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Yes—BigQuery Sandbox lets you learn SQL and query public datasets without adding a credit card or billing account to the Sandbox project. It includes up to 1 TiB of query data processed and 10 GB of active storage per month, but it is not a permanent free production environment: tables, views, and partitions you create in Sandbox expire after 60 days. This guide walks through setup, dataset discovery, safe queries, cost controls, and the point at which you may need a billed project.
What BigQuery is—and what “Sandbox” means
BigQuery is Google Cloud’s managed analytics data warehouse. It lets you analyze data with SQL without managing database servers. A useful way to picture its structure is:
Project → dataset → table → rows and columns
- Project: The Google Cloud container that organizes resources and associates usage with a project.
- Dataset: A container for tables and views.
- Table: Structured data with a schema—columns, their types, and rows.
- Query job: A SQL statement submitted to BigQuery for execution.
- Public dataset: Data made available to users through Google’s public dataset program.
BigQuery Sandbox is a restricted way to use BigQuery without a billing account attached to the project. It is intended for learning and limited evaluation, not as a permanent home for production data. Google documents a 10 GB active-storage allowance and 1 TiB of processed query data per month for the Sandbox; standard BigQuery quotas and limits still apply. User-created tables, views, and partitions automatically expire after 60 days. See the Sandbox documentation for current terms and limitations.
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Three different meanings of “free”
These options are related but not interchangeable:
- Sandbox: A no-billing-account learning environment with the limits and expiration rules above.
- BigQuery free usage tier: Monthly free usage that can also apply to a project with billing enabled, according to Google’s pricing rules. Billing being enabled means usage beyond free allowances or other billable services can incur charges.
- Google Cloud free trial: Promotional credits for eligible new customers. This is a separate offer, with its own eligibility, verification, and terms; it is not required for Sandbox. Check Google Cloud’s current offer rather than assuming eligibility.
Public data is not automatically unlimited free compute. The dataset owner generally pays for hosting the public data, while query processing is associated with the project running the query and its applicable usage tier.
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Before you start
You need a Google account and access to the Google Cloud Console. You also need a project: create one if your account allows it, or select an existing project you are authorized to use. Basic familiarity with SELECT, FROM, WHERE, GROUP BY, and ORDER BY will help, but you can learn as you go.
On a school or workplace account, organization policies, permissions, or security controls may prevent project creation or public-dataset access. If you cannot create a project, selecting an authorized existing project may work; otherwise ask your administrator. Google notes that project creation requires the appropriate permission, such as Project Creator access.
Open BigQuery Sandbox
- Sign in to Google Cloud Console and open BigQuery. You can also find it through the product navigation.
- Use the project selector at the top of the console. Create a project for experiments if permitted, or choose an existing project intended for this work.
- If your goal is to remain in Sandbox, do not attach or enable a billing account for that project. Google’s quickstarts explain that BigQuery tutorials can be followed through Sandbox without a credit card. If billing is already attached and you want to use Sandbox, consult Google’s instructions for disabling billing.
- Open BigQuery Studio. In the Explorer panel, expand the project and browse available datasets, or use Google’s public-data resources to find a dataset.
- Expand a dataset, select a table, and inspect its schema before writing a query. The console’s layout and button labels can change, but the core flow—project, Explorer, dataset, table, query editor—remains the useful mental map.
Double-check which project is selected before running jobs, and do not disable billing on a project that is supporting unrelated services without understanding the consequences. Sandbox is about the selected BigQuery project; it does not make other Google Cloud resources or exports free.
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Discover datasets in BigQuery Explorer or browse Google’s public datasets and Cloud Marketplace listings. Before querying, check the dataset and table descriptions, provider, column names and data types, location, update frequency, licensing or attribution terms, and whether the table is partitioned. Do not assume that a marketplace page’s “Last Updated” date is the refresh date of the underlying data. Also check that the data is current enough for your question and appropriate to use.
Public tables commonly use a fully qualified name in this form:
`project.dataset.table`For example, the project portion often appears as bigquery-public-data. Use GoogleSQL for new work; it is BigQuery’s recommended SQL dialect for this tutorial. The full identifier should be enclosed in backticks, as in the examples below.
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Run a first query
In the query editor, try this template after replacing the dataset and table placeholders with names from Explorer:
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FROM `bigquery-public-data.DATASET.TABLE`
LIMIT 10;This is a convenient first look at returned rows, but it is not an ideal habit for larger tables. LIMIT 10 limits the number of rows returned; it does not necessarily reduce the amount of data BigQuery scans. With SELECT *, BigQuery may read every column even though it returns only ten rows.
After checking the schema, select only the columns you need:
SELECT
column_a,
column_b,
column_c
FROM `bigquery-public-data.DATASET.TABLE`
LIMIT 100;Replace the placeholder columns with actual names shown in the table schema. Before clicking Run, review the query validator’s estimate of bytes processed. A query against a Google-hosted public table still uses query-processing capacity associated with your project.
Useful queries for exploring a table
These examples are templates. Substitute the real table and column names, and check the column types in the schema first.
Count rows
SELECT COUNT(*) AS row_count
FROM `bigquery-public-data.DATASET.TABLE`;A count can still require substantial processing, depending on the table and available metadata. Check the estimate before running it.
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Summarize records over a date range
SELECT
date_column,
COUNT(*) AS records
FROM `bigquery-public-data.DATASET.TABLE`
WHERE date_column >= DATE '2024-01-01'
GROUP BY date_column
ORDER BY date_column;The filter shown is for a DATE column. If the field is a DATETIME, TIMESTAMP, or string, its comparison and conversion syntax may differ. For a partitioned table, filtering on the partitioning column can reduce the amount scanned.
Find the most frequent categories
SELECT
category_column,
COUNT(*) AS records
FROM `bigquery-public-data.DATASET.TABLE`
WHERE category_column IS NOT NULL
GROUP BY category_column
ORDER BY records DESC
LIMIT 20;Compare numeric values by category
SELECT
category_column,
AVG(numeric_column) AS average_value,
MIN(numeric_column) AS minimum_value,
MAX(numeric_column) AS maximum_value
FROM `bigquery-public-data.DATASET.TABLE`
WHERE numeric_column IS NOT NULL
GROUP BY category_column
ORDER BY average_value DESC;Check for missing values
SELECT
COUNTIF(column_name IS NULL) AS null_count,
COUNT(*) AS total_rows
FROM `bigquery-public-data.DATASET.TABLE`;These aggregations are useful for learning a dataset’s shape, but they are not inherently cheap. Use BigQuery’s estimate and cost-control guidance before submitting queries over large tables.
Keep queries within safe limits
- Read only needed columns. Replace
SELECT *with a short column list after the initial inspection. - Check the estimate first. The console validator estimates bytes processed. A dry run or equivalent estimate is useful before executing a query.
- Filter early and narrowly. Use a selective date range and, where applicable, filter on partitioning columns.
- Do not rely on LIMIT to cap scanned data. It restricts returned rows, not necessarily bytes read.
- Avoid repeatedly launching expensive exploratory queries. Refine the query and review its estimate before rerunning it.
- Use cache appropriately. BigQuery may reuse cached results in eligible cases, but cache behavior is not a substitute for understanding scanned data or controlling query size.
- Use a maximum-bytes-billed limit when available. Supported interfaces can reject a query when its estimated processing exceeds your chosen ceiling.
For example, with the bq command-line tool, a query can specify a maximum:
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bq query
--use_legacy_sql=false
--maximum_bytes_billed=1000000000
'SELECT COUNT(*) FROM `bigquery-public-data.DATASET.TABLE`'This sets a ceiling of 1,000,000,000 bytes; if estimated processing exceeds it, the query fails rather than running. Check the current cost-control documentation and CLI quickstart for supported options in your workflow.
The on-demand pricing page currently lists the first 1 TiB of query processing per month as free and displays $6.25 per TiB above that tier for the shown USD pricing model. That figure is not universal: pricing can vary by service, region, account, and pricing model, and may change. Check live pricing before enabling billing or running a larger workload. A billing alert can notify you, but it is not a hard stop; estimates, maximum-bytes-billed limits, and applicable quotas are more direct query safeguards.
Know what Sandbox keeps—and what it does not
Simply querying a Google-hosted public table does not create a copy of that source table in your project. But creating a table from query results does create user data in your project. In Sandbox, created tables, views, and partitions are subject to the 60-day expiration, as well as the 10 GB active-storage limit. Exporting results elsewhere involves a separate destination, with its own permissions, limits, and possible charges.
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Sandbox is a poor fit for durable application data, production dashboards that depend on user-created tables, or workloads needing unrestricted feature access. It also does not remove general BigQuery quotas and system limits; consult the current quotas and limits page if a query is rejected for size, resources, or rate.
Location and access can matter
BigQuery datasets have a location chosen when they are created, and that location cannot later be changed. Tables referenced in a query must be in compatible locations. This becomes relevant when joining a public table to your own table, materializing results into a destination dataset, or using external sources. Check the source dataset’s location before creating your own destination. Google also notes that public datasets are not accessible by default from within a VPC Service Controls perimeter.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common problems and fixes
“I can’t create a project”
Your account may lack project-creation permission, or a school or company policy may block it. Confirm that you are using the intended Google account, choose an existing project you are authorized to use, or ask an administrator. A personal account may be an option where appropriate.
“The console is asking me for billing”
You may be following a billed-project setup rather than the Sandbox path. Return to Google’s Sandbox instructions and confirm the selected project. If a billing account is already attached, decide whether you want Sandbox and follow the documented process for that project. Do not assume a billing change affects only BigQuery; check for other services using the project.
“Not found: Table…”
Check spelling, the selected project, and the complete project.dataset.table identifier. Copy the name from Explorer and wrap it in backticks. Confirm that the dataset still exists and check its location; a location mismatch can produce confusing errors. If your organization restricts public-data access, you may need an administrator’s help.
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The project may not permit query jobs, your account may lack the required IAM role, or an organization security policy may block access. Permissions differ depending on whether you are merely querying a public table or creating datasets and tables. Ask the project administrator to grant the minimum permissions needed for your task.
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“The table I created disappeared”
In Sandbox, user-created tables, views, and partitions expire after 60 days. Recreate the result or move to a project and storage setup appropriate for data that must persist.
“The estimate is large even with LIMIT”
That is expected when the query still reads many columns or scans a large table. Select specific columns and filter the data, especially by a partition column where one is available.
“The query is too large or too CPU-intensive”
Reduce the columns scanned, narrow filters, simplify joins, or break the work into smaller stages. If the workflow permits, create a smaller intermediate table—but remember Sandbox storage and expiration limits. Check Google’s current quotas page; some quotas may be adjustable, while fixed system limits are not.
When to move beyond Sandbox
Stay in Sandbox if you are learning SQL, browsing public tables, and running short experiments within the documented limits. Consider a normal project with billing enabled when you need persistent tables, scheduled jobs, production dashboards, larger workloads, team or application access, or features unavailable in Sandbox. Billing does not mean every query immediately costs money—the free usage tier may apply—but you should set appropriate controls and review current pricing before using a billed project.
If you only need public-data SQL practice, Sandbox is usually the simpler starting point. If you later need a visualization, Looker Studio is an adjacent option to explore; it is not required to learn BigQuery. For a command-line next step, Google’s Cloud Shell and bq quickstart provides a path beyond the console without making terminal use a prerequisite.
Optional: try a query from the command line
Cloud Shell includes the Google Cloud CLI and bq tool. This is optional; the console is easier for a first query because command-line use adds project selection, authentication, and shell quoting considerations.
bq query
--use_legacy_sql=false
'SELECT 1 AS example'To query a public table, use the same fully qualified table name and GoogleSQL flag:
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bq query
--use_legacy_sql=false
'SELECT *
FROM `bigquery-public-data.DATASET.TABLE`
LIMIT 10'For your first few queries, use the console’s estimate and select only the columns you need rather than treating the row limit as a cost control.
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