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GCP BigQuery Tutorial for Beginners

Let's learn together about Columnar Storage, which allows you to conveniently handle both structured and unstructured data provided by GCP and allows for a free table format, and BigQuery, a universal database.

20 learners are taking this course

Level Beginner

Course period Unlimited

SQL
SQL
Google Cloud Platform
Google Cloud Platform
bigquery
bigquery
sql-query
sql-query
database
database
SQL
SQL
Google Cloud Platform
Google Cloud Platform
bigquery
bigquery
sql-query
sql-query
database
database

What you will gain after the course

  • Special syntax provided by BigQuery

  • ARRAY, STRUCT Data Type Queries

  • Simple SQL Query

  • Various JOIN Patterns and Pros/Cons

What is BigQuery and why should you learn it? 🤔

BigQuery is a serverless data warehouse on Google Cloud Platform (GCP). BigQuery is a cloud database that easily handles petabytes of data, helping companies store and query it. These advantages allow you to upload massive amounts of data to BigQuery machine learning to better understand it. BigQuery also allows you to easily retrieve any data you need from anywhere. Using various plugins, including Google Drive, you can easily import external datasets into BigQuery tables. This scalability is driving BigQuery's continued growth in popularity.


Anyone familiar with SQL will find BigQuery easy to learn . Its advantages include fast data processing and scalability. It can be used for complex data analysis and integration with other Google Cloud services, making it ideal for Google Analytics 360.

In this course, you'll learn how to explore data, tables, and datasets. Use BigQuery hints and formatting help to write efficient queries. Using the SELECT statement, you'll write SQL queries that answer important questions about your data, using the FROM, WHERE, GROUP BY, HAVING, and ORDER BY clauses. If you're unfamiliar with working with multiple tables and using joins, don't worry. You'll learn everything in this beginner's BigQuery course.

BigQuery's serverless architecture allows you to answer your organization's biggest questions without managing infrastructure, using languages like SQL and Python. BigQuery provides a uniform way to handle both structured and unstructured data and supports open table formats like Apache Iceberg, Delta, and Hudi.


By completing this course, you will gain a lot of confidence in SQL 😃



What are the unique features of this course for beginners using GCP BigQuery?

📌 The course will be conducted directly in the BigQuery environment.

📌 Even those without SQL experience can easily follow the lecture content.

📌 We focus more on practice than theory, so we write SQL line by line and include additional explanations.

📌 The instructor's know-how and explanations, with over 10 years of SQL experience, are easy to understand.

I recommend this to these people

I want to learn more about BigQuery.
Anyone who wants to learn the in-depth internals of BigQuery components

I want to learn SQL quickly.
If you're just starting your data journey and want to master SQL from beginner to advanced in a short period of time.

I'm interested in the Google Cloud Data Engineer certification.
If you would like to learn more about BigQuery, it is included in the data engineer certification course.

After class

  • The BigQuery Console environment is becoming more convenient.

  • You can become more proficient in SQL.

  • BigQuery makes data analysis easier.

  • Preparing for the Google Cloud Data Engineer certification becomes easier.

Learn about these things.

ARRAY & STRUCT

ARRAY, a complex data type provided by BigQuery & STRUCT is covered in depth and explained in an easy-to-understand way with abundant practical exercises.

Various JOIN patterns

SQL has many JOIN patterns. Knowing when and how to use JOINs can be difficult for beginners, so we've included illustrations throughout the lecture to help you understand.

SQL basic grammar

This course covers numerous essential keywords for writing SQL. You can enjoy hands-on practice without worrying about incurring fees when running queries using public datasets provided by BigQuery.

CRUD

This is a topic that is not covered well in many SQL lectures, but this lecture explains in detail what CRUD is and how it is used in BigQuery.

Instructor Introduction


  • Tempus Inc - Senior Software Engineer (2019 ~ Present)

    • Data preprocessing development using BigQuery & Python BigQuery SDK & DBT (Data Build Tool)

    • Implement PHI data masking & automate data legal review process

  • CarVi Inc - Data Scientist ( 2017 - 2019)

    • Autonomous driving data analysis

    • Real-time driving data collection and analysis, lane departure and vehicle collision detection system implementation


  • Inflearn Instructor - AWS Beginner Course ( Lecture Link )

  • Inflearn Instructor - AWS Intermediate-Advanced Course ( Lecture Link )

  • Inflearn Instructor - AWS Practical Course ( Lecture Link )

  • Inflearn Instructor - Python Beginner Course ( Lecture Link )

  • Author - AWS for Business ( Book Link )


Things to note before taking the course

Practice environment

  • The lecture will be held on MacOS and Google Chrome.


Learning Materials

  • This course does not provide separate attachments. All lectures are conducted via video.

Player Knowledge and Precautions

  • SQL basics (even those who have never used SQL before are welcome 😄 )

  • Basic programming knowledge (no programming knowledge required 😃 )

Recommended for
these people

Who is this course right for?

  • If you want to try BigQuery

  • If you are interested in SQL

  • If you want to learn BigQuery's unique grammar

Need to know before starting?

  • SQL Basics

  • Basic programming knowledge

Hello
This is Sungmin Kim

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Hello. I am Simon Kim, currently working as a data engineer in the healthcare domain after completing my Bachelor's in Computer Science and Master's in Data Science in the United States.

In my current role, I design and operate ETL pipelines that collect large volumes of data daily based on AWS and Airflow. I also manage monitoring systems using CloudWatch and Splunk to ensure data stability and quality. My responsibilities include analyzing the root causes of issues, improving pipelines as needed, and directly implementing new features.

My primary technology stack includes Python, SQL, and AWS. Recently, through a large-scale migration project to GCP, I have been gaining in-depth experience in BigQuery, GCS, and GKE environments. Additionally, I continuously work in IaC environments, managing infrastructure as code using Docker and Terraform.

Furthermore, I have recently developed an interest in AI Agent systems and Harness Engineering, and I am designing and experimenting with agent-based automation systems in both my professional work and personal projects. Beyond simply using models, I am continuously contemplating how to connect multiple agents and ensure their stable execution and management—specifically focusing on "AI Agent Orchestration" and "Execution Harness" architectures.

What I have felt most strongly while working as a data engineer is that while technology is constantly changing, its essence does not differ as much as one might think. Once you deeply understand one technology, the process of expanding to others becomes much easier. Focusing on this "commonality of core principles," I want to deliver a learning experience that goes beyond a simple list of technologies to help you understand the fundamental essence.

Through this lecture, I want to generously share the practical experience and insights I have gained in the field, and serve as a guide so that you can develop the strength to solve problems on your own.

I, Simon Kim, aim to create fun and easy-to-understand lectures by breaking down difficult and complex technologies. I want to grow together with my students through constant communication.

It is my greatest reward to witness the process of your skills growing noticeably. Thank you.

 

Published Books: Introduction to AWS for Immediate Practical Use

 

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