At Discover WebTech, we often receive questions from beginners who are just starting with Google BigQuery and find themselves overwhelmed by its bigquery data types. BigQuery is a powerful tool for analyzing large datasets, but to use it effectively, one must understand how it stores and interprets data.
In this beginner’s guide, we’ll walk you through the fundamentals of bigquery data types, share simplified bigquery examples, and explain how to perform bigquery data type conversion or manage bigquery change data type operations with ease.
What Are BigQuery Data Types?
In BigQuery, data types define how information is stored and processed. Selecting the right data type is important for accuracy, performance, and cost-effectiveness. For example, using a “number” data type instead of “text” for values like age or price makes calculations easier and more efficient.
When working with structured data, choosing appropriate bigquery data types helps prevent errors, optimize queries, and improve data storage practices.
Common BigQuery Data Types (With Simplified Examples)
Let’s explore the most common bigquery data types available in BigQuery, along with how we typically use them:
Numeric Types
- INT64: Used for whole numbers like ID numbers or quantities.
- FLOAT64: Ideal for decimal values such as ratings or percentages.
- NUMERIC / BIGNUMERIC: Used when we need high precision—like for financial transactions or tax calculations.
Text and Binary Types
- STRING: Used for any kind of text such as names, email addresses, or product descriptions.
- BYTES: Used for binary data like images or encrypted content.
Boolean and Temporal Types
- BOOL: Stores TRUE or FALSE values—for example, whether a user is active.
- DATE / DATETIME / TIME / TIMESTAMP: These types help us store calendar dates, specific times, or full timestamps (including time zones).
Complex Types
- ARRAY: Allows storing multiple values in a single field. For instance, a user’s multiple phone numbers.
- STRUCT: Groups related fields together—for example, a customer’s name, address, and contact in one unit.
- GEOGRAPHY: Helps store location-based data like coordinates.
Using the correct bigquery data types ensures cleaner queries and better results. For example, storing a customer’s address as a group of fields (street, city, zip) is better handled using STRUCT, while a list of purchased products is suited for an ARRAY.
BigQuery Data Type Conversion
Why and When We Convert Data Types
During data import or when cleaning data, we often find the need to convert one data type into another. For instance, if a date is stored as text, we’ll need to convert it into a DATE type for accurate filtering and analysis. This process is known as bigquery data type conversion.
How We Do It
In BigQuery, we can convert data types using specific functions. These functions help transform a value from one type to another, such as from a string to a number or from text to a timestamp.
When the data is clean, standard conversion works smoothly. However, when the data contains errors or unexpected formats, we use safer conversion methods that avoid crashing the query and instead return empty or null values for problematic rows.
For example, if we try to convert the word “hello” into a number, the safe conversion method will mark it as invalid, while the regular method will result in an error.
How to Change Data Types in BigQuery
Unlike traditional databases, BigQuery doesn’t allow us to directly change the data type of an existing column. Instead, here are two common approaches we use when we need a bigquery change data type solution:
1. Create a New Table with Correct Data Types
We often create a new version of the table where each column is converted into the appropriate data type. This method allows us to keep the original data safe while working with a clean, correctly typed version.
For example, if a column called “Price” was stored as text, we create a new table where that column is converted into a numeric type. We do this for multiple columns as needed.
2. Use Views for Dynamic Conversion
In cases where we don’t want to create new tables, we create a “view” that shows the data with the corrected types. This is like creating a virtual table that formats the data as needed without actually changing the original source.
This is a flexible method for temporary use or testing purposes.
Best Practices We Follow for BigQuery Data Types
Over time, we’ve identified some best practices that help us make the most out of bigquery data types:
Choose the Right Type for Each Column
Always use the simplest and most appropriate data type. For example, store numbers as numbers, not text. This reduces processing time and improves query performance.
Use Date Types for Time-Based Data
When storing order dates or signup dates, we always use the DATE or TIMESTAMP type instead of plain text. This makes it easier to filter records by month, day, or year.
Save Space and Cost with Efficient Types
BigQuery charges based on the amount of data processed. Smaller data types take up less space and result in faster queries. For example, using an INT64 for a number is much lighter than using a STRING.
Group Data Using STRUCT and ARRAY
Instead of creating multiple separate fields or tables, we group related data using STRUCT or ARRAY. This keeps our data cleaner and more organized, especially in reporting dashboards.
Handle Errors Gracefully
When performing a bigquery data type conversion, we always validate the results. Using safer conversion functions helps us catch invalid records without breaking the entire process.
Simplified BigQuery Examples
Here are some common situations where we apply our knowledge of bigquery data types in real projects:
Converting Text to Numbers
If we receive data where numbers are saved as text (e.g., “100”), we convert them into actual numbers. This helps us perform calculations like totals or averages. This is a typical case of bigquery data type conversion.
Structuring User Information
Instead of storing user information across many separate fields, we group them together. For example, we keep name, address, and phone numbers in a single field using logic similar to STRUCT and ARRAY, which simplifies queries.
Preparing Data for Analysis
Before running reports, we ensure all date fields are correctly formatted as DATE or TIMESTAMP, and that any currency values are stored using numeric types with the required precision.
These are some real-world bigquery examples where the correct use of data types leads to cleaner reports and faster insights.
Final Thoughts
At Discover WebTech, we believe that understanding bigquery data types is fundamental for anyone working with data in Google Cloud. Whether you’re building dashboards, cleaning imported datasets, or optimizing your queries, choosing the right data type and converting it correctly can make all the difference.
By following the practices shared above—like using efficient types, converting data safely, and leveraging advanced structures—you can confidently manage bigquery change data type tasks and perform seamless bigquery data type conversion.
As your skills grow, you’ll find that mastering data types is not just a beginner’s task—it’s a habit that powers better analytics at every level.
If you’re looking for expert help in building clean, scalable BigQuery projects or need personalized guidance on bigquery data types, we at Discover WebTech are here to support your journey.
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