasks, ensuring that participants gain actionable skills for using BigQuery in the context of cloud-based data engineering projects.

Objectives

By the end of this course, participants will:

• Understand the core concepts of Google BigQuery and its architecture.
• Gain experience with creating and managing datasets, tables, and views in BigQuery.
• Learn how to perform efficient data loading, transformation, and querying operations in BigQuery.
• Master SQL and BigQuery-specific features for advanced querying and optimization.
• Understand how to integrate BigQuery with other Google Cloud services like Google Cloud Storage, Cloud Functions, and Dataflow.
• Learn best practices for performance optimization, cost control, and data security in BigQuery.
• Gain hands-on experience building real-world data engineering pipelines using BigQuery.

Who Should Attend?

This course is intended for:

• Data engineers, data architects, and cloud engineers responsible for designing and managing data solutions in the cloud.
• Business analysts, data analysts, and data scientists who need to work with large datasets in Google BigQuery for data analysis and reporting.
• IT professionals seeking to expand their expertise in cloud data engineering and BigQuery.
• Developers interested in learning how to integrate BigQuery with other Google Cloud services for building end-to-end data solutions.
• Anyone seeking to understand and leverage the power of Google BigQuery for data engineering tasks.

Day 1: Introduction to Google BigQuery and Data Engineering Concepts

Morning Session: Introduction to BigQuery

• Overview of cloud data warehousing and Google BigQuery’s role in the ecosystem.
• Key features and benefits of using BigQuery for data engineering and analytics.
• Understanding the architecture of Google BigQuery: Storage and compute layers, datasets, tables, and views.
• Introduction to Google Cloud Platform (GCP) and BigQuery’s integration within GCP.
• Getting started with BigQuery: Setting up a Google Cloud project and accessing the BigQuery Console.
• Hands-on: Navigating the BigQuery Console, creating projects, and setting up datasets and tables.

Afternoon Session: BigQuery Storage and Compute Architecture

• Understanding BigQuery storage: Columnar storage format and how BigQuery stores data in the cloud.
• BigQuery compute: The role of query execution engines and resource allocation in cloud data processing.
• Managing data in BigQuery: Datasets, tables, partitions, and views.
• Working with both structured and semi-structured data (JSON, Avro, Parquet) in BigQuery.
• Hands-on: Creating datasets, loading data into BigQuery, and performing basic queries.

Day 2: Data Ingestion and Transformation in BigQuery

Morning Session: Data Loading Techniques

• Overview of data loading methods in BigQuery: Streaming data, batch loading, and external tables.
• Loading data from Google Cloud Storage into BigQuery using the Web UI and bq command-line tool.
• Using BigQuery Data Transfer Service to schedule recurring data imports.
• Importing structured and semi-structured data (JSON, Parquet) into BigQuery.
• Hands-on: Loading CSV, JSON, and Parquet data from Google Cloud Storage into BigQuery.

Afternoon Session: Data Transformation in BigQuery

• Using SQL to perform data transformations in BigQuery: SELECT, JOIN, GROUP BY, and advanced SQL functions.
• Introduction to BigQuery SQL UDFs (User-Defined Functions) for custom transformations.
• Working with BigQuery Dataflow for ETL pipelines and real-time data processing.
• Using BigQuery Streams and Tables for real-time data ingestion and transformations.
• Hands-on: Writing SQL queries to clean and transform data in BigQuery.

Day 3: Advanced Querying and Optimization in BigQuery

Morning Session: Advanced SQL Queries in BigQuery

• Writing advanced SQL queries: Window functions, complex joins, and subqueries.
• Working with ARRAYS and STRUCTS for handling nested and repeated fields in BigQuery.
• Leveraging BigQuery ML for machine learning within BigQuery using SQL.
• Writing optimized queries for large datasets using partitioning and clustering.
• Hands-on: Creating complex queries to perform analytics on large datasets using BigQuery.

Afternoon Session: Performance Optimization and Cost Control

• Understanding BigQuery’s query execution model: How queries are processed, cached, and executed.
• Query optimization techniques: Reducing query costs, optimizing performance with partitioning and clustering.
• Monitoring BigQuery performance using Query Execution Plans and BigQuery UI tools.
• Best practices for controlling costs: Managing query pricing, optimizing storage, and managing resource utilization.
• Hands-on: Analyzing query performance, optimizing SQL queries, and reducing execution time.

Day 4: Integrating BigQuery with Google Cloud Services

Morning Session: Integrating BigQuery with Google Cloud Storage and Cloud Functions

• How to connect BigQuery with Google Cloud Storage for seamless data transfer and storage management.
• Using Cloud Functions with BigQuery to trigger events, automate data workflows, and integrate with other services.
• Creating scheduled queries in BigQuery to automate recurring data tasks.
• Hands-on: Setting up automated data workflows with Cloud Functions and Google Cloud Storage.

Afternoon Session: BigQuery with Dataflow and Pub/Sub

• Introduction to Dataflow: Using Dataflow for ETL processing in combination with BigQuery.
• Setting up data pipelines with Pub/Sub to stream data into BigQuery.
• Integrating BigQuery with external tools like Apache Kafka for real-time streaming data.
• Hands-on: Creating a real-time data pipeline using Pub/Sub and Dataflow, loading data into BigQuery.

Day 5: Data Security, Governance, and Real-World Use Cases

Morning Session: Data Security and Governance in BigQuery

• Overview of data security in BigQuery: Encryption, authentication, and access controls.
• Managing access with Identity and Access Management (IAM) roles in Google Cloud.
• Auditing and monitoring data access using Cloud Audit Logs and BigQuery’s Access Transparency.
• Best practices for data governance: Managing datasets, tables, and sensitive data.
• Hands-on: Setting up IAM roles, managing data access, and securing sensitive data in BigQuery.

Afternoon Session: Real-World Data Engineering Use Cases

• Real-world use cases: Building end-to-end data pipelines with BigQuery, Google Cloud Storage, and Dataflow.
• Leveraging BigQuery for business intelligence, machine learning, and real-time analytics.
• Best practices for scaling BigQuery workloads in large data environments.
• Review of key takeaways and hands-on final project: Designing and deploying a cloud-based data engineering solution.
• Hands-on: Building a complete data pipeline and solution using BigQuery.

Materials and Tools:

• Software: Google Cloud Platform (GCP), BigQuery Console, Google Cloud SDK, Google Cloud Storage.
• Datasets: Sample datasets for real-world scenarios (e.g., e-commerce, financial, or log data).
• Recommended Reading: Google BigQuery documentation, best practices, and case studies.

Post-Course Support:

• Access to course materials, recorded sessions, and community forums for ongoing learning.
• Practical exercises and a final project focused on building a complete data pipeline using BigQuery.
• Continuing support through expert Q&A sessions and further resources for expanding BigQuery knowledge.