4 min read

How & When to Use Google Cloud BigQuery to Store Your Data

Data offers an endless array of insights, from product performance to changes in the market. Collecting and analyzing data can help you reduce costs, optimize processes, improve customer experience, increase profits, and quickly make well-informed decisions. Data analytics is crucial for businesses of all sizes and industries.

There are countless tools designed to help organizations analyze data. Many companies are turning to cloud-based solutions like Google BigQuery. With BigQuery, you can store, manage, and quickly analyze data in real time. Below, we’ll explain what BigQuery is and how to use it.


What Is Google BigQuery?

Google BigQuery is a serverless, fully managed data warehouse solution offered in the Google Cloud Platform (GCP). As such, it can be integrated with other Google tools and services. This tool has a built-in query engine that can run SQL queries on terabytes of data in seconds. It is designed to query structured data and semi-structured data.

With a serverless architecture, your company can derive valuable insights without having to manage any infrastructure. It is ready to use as soon as you set up your account, with no need for installation or configuration. This allows you to focus on data and decision-making rather than management. Additionally, you only pay for the data being processed and your storage.

BigQuery is most effective for tackling several petabytes of data. Interactive ad-hoc queries of read-only datasets are the best use cases.


What Can You Do With BigQuery?

Cloud data warehouses allow organizations to handle larger datasets than on-premises servers. You can access all kinds of data in real time for rapid analysis.

With BigQuery, you can manage and analyze data using machine learning, business intelligence (BI), geospatial analysis, and other built-in features. BigQuery is built for big data, which is ideal for large and complex analytical SQL queries.

One of the major benefits of a data warehouse is that you can perform many forms of analysis. As an SQL engine, BigQuery can be used with BI tools like Looker Studio, Looker Studio Pro, and Google Workspace suite. Your company can take advantage of powerful reports, visualizations, and analyses.

Google Cloud BigQuery supports both descriptive analytics and predictive analytics. One extension allows the creation of machine learning models for batch predictions. You could train a BigQuery model to make predictions without having to export data — a highly beneficial feature for security and data locality.

BigQuery can use data stored in other Google Cloud services, including Google Drive, Cloud Storage, Spanner, Bigtable, and Cloud SQL. You can load data into BigQuery from many sources. Upload local files, automate data movement with the BigQuery Data Transfer Service (DTS), or enable real-time integration for continuous analytics with Dataflow. DTS also allows you to use third-party tools to transfer data.


How to Use Google BigQuery

BigQuery can be accessed from the Google Cloud console. From there, getting started is simple. Follow this step-by-step guide to see how to analyze your data sets.

  1. Download the Dataset
    Download the latest version of the dataset to your computer. BigQuery supports standard file formats like CSV, JSON, Avro, OCR, and Parquet.

  2. Upload and Store Your Dataset in Google BigQuery

    You will find BigQuery in the left side menu of the Google Cloud Platform console under Big Data. Click Create dataset on the right-hand panel. Create a unique Dataset ID and set a geographical location for data storage and processing.

    On your newly created dataset, select Create table. Choose the source method and file format. Select the dataset file from your computer and decide on a table name. For the schema, check the Auto detect option.
  3. Query Data Stored in BigQuery

    You can now begin querying your BigQuery dataset in standard SQL. With BigQuery’s robust and comprehensive SQL capabilities, you can use fairly advanced queries.

The BigQuery navigation menu also carries a variety of helpful administration, analysis, and migration tools.

  • SQL workspace: This section contains your datasets, tables, and other resources. In most cases, you will spend most of your time here. This is where you create tables, run BigQuery queries, view your history, and do other common BigQuery activities.
  • Data transfers: Use this to open the BigQuery Data Transfer API page.
  • Scheduled queries: View your scheduled queries.
  • Analytics hub: This section shows you the data exchanges you can access in your Cloud project.
  • SQL translation: You can use this page to convert Teradata SQL queries to use in BigQuery.
  • Capacity Management: This area displays slot commitments, reservations, and reservation assignments.
  • BI Engine: This takes you to the BI Engine page.


How Promevo Can Help

Promevo can help you develop your data strategy and integrate powerful business intelligence tools like BigQuery for enhanced performance. We offer personalized guidance and expertise based on your company’s needs, so you can optimize your experience with the Cloud.

When you work with Promevo, you can leverage the full potential of Google. We are 100% Google-focused, meaning we offer unmatched expertise, commitment, and customer service.

Promevo can help you reinvent the way your team works with Google Workspace management solutions, Google Cloud solutions, and Chrome device solutions. We’ll be your guide no matter where you are on your Cloud journey, from Cloud migration to identifying opportunities to optimize Cloud usage. 

As a customer, you’ll have access to free support and training from our Google certified specialists. When you’re ready to get started, contact us to speak with an advisor.


Frequently Asked Questions

Is BigQuery in Google Cloud?

You can find BigQuery in the GCP console. Check the left side menu — BigQuery should be under Big Data. You can also enter https://console.cloud.google.com/bigquery in your browser search bar.

What is the difference between Google Cloud Storage and BigQuery?

Google Cloud Storage (GCS) is a flexible storage service that allows you to save, share, copy, delete, and access data instantly. Google BigQuery is a data warehouse solution and SQL engine. The primary function of BigQuery is to run and analyze Cloud SQL queries to derive insights from data. You can import data from Google Cloud Storage and store datasets, but you would not use BigQuery to store images, videos, and similar files.

When would you use BigQuery instead of Cloud SQL?

Google Cloud SQL is a fully managed relational database that supports MySQL, SQL Server, and PostgreSQL. You can easily migrate existing database systems to the Google Cloud SQL system, create tables, and make simple queries.

BigQuery is great for querying large datasets with almost immediate results. If you are dealing with extensive datasets and looking for rapid data analysis, BigQuery is the best option.

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