How to Access & Query BigQuery Public Datasets

Imagine being able to explore extensive data and improve your SQL skills without the usual concerns about setting up infrastructure. Google BigQuery provides access to public datasets, offering a wealth of real-world information ready for analysis.

Whether you're an aspiring data scientist, a seasoned analyst, or simply want to create some tutorials about big data, accessing and using these public datasets can greatly improve your SQL expertise, skill, and knowledge.

In this guide, we'll show you how to set up your BigQuery environment so you can practice with sample data and sharpen your SQL skills. These datasets offer a practical playground for refining your querying techniques and gaining deeper insights into data analysis.

BigQuery public datasets, provided through Google Cloud's Public Dataset Program, are stored and maintained by Google, allowing free access with Google covering storage costs.

Users only pay for their queries, with the first 1 TB each month free. These datasets can be queried using both #legacySQL and #GoogleSQL, requiring placing fully qualified table names like bigquery-public-data.bbc_news.table_name.

Access methods include the Google Cloud console, the bq command-line tool, and the BigQuery REST API with client libraries in Java, .NET, and Python. Although access is broadly available, restrictions may apply within secured organizational perimeters, and there is no SLA guaranteeing dataset availability.

BigQuery public datasets provide numerous advantages for data scientists, enriching their research and development endeavors with vast, accessible, and diverse data sources.

Here are the key benefits:

1. BigQuery's public datasets span various domains and their data structures, such as genomics, environmental sciences, and social sciences, offering real-world data for practical training.
2. Data scientists gain experience in data preprocessing, feature engineering, and then data visualization if that’s the case, enhancing their analytical skills.

3. Hosted on Google Cloud, these datasets save data scientists time and resources previously spent on data acquisition and storage.
4. BigQuery allows direct querying of these datasets without the need for data transfer, streamlining exploratory data analysis, and model development.
5. A flexible pricing model ensures affordability, charging only for the resources used.

6. BigQuery public datasets are massive, often encompassing billions of rows and extensive terabytes of information, ideal for building complex, high-accuracy models.
7. These extensive datasets enable deep analysis, from studying genetic variations in genomic datasets to exploring celestial phenomena in astronomical data.

8. Public accessibility fosters a global collaborative environment among data scientists, encouraging knowledge sharing and collective problem-solving.
9. This open environment helps data scientists replicate studies, refine techniques, and build upon existing research, accelerating innovation in machine learning.

By utilizing BigQuery public datasets, data scientists advance numerous scientific fields by contributing to and benefiting from a vibrant, cooperative community and improving their technical proficiency.

Accessing BigQuery’s public datasets gives individuals and data analysts a solid starting point for exploring diverse database resources. This guide offers a road map for navigating the Google Cloud Platform and the full power of its extensive database and data resources.

If you haven't yet logged in or don't have access to BigQuery, start by signing in to the Google Cloud Console and selecting BigQuery from the navigation menu.

To create a project in the Google Cloud Console, follow these step-by-step instructions.

Here are the steps to create a cloud project:

Step 1: Go to Menu > IAM & Admin > Create a Project.

Step 2: Name your project and edit the Project ID (Optional).

Step 3: Choose a billing account.

Step 4: Click 'Browse' to select your location.

Step 5: Click 'Create' and wait for setup.

Note: To use certain Google Workspace APIs and features, you may need to enable billing for your Cloud project.

In the Google Cloud Console:

• Go to Billing by navigating to Menu > Billing > My Projects.
• Select the organization associated with your Google Cloud project.
• In the project row, open the Actions menu (more_vert), click Change Billing, and select the appropriate Cloud Billing account.
• Click Set Account to finalize the change.

To utilize BigQuery, log into the Google Cloud Console and select BigQuery. The UI includes an Explorer for managing datasets, a central Workspace for SQL queries, and a results panel. Create, explore, and query datasets using standard SQL. Visualize results directly or with tools like Looker Studio. Manage queries and jobs through the built-in history feature.

Accessing public datasets in BigQuery can provide a wealth of data for analysis and learning. Here’s how to efficiently locate these datasets within the BigQuery interface.

Begin by opening the BigQuery interface in the Google Cloud Console. You'll find the ‘Resources’ section on the left-hand side of the console. This is the starting point for navigating through your BigQuery resources, including your datasets, projects, and other data-related assets. Here, you can get an overview of the datasets available to you, both public and private.

Use the search bar in the ‘Resources’ section to look for specific public datasets. You can enter keywords related to the data you are interested in, such as 'health,' 'economics,' or ‘weather.’ This functionality is particularly useful if you have a clear idea of the data you need, but are unsure where it is located within the vast array of BigQuery's public resources.

If your initial search does not yield the desired results, or if you wish to explore a broader range of datasets, click the 'SEARCH ALL PROJECTS' link. This option expands your search beyond your immediate projects and datasets to include all available public datasets hosted by BigQuery. It's a useful feature for discovering new and relevant datasets across diverse fields.

After expanding your search, look for the project named 'bigquery-public-data.' This project contains a comprehensive collection of public datasets provided and maintained by Google. Clicking on this project will give you access to various datasets, from vast geographical information to detailed transactional data from various industries.

Each dataset within this project is well-documented, allowing you to understand the context and structure of the data before you begin querying.

Exploring BigQuery's resources can be streamlined with an alternative approach, utilizing the interface's functionality to add and explore public datasets directly. This method leverages built-in tools to quickly navigate and directly incorporate a diverse array of datasets into your analysis workspace.

It simplifies the search process and enhances the accessibility of various data types for immediate use and exploration.

Start by clicking the '+ADD' button in the BigQuery interface. This initiates a process that allows you to incorporate new datasets directly into your workspace.

You should now be viewing a screen similar to the one depicted below:

After clicking the '+ADD' button, proceed to the search box and type 'Public.' This action filters the displayed options, making navigating and selecting the 'Public Dataset' category easier.

You should now be able to view a list of all available categories of public datasets:

After filtering, you will be presented with a list of public datasets. Take a moment to browse through these options and select the dataset that best matches your research or analysis objectives. This step is critical as it determines the specific data you will be working with, shaping the direction of your subsequent analysis.

For example, filter the category ‘Datasets for COVID-19 Recearch’ and choose 'About Covid-19 Public Datasets':

Once a dataset is selected, click the 'View Dataset' button. This will lead you to a detailed overview of the dataset, where you can view its comprehensive description, the data it contains, and various metadata elements.

For a deeper dive into the dataset’s structure, click the 'Expand Node' button. This expands the dataset to reveal all the tables and elements it comprises. It allows you to see the relationships and hierarchies within the dataset, aiding in more strategic data manipulation and querying.

Select the specific data table from the expanded dataset view you intend to query or examine. This final step focuses your analysis on a dataset segment tailored to your needs.

By following this alternative approach, you can efficiently locate and access the public datasets within BigQuery, ensuring a smoother and more targeted exploration of the data available.

Querying public datasets in BigQuery allows users to delve into vast data using SQL queries to extract and analyze information relevant to their specific needs. This process is crucial for gaining actionable insights and enhancing analytical skills across various domains.

To start querying, the first step is to access the Query Editor in BigQuery. This is where you will write and manage your SQL queries. You can find the Query Editor by navigating to the BigQuery web interface in the Google Cloud Console. The editor offers a robust environment with tools to help you write, format, and manage your SQL queries effectively.

Once in the Query Editor, composing and executing your query involves writing SQL code tailored to the specific data you want to retrieve or analyze. Start by specifying the dataset and crafting your SQL statement to select, filter, join, or manipulate the data as needed. After composing your query, execute it directly in the editor.

BigQuery processes the query and returns results quickly, even for large datasets. This allows you to see immediate outputs and make iterative adjustments as necessary.

Adopting best practices can significantly enhance the efficiency and effectiveness of your data analysis when using BigQuery datasets, whether public or private.

Here are some key strategies to ensure you get the most out of BigQuery’s capabilities:

1. Structure Your Queries Efficiently: Optimize your SQL queries for performance. This includes selecting only the necessary columns, using appropriate WHERE clauses to filter rows early, and avoiding SELECT * whenever possible.
2. Use Partitioned Tables: Use partitioned tables to manage large datasets more effectively. Partitioning helps reduce the amount of data scanned during queries, thus lowering costs and speeding up analysis.
3. Leverage Caching: BigQuery automatically caches query results for 24 hours. Re-running the same query within this time frame can fetch results from the cache, leading to faster access and reduced costs.
4. Monitor Query Performance: Utilize the BigQuery Query Plan Explanation to understand how your queries are executed. This tool helps identify bottlenecks and optimize query performance.
5. Batch Your Queries: When running multiple queries, especially during off-peak hours, consider batching them to reduce workload and improve resource allocation.
6. Use Cost Controls: Set up cost controls in BigQuery to avoid unexpected charges. This includes setting daily quotas or maximum cost limits per query.
7. Secure Your Data: Implement proper access controls to secure your datasets. This involves setting up the appropriate IAM roles and permissions for different users, and ensuring that sensitive data is encrypted and managed correctly.
8. Integrate with Other Google Cloud Services: Enhance your BigQuery usage by integrating with other Google Cloud services, such as Google Data Studio for visualization or Google Cloud Storage for additional data backup.

By following these best practices, you can maximize the potential of BigQuery for robust, cost-effective, and secure data analysis. Whether you are querying public datasets for exploratory analysis or using private datasets for business intelligence, these strategies will help you optimize your data workflows in BigQuery.

Understanding the use of public datasets in BigQuery is crucial for anyone aiming to sharpen their SQL skills and broaden their analytical expertise. These datasets provide diverse real-world data across multiple sectors, offering both novices and experts a valuable resource for practical learning and advanced data exploration.

For those looking to elevate their analysis and leverage these datasets to their full potential, the OWOX Reports Extension for Google Sheets is an invaluable tool. This add-on integrates seamlessly with BigQuery, enhancing your reporting capabilities and providing deeper insights into the data.

By incorporating the OWOX BI extension into your BigQuery experience, you can enhance the efficiency of your data analysis, gain more detailed and accurate insights, and accelerate your journey from data to decision.

Whether you're just starting or looking to refine your querying prowess, these resources offer significant advantages for personal development and professional growth.