Google BigQuery Training
Level
IntermediateDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedGoogle BigQuery Training
Google BigQuery is a data warehouse available in Google Cloud. It provides storage and management of large amounts of data. BigQuery is highly scalable and does not require maintaining costly infrastructure.
What You Will Learn
- Understand the basics of Google BigQuery – a rapidly growing data warehouse in Google Cloud Platform
- Learn core skills in writing queries, creating and managing datasets and tables, and designing ETL/ELT processes with Google Cloud tools
- Visualize data stored in the warehouse using Looker Studio and Google Sheets, and connect BigQuery with tools such as Power BI and Tableau
- Explore real-world applications of BigQuery in data science and get introduced to machine learning with BigQuery ML
Who is this training for?
Solution architects and data warehouse specialists-
Data analysts and professionals working with data processing
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Data engineers responsible for building and maintaining infrastructure
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People familiar with SQL basics
Training Program
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Module 1 – Introduction to Google Cloud Platform
- BigQuery as part of Google Cloud Platform (GCP)
- Complementary GCP services: Cloud Storage, Cloud SQL, Cloud Functions, DataPrep, etc.
- Key GCP components for working with BigQuery:
- Projects
- Billing accounts
- User permissions
- Introduction to data warehouses – concepts and assumptions
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Module 2 – Basics of Working with Google BigQuery
- Datasets, tables, and queries – data management in BigQuery
- BigQuery query editor interface
- Cloud Shell – working in the CLI environment
- Basic SELECT queries
- Filtering (WHERE), sorting (ORDER BY)
- Aggregations (COUNT, SUM) with GROUP BY and HAVING
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Module 3 – Creating and Managing Datasets and Tables
- Creating and configuring datasets
- Creating tables with schema and
CREATE OR REPLACE TABLE - Data types and column modes
- Working with arrays and structs
- Partitioning data
- Querying wildcard tables
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Module 4 – Loading Data into Google BigQuery
- ETL / ELT processes in BigQuery
- Using BigQuery Public Datasets
- Importing data from Google Cloud Storage
- Loading data from MySQL and PostgreSQL
- Loading data from Google Drive and Google Sheets
- Using the BigQuery API for data logging
- Automating data loads:
- Data Transfer Service
- Scheduled Queries
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Module 5 – Writing SQL Queries in BigQuery (Practice)
- JOINs for combining data from multiple tables
- Common Table Expressions (CTEs) using
WITH - Working with arrays:
ARRAY_AGGandUNNEST - Date and time formatting using
TIMESTAMP,DATETIME, andDATE - Saved Queries for teamwork and collaboration
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Module 6 – Data Visualization and Reporting
- Exporting and analyzing data in Google Sheets
- Building dashboards in Looker Studio with BigQuery
- Integrating BigQuery with:
- Power BI
- Tableau
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Module 7 – Practical Applications in Daily Work
- Using the BigQuery API and Google Cloud Client Libraries:
- Python
- pandas
- Jupyter Notebooks
- Service accounts and external tools (e.g. DataGrip)
- Introduction to BigQuery ML:
- Regression models
- Time series forecasting
