BigQuery Tutorial

The knowledge of Cloud Computing has become a requirement across the data science job spectrum. Every role from "data analyst" to "data engineer" is expected to have a basic knowledge of cloud computing. Along with Microsoft and Amazon Web Services (AWS), Google Cloud Platform (GCP) is one of the most popular cloud platforms. Mastering the GCP tools, especially SQL engines like BigQuery, is critical when it comes to beginning or progressing in a data-oriented career.

Unlike localized SQL tools like MySQL or Postgre, Google BigQuery uses the power of cloud computing to allow users to interact with and seamlessly scale large volumes of data.BigQuerys SQL dialect has some quirks that distinguish it from legacy dialects like PostgreSQL, however understanding both how to write efficient queries and learning whats happening "under the hood" will allow a BigQuery user to quickly gain proficiency.

About the BigQuery Tutorial

The objective of this tutorial is to get the readers acquainted with the basic concepts of BigQuery through the use of BigQuery Studio, BigQuerys SQL engine, and other external Google Cloud integrations. The tutorial covers everything from initial setup to creation of datasets and tables to creating and running complex SQL scripts.

Aside from hands-on SQL concepts, learners will better understand the architecture and design that lies "under the hood" of BigQuery and how this design enables users to create, query and manipulate large datasets.

The tutorial will also include a discussion surrounding the business use cases and relevance of BigQuery as a viable candidate for a data warehousing solution.

Who Should Use the BigQuery Tutorial?

The BigQuery tutorial is intended to be accessible to a broad spectrum of learners. This audience will include data analysts, data scientists, data engineers, software engineers and business leaders who utilize data and SQL engines in their work.

Developers who hope to gain a deeper understanding of cloud computing in conjunction with SQL can also benefit from this tutorial. While this tutorial is aimed at beginners, it can also enhance the understanding of intermediate users and working professionals alike.

The average reader experience level will vary but generally those who will benefit most from this tutorial will be students, interns or junior developers.

Prerequisites for Learning BigQuery

For this tutorial, we assume you have a baseline knowledge of SQL, cloud computing and data analytics.

Although SQL is featured prominently in this tutorial, the following chapters are intended to cover BigQuery as a BI tool and do not specifically teach SQL. Therefore, a basic knowledge of SQL is a prerequisite for this tutorial and for learning BigQuery in general.

Even with SQL knowledge, however, its important to note that BigQuery has its own SQL dialect and functions and syntax may differ. Since BigQuery is an application on Google Cloud Platform, it is strongly suggested that anyone learning BigQuery be familiar with or experienced in cloud computing concepts.

FAQs on BigQuery

In this section, we have collected a set of Frequently Asked Questions on BigQuery followed by their answers −

1. What is Google BigQuery?

Google BigQuery is Google Cloud Platforms serverless SQL engine and data warehousing solution. It is mainly accessible by using BigQuery Studio in the Google Cloud Console.

Through various methods, BigQuery allows users to query, create and manipulate datasets instantly using serverless cloud infrastructure. Consequently, students, professionals and organizations gain the ability to store and analyze data at a nearly infinite scale.

2. Why should you use BigQuery?

SQL and cloud computing are two of the most in-demand and marketable skills for beginning data scientists, data engineers, data analysts and software developers.

Google Cloud is one of the largest and most recognizable cloud vendors in the world. Knowledge of BigQuery SQL can help a beginning developer learn or refine skills to land jobs and help build enterprise-scale data infrastructure.

Business leaders should strongly consider BigQuery as a viable option for building and improving existing data infrastructure; this is especially true for companies that aspire to migrate from on-premises (on-prem) set ups to cloud infrastructure.

3. What are the main features of BigQuery?

BigQuerys main feature is its SQL environment, BigQuery Studio. BigQuerys features also include integrations with existing products like Google Sheets, Google Cloud Storage, the gcloud command line interface (CLI) tool and the BigQuery API.

BigQuery also includes services for automatically transferring data from upstream Google Cloud sources through BigQuery Data Transfer Service. BigQuery allows for the creation and facilitation of scheduled queries, the creation of views and conversion of views to materialized views.

4. What is the best way to learn BigQuery for beginners?

The best way for beginners to learn BigQuery is through hands-on experience, like this tutorial illustrates and encourages. For BigQuery beginners, Google Cloud Platform provides access to public datasets and offers an initial 3 month trial period for users considering the platform.

Google Cloud Platform has also created learning resources for beginners interested in learning BigQuery. These learning resources include Google Qwiklabs, hands-on learning exercises, and extensive documentation on BigQuery processes and syntax.

5. How does BigQuery store data?

BigQuery is a SQL database that stores data in a structured manner (as opposed to unstructured data). BigQuery is a columnar data store, meaning that data is stored in columns that users can then access, manipulate and add/delete as they see fit.

BigQuerys data storage also allows developers to store data in partitions, which are "sections" of data divided by a field like a date.

6. How does BigQuery handle security?

BigQuery integrates with existing Google Cloud Platform features to secure data. With data privacy becoming an increasingly important topic in the field of data science, BigQuery provides several ways for users to secure sensitive data.

BigQuery allows users to set policy tags to indicate if a particular field (column) contains personally identifiable information (PII). BigQuery also allows project owners to assign permissions and roles that can limit access to potentially sensitive data.

7. Do I need to know Google Cloud Storage (GCS) when working with BigQuery?

Knowing Google Cloud Storage (GCS) is not a prerequisite when working with data stored in BigQuery. However, understanding the principles of cloud storage and the specifics and limitations of external storage can be helpful in understanding the "inner workings" of BigQuery.

Additionally, knowing Google Cloud Storage can enable users to better and more seamlessly integrate data stored in Cloud Storage with BigQuery. Understanding Cloud Storage can help developers more quickly understand how to connect Google Sheets and other external integrations with BigQuery.