Google Big Query is a cloud-based data warehousing solution that allows users to analyze and query large datasets in real time. Given its complexity and advanced functionality, it is helpful for individuals seeking to expand their knowledge on the platform to enroll in online courses. In this article, we will explore some of the best Google Big Query courses available online, providing insightful information on the course content and the skills and knowledge they offer.
Here’s a look at the Best Google Big Query Courses and Certifications Online and what they have to offer for you!
10 Best Google Big Query Courses and Certifications Online
- 10 Best Google Big Query Courses and Certifications Online
- 1. Google BigQuery & PostgreSQL : Big Query for Data Analysis by Start-Tech Academy (Udemy) (Our Best Pick)
- 2. BigQuery for Big data engineers – Master Big Query Internals by J Garg – Real Time Learning (Udemy)
- 3. Learn SQL for Data Analysis with Google Big Query by Annabel Lyle (Udemy)
- 4. Applied SQL For Data Analytics / Data Science With BigQuery by Jeff James (Udemy)
- 5. Google BigQuery for Marketers and Agencies – 2022 by Lachezar Arabadzhiev, SkildLabs Inc. (Udemy)
- 6. The Complete Google BiqQuery Masterclass: Beginner to Expert by Sandeep Kumar (Udemy)
- 7. Practical Google BigQuery for those who already know SQL by Jerzy Balcerzak (Udemy)
- 8. Introduction to Google Cloud BigQuery by Dan Sullivan, Daniel Sullivan (Udemy)
- 9. Serverless Data Analysis with Big Query on Google’s Cloud by Mike West (Udemy)
- 10. BigQuery ML – Machine Learning in SQL using Google BigQuery by J Garg – Real Time Learning (Udemy)
1. Google BigQuery & PostgreSQL : Big Query for Data Analysis by Start-Tech Academy (Udemy) (Our Best Pick)
This course, offered by Start-Tech Academy, teaches students how to become a BigQuery expert by mastering Google BigQuery for data analysis. The curriculum covers all SQL queries in PostgreSQL and Big Query, and includes business-related examples and case studies. The course also includes ample practice exercises on Google BigQuery, as SQL and Google BigQuery require practice. Additionally, the course offers downloadable resources on SQL and Google BigQuery. Students can expect a verifiable certificate of completion upon finishing the course.
The course is taught by Abhishek and Pukhraj, who have been teaching Data Science and Machine Learning for over a decade. They have experience in teaching and using Google BigQuery and PostgreSQL for data analysis purposes. The course promises to answer any questions students have about the course content, Google BigQuery, PostgreSQL, practice sheet, or anything related.
Students can download practice files, take quizzes, and complete assignments with each lecture. Each section contains a practice assignment for students to practically implement their learning on Google BigQuery and PostgreSQL. By the end of this course, students should have a thorough understanding of how to use Google BigQuery and PostgreSQL for data analytics as a career opportunity.
SQL is the most universal and commonly used database language, powering the most commonly used database engines like PostgreSQL, SQL Server, SQLite, and MySQL. This course teaches SQL from the basics to the advanced level, with practice exercises provided in every section. SQL is one of the most sought-after skills by hiring employers and can lead to good earning potential.
PostgreSQL is one of several Relational Database Management Systems (RDMS) that use SQL as their language. Other examples of RDMS include Oracle, Informix, MySQL, and MSQL. BigQuery is a web service from Google used for handling or analyzing big data. It is part of the Google Cloud Platform and offers users the ability to manage data using fast SQL-like queries for real-time analysis.
2. BigQuery for Big data engineers – Master Big Query Internals by J Garg – Real Time Learning (Udemy)
The course titled “BigQuery for Big data engineers – Master Big Query Internals” is designed for data engineers and analysts looking for a complete and in-depth guide to Google BigQuery concepts and internals. The course focuses on providing hands-on examples to teach participants to interact with BigQuery using its web console, Bq CLI, and Python client library. Participants will learn to create, load, modify, and manage BigQuery datasets, tables, views, materialized views, and query execution plan, efficient schema design, optimization techniques, partitioning, and clustering.
The course also covers building and deploying end-to-end data pipelines using services such as Dataflow, Pub/Sub, BigQuery, and Cloud Storage. Best practices and optimization techniques for real-time Google Cloud BigQuery projects are also discussed. After completing the course, participants will have the confidence to work on any BigQuery project.
The course includes a brief introduction to the set of services provided by Google Cloud, complete in-depth knowledge of Google BigQuery concepts, and datasets and queries used in lectures for convenience. The course content includes sections such as Introduction to GCP & its services, Introduction to BigQuery, Dataset & Table creation, Using BigQuery Dashboard options, Efficient Schema Design in BigQuery, Operations on Datasets & Tables, Execution Plan of BigQuery, Partitioned Tables in BigQuery, Clustered Tables in BigQuery, Loading & Querying External Data Sources, Views in Bigquery, Materialized Views in BigQuery, BQ Command Line, Python Client Library of BigQuery, Build End-to-End Data Pipelines (Apache Beam), Build Streaming Data Pipelines, BigQuery Pricing, Best Practices/Optimization Techniques, and Additional Learnings – Different File Formats & Apache Beam.
The instructor of the course is J Garg from Real Time Learning. Add-ons to the course include quick responses to questions and queries, frequent updates to the course with new components of Bigquery, and a bonus section.
Course Title: Learn SQL for Data Analysis with Google Big Query
This course is designed to teach students how to use SQL with Google BigQuery for fast and effective data analysis. Students will learn how to write complex queries and use functions to query their data sets. The course covers BigQuery’s key features and how to navigate its user interface.
The course is designed to provide transferable SQL skills that can be used with any SQL database, including MySQL and PostgreSQL. Students will also learn how to export their data for a range of use cases after completing their analysis.
This course is aimed at data analysts, data scientists, engineers, and anyone interested in learning more about SQL, BigQuery, or data analysis. The course instructor, Annabel Lyle, is a Data Scientist with over 6 years of experience in Data Analytics.
The course is divided into several sections covering key topics, including retrieving data with the SELECT statement, filtering data with the WHERE statement, using aggregate analytical functions to calculate metrics, and joining tables together. Students will also learn how to create new columns with single line functions and export their data.
Learning SQL is an in-demand tech skill and one of the most important skills for data analysts. This course provides a comprehensive introduction to SQL and BigQuery, with free preview videos available for more information.
This course, titled “Applied SQL for Data Analytics / Data Science with BigQuery,” is designed to help students develop a comprehensive understanding of SQL concepts and their application in solving real-world problems. Instructor Jeff James has over a decade of industry experience and is well-versed in SQL. The course focuses on application-based learning and does not use toy data unless necessary. Students can start the course immediately as no set-up is required, and the course follows a spaced repetition approach to develop mastery. Mini-challenges are provided throughout the course material to help students solidify their understanding.
The course is designed to be engaging and interactive, with a focus on problem-solving rather than theory without context. Students will learn to write SQL queries and develop rich mental models for solving complex problems. The course covers a range of topics and is divided into sections such as “BigQuery Set-up – Writing Our First Queries!” and “Google Analytics Attribution Analysis.” The course also includes mini-projects such as “JOIN’s Mini Project – Hourly Revenue Trends” and “Stock Price Project” to help students apply what they have learned.
Overall, this course is suitable for those looking to develop their SQL skills and apply them to data analytics and data science. It is recommended for students who want to learn from an experienced instructor who provides plenty of practical examples and challenges.
5. Google BigQuery for Marketers and Agencies – 2022 by Lachezar Arabadzhiev, SkildLabs Inc. (Udemy)
The Google BigQuery for Marketers and Agencies course is designed to help marketing professionals use SQL-powered queries in Google BigQuery to analyze marketing data and derive meaningful insights. The course comprises an introduction to the most useful SQL queries for marketers, the role of BigQuery and SQL in the larger data ecosystem, and two hands-on projects. By the end of the course, students will be able to explore user-level data in Google Analytics 360, visualize active queries and data tables in Google Data Studio, filter values with the WHERE clause, create permanent BigQuery tables, combine multiple tables with different types of JOINs, aggregate data with SUM, COUNT, and GROUP BY, and create nested queries using the WITH clause.
The course instructor, Lachezar Arabadzhiev, is a digital marketing technology expert with over four years of experience in performance analytics and data visualization. Lachezar has worked with major brands such as Air Canada, RBC, Kimberly-Clark, Mazda, and HSBC, and is a certified GMP expert and official speaker at the Canadian Google Data & Analytics Summit, 2018.
The course is divided into the following sections: getting started with Google BigQuery, creating and querying tables with SQL in Google BigQuery, exploring semi-structured data with ARRAY_AGG, UNNEST, and STRUCT, combining multiple tables with different JOIN statements, and two projects. The first project involves exploring eCommerce and CRM user-level data in Google Analytics 360, while the second project involves visualizing BigQuery tables and queries in Google Data Studio.
The course has received positive feedback from students who found the materials easy to understand and helpful in mastering the basics of Google BigQuery and its integration with Data Studio. The course has also helped students take their data analysis to the next level, allowing them to combine data from various sources and derive key insights.
The Complete Google BigQuery Masterclass is a course for data scientists and developers who want to master Google BigQuery analytics with SQL. The course teaches students how to analyze real data and apply the SQL queries used in BigQuery to other database management tools, including Oracle, MySQL, PostgreSQL, Microsoft Access, SQLite, and DB2.
The course covers all the important concepts of Google BigQuery, including arrays, UNNEST, STRUCT, CTE, derived tables, and more. Students will also learn all the concepts of SQL in BigQuery, including writing commands like joins, group by, order by, having clause, and subqueries. They will also learn to create tables with partitioning and connect Google BigQuery to Google Data Studio.
Learning is based on a real-time project that helps students apply the concepts in their job, making them job-ready in Google BigQuery. The course also covers BigQuery for Google Analytics.
The course content is clear, concise, and assumes no prior knowledge of Google BigQuery. The instructor has over 15 years of experience as an instructor and more than 10 years of experience in SQL along with BigQuery. Students get lifetime access to the course and all future updates.
The course is suitable for data scientists, data analysts, SQL developers, and anyone who wants to work with other databases like Oracle, MySQL, SQLite, PostgreSQL, etc. It is also helpful for anyone who aspires to become a data scientist, as SQL is a must for data analytics.
The course covers everything step-by-step, making it the best course for absolute beginners. The instructor answers all questions and updates the content regularly. Once students complete the course, they receive a course completion “Google BigQuery Certification” by Udemy.
The course “Practical Google BigQuery for those who already know SQL” is designed for data professionals who want to become BigQuery power users. This course is suitable for data scientists, data engineers, business intelligence specialists, and developers who are unfamiliar with BigQuery and want to learn how to use this powerful tool. Participants who wish to enhance their knowledge of BigQuery can benefit from this course as it provides an in-depth understanding of BigQuery’s functionalities and how to optimize query costs.
The course covers various topics such as accessing Google Sheets over BigQuery, understanding BigQuery’s resources hierarchy, making use of SQL editor shortcuts, and learning about BigQuery’s additional functionalities like Google transfers. Participants will also learn how to access BigQuery from a Python client and how BigQuery works under the hood – how it stores data and performs queries. Additionally, the course covers BigQuery pricing, how to optimize query costs, and how to create a table with advanced options like partitioning, clustering, and retention policy.
The course also teaches participants how to monitor the costs of each query in BigQuery, recover deleted data in BigQuery using SYSTEM TIME, and backup view’s SQL code. The course material includes videos, code snippets, SQL code of examples, cheat sheets, and slides. The course is suitable for anyone who wants to boost their career by using this amazing product, as BigQuery is revolutionizing the data analysis world.
The course content is divided into sections and covers topics such as BigQuery concepts that participants need to understand to use it efficiently, useful tricks, and how to make BigQuery work for them. The course is an excellent opportunity for data professionals to enhance their knowledge of BigQuery and become a power user.
The “Introduction to Google Cloud BigQuery” course, instructed by Dan Sullivan and Daniel Sullivan, is designed for data analysts, engineers, and scientists who want to efficiently work with petabytes of data. The course starts with the basics of signing up for Google Cloud and working with the BigQuery graphical user interface (GUI), introduces SQL for BigQuery, and then moves to loading data and using BigQuery with the command line and Python.
Participants will learn how to explore data, tables, and datasets, write queries efficiently using BigQuery hints and formatting helps, work with SELECT statements, and use features like Saved Queries, Exporting Data, and Execution Details to improve query performance. The course also covers creating tables and data sets, loading data into BigQuery, and using the bq command line utility to query data and work with datasets from the command line.
The course includes quizzes and assignments to check understanding as participants progress through the sections. The course content covers topics like BigQuery architecture, SQL functions, and BigQuery and Python. Participants will also learn about BigQuery’s unique design for building a data warehouse and designing data models, which differs from traditional relational databases like Oracle and SQL Server.
The course is instructed by Dan Sullivan, who has decades of experience in working with data and is the author of books and numerous articles on databases and Google Cloud. His courses can be found on Udemy and LinkedIn Learning. The course is comprehensive and covers a wide range of topics in a structured manner.
Serverless Data Analysis with Big Query on Google’s Cloud is the second course in a series designed to help students attain the Google Certified Data Engineer certification. The course focuses on the role of the data engineer on the Google Cloud Platform, with an emphasis on BigQuery, Google’s fully managed, petabyte scale, low cost enterprise data warehouse for analytics.
Since SQL is a prerequisite for the course, it is mostly lecture-based. However, BigQuery is heavily covered on the exam, so it is important for students to pay attention. The goal of the course and the entire series is to provide students with the foundation of the services needed to pass the Google Certified Data Engineering Exam.
The course highlights the importance of data engineering in a data revolution where data is increasingly recognized as the source of truth. The role of the data engineer becomes extremely important as a bridge between the DBA, developer, and the data consumer. Additionally, most cloud computing vendors are moving towards a serverless architecture, which abstracts users away from servers, infrastructure, and low-level configuration.
BigQuery is serverless, meaning that there is no infrastructure to manage and no need for a database administrator. Students can focus on analyzing data to find meaningful insights using familiar SQL. The course emphasizes the benefits of being a data engineer, including career growth potential, compensation, and the ability to work remotely.
The data revolution is here and the growth of data is insane. Ninety percent of the world’s data has been created in the last two years, and the amount of data collected by all organizations is approximately 2.5 Exabytes a day, doubling every month. Students who take this course will be ahead of the curve in a burgeoning field, with the potential to be the first to be hired and receive top compensation packages.
10. BigQuery ML – Machine Learning in SQL using Google BigQuery by J Garg – Real Time Learning (Udemy)
This course, titled “BigQuery ML – Machine Learning in SQL using Google BigQuery,” is aimed at ML and data engineers who want to create machine learning models in Google Cloud BigQuery using standard SQL. With Big Query ML, users can leverage their existing SQL knowledge to build operational production-grade machine learning models, even if they lack programming language skills like Python or R.
The course provides a brief introduction to various machine learning services of Google Cloud and the fundamentals of BigQuery ML, including the challenges it solves. All machine learning algorithms are explained in two steps: theoretical explanation of how the algorithm works, followed by practical implementation of the algorithm in BigQuery ML. Hands-on examples are provided for each algorithm, along with hyperparameter tuning, model explainability functions, and feature pre-processing functions.
The course covers six machine learning algorithms: linear regression, logistic regression, K-means clustering, boosted tree, deep neural networks, and ARIMA+ time series forecasting. It also covers product component analysis (PCA) and matrix factorization. Additionally, the course teaches best practices and optimization techniques for BigQuery ML.
After completing the course, students will be able to confidently create production-grade machine learning models in real-world corporate projects using BigQuery ML. The instructor is constantly updating the course with new components of BigQuery ML and provides quick answers to questions and queries. Course content is divided into sections, including an introduction to GCP, BigQuery ML basics, linear regression, hyperparameter tuning, model explainability functions, logistic regression, K-means clustering, and more.