Essential BigQuery Data Types You'll Ever Need: A Complete Guide

BigQuery is a powerful tool for data analysis, but using it effectively hinges on understanding how to store your data. This article will guide you through the essential data types BigQuery offers, explaining what each type is used for and how to choose the right one for your analysis

Whether you're a digital analyst, BI specialist, or data engineer, understanding these data types is essential for designing effective datasets, optimizing query performance, and making informed data-driven decisions.

In data analysis, it's important to understand the different data types available in BigQuery for effective data manipulation and querying. BigQuery, Google's fully managed, serverless data warehouse, has a versatile set of data types designed to manage a variety of data formats and structures. By mastering these data types, analysts can achieve better performance and accuracy in their work.

In traditional SQL environments, users often struggle with rigid schema requirements and complex syntax, making it tough for those who aren't database management experts. Google BigQuery makes things easier by letting you specify schemas on the fly when loading data and simplifying SQL queries.

This flexibility, along with BigQuery's powerful analytics and a wide range of data types, lets users focus on extracting insights flexibly from data rather than getting bogged down by database management. BigQuery also performs exceptionally well, efficiently handling vast datasets thanks to its serverless architecture that scales seamlessly with user needs.

The top BigQuery data types are essential building blocks for data handling and analysis. Understanding these data types is crucial for digital analysts, BI specialists, and data engineers.

These data types include NUMERIC, BOOLEAN, STRING, STRUCT, ARRAY, BYTES, GEOGRAPHY, TIME, DATE, DATETIME, TIMESTAMP, and JSON. Each data type has specific uses, limitations, and best practices, empowering users to design efficient datasets, optimize query performance, and confidently make data-driven decisions.

Mastering these data types ensures effective data modeling, analysis, and schema optimization, enhancing overall data analytics capabilities in BigQuery.

In BigQuery, the NUMERIC data type is designed to store precise numerical values. This type is crucial for applications like financial calculations where precision is essential. It can represent numbers from -10^38 +1 to 10^38 -1, with a precision of up to nine decimal places. By using the NUMERIC type, you can maintain high accuracy in your data, ensuring consistent and reliable results in computations that require exact values.

The hierarchy of values, starting from the least valuable, is as follows:

• NULL
• NaN
• -inf
• Negative numbers
• 0
• Positive numbers
• +inf

Numeric data can be classified into four specific sub-types. These sub-types cater to different needs in handling numerical data across various applications.

1. Integer type (INT64)
2. Numeric type (NUMERIC, DECIMAL)
3. Bignumeric type (BIGNUMERIC, BIGDECIMAL)
4. Floating point type (FLOAT64)

Integers have a range from \(-2^{63}\) to \(2^{63} - 1\), which means they can vary from \(-9,223,372,036,854,775,808\) to \(9,223,372,036,854,775,807\). This defines the maximum capacity for a 64-bit integer. These values are strictly whole numbers, without any decimal or fractional parts.

Syntax of INT64

• INT64: This type consists of whole numbers within the range of -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807.

Example of INT64: Suppose you want the string '12345' to be converted to a 64-bit integer. The output column is labeled integer_output.

Here:

• '12345': This is the input expression, a string literal containing the number 12345. Even though it's in quotes, indicating a string, it represents a numeric value that can be converted.
• AS INT64: This specifies the target data type for the conversion. INT64 is a data type representing a 64-bit integer. The AS keyword is used in SQL to define aliases and to designate conversion types in the CAST function.
• AS integer_output: This is an alias for the column in the resulting data set. By using AS integer_output, the column that would normally be labeled as CAST('12345' AS INT64) in the output is instead labeled simply as integer_output.

NUMERIC and BIGNUMERIC types store exact numeric values with precision and scale. Precision refers to the total number of digits stored, whereas scale specifies the number of digits after the decimal point.

NUMERIC is a good choice for balancing storage space and accuracy. It can hold numbers up to 38 digits long, with 8 decimals. This makes it suitable for financial data or scientific measurements where specific decimal places matter.

For situations requiring even higher precision, BigQuery offers BIGNUMERIC. It can handle mind-boggling numbers with up to 76.76 digits (almost 77!), with 38 of them being decimals. While this comes at the cost of more storage, BIGNUMERIC is ideal for tasks like complex financial modeling or astronomical data analysis, where extremely accurate calculations are essential.

Syntax for NUMERIC:

Syntax for BIGNUMERIC:

Example: A large multinational corporation conducts an end-of-year financial review to analyze key financial metrics involving large figures, like total annual revenue and long-term liabilities, often stored in scientific notation for precision. Financial analysts use SQL queries to convert these figures from scientific notation into NUMERIC and BIGNUMERIC forms for easier analysis and reporting, fetching the data from a corporate database.

Here:

• ''5.2E11'' : String representations of numbers in scientific notation, where '5.2E11' stands for \(5.2 \times 10^{11}\). 'NUMERIC' is the data type to which the expression is being converted
• ''5.2E37'': String representations of number in scientific notation, where '5.2E37' stands for \(5.2 \times 10^{37}\). 'BIGNUMERIC' is the data type to which the expression is converted.
• AS total_revenue: Alias used to rename the result column for the output of the ‘CAST’ operation, ‘total_revenue’ corresponds to the ‘NUMERIC’ conversion result
• AS total_liabilities: Alias is used to rename the result column 'total_liabilities' to the ‘BIGNUMERIC’ conversion result.

FLOAT64 represents 64-bit IEEE binary floating-point numbers. Suitable for scientific calculations requiring large ranges but not precise decimal accuracy.

Syntax of FLOAT64

• FLOAT64: This type can store numbers with fractional parts, suitable for scientific calculations.

Example of FLOAT64

Here:

• 5.4321: The string which is being converted.
• AS FLOAT64: The NUMERIC type to which the string is converted. It demonstrates how a numeric value with a decimal can be represented in SQL.

When working with BigQuery, choosing between NUMERIC and FLOAT 64 data types depends on the nature of your data and the precision required. The following details help to select the data type based on the desired result.

The BOOLEAN data type in BigQuery represents true or false values. It is essential for logical operations and decision-making in queries. Here’s a and detailed look at the BOOLEAN data type, including its syntax, examples, and logical and conditional operations.

• BOOLEAN: It can store three possible values: TRUE, FALSE, or NULL.

Suppose a company conducts an online survey asking customers whether they are satisfied with the service provided. The survey responses are initially recorded as strings "true" for satisfied and "false" for not satisfied. The company's database stores these responses as strings, and the data analysis team needs to convert them into boolean values for easier aggregation and analysis.

Here:

• CAST(expression AS BOOL): CAST is a function used in SQL to change the data type of an expression to another specified type.
• Expression 'false': This string literally represents the survey response. In this context, it indicates dissatisfaction.
• AS BOOL: This directs SQL to convert the false string into a boolean value. The boolean data type only supports two values: TRUE and FALSE.
• AS customer_satisfaction: This part of the query assigns an alias to the output column. Instead of getting a column named generic like CAST('false' AS BOOL), it will simply be labeled as customer_satisfaction. This clarifies what the data represents in result sets and is particularly useful for reports and data visualizations.

Strings in BigQuery are sequences of Unicode characters with variable lengths, essential for data manipulation tasks. BigQuery provides a robust set of built-in functions for string manipulation.

Strings must be quoted using single(‘), double(“), or triple quotation(‘‘‘) marks. Alternatively, strings can also be enclosed in triple quotes, using three single (''') or three double (""") quotation marks. Key functions supported for strings in BigQuery include casting, coercion, concatenation, and regular expressions.

• expression: This is the value or field that you want to convert. It can be a constant, a variable, or a column from a table.
• AS STRING: This specifies that the expression should be converted into a STRING data type.
• AS alias: This optional part assigns a more readable or meaningful name to the result of the expression in the output. This name is used as the column header in the result set.

Suppose a retail company manages an inventory database storing product IDs as integers. For a specific report, the product IDs must be displayed as part of a text message or concatenated with other strings, requiring them to be in a STRING format.

Here:

• product_id: This is the column being converted. It typically contains integer values that uniquely identify each product in the inventory.
• AS STRING: This function converts the integer product IDs into string format, enabling them to be used in operations that require text handling, such as concatenation.
• AS string_product_id: This alias renames the output column to string_product_id, making it clear that this column now contains product IDs in string format.
• FROM inventory: Specifies the table named inventory from which to retrieve the product_id.

STRUCT represents a container of ordered data of any type. Data types can be freely mixed within a STRUCT. It’s useful for representing complex data structures within a single field, allowing for organized and hierarchical data storage.

One of the key benefits is its flexibility. It can contain various data types and even other STRUCTs, making it a powerful tool for managing complex datasets in BigQuery.

When defining a STRUCT type, the elements' types are specified using angle brackets (< and >). Here are some examples of STRUCT types:

• STRUCT <INT64>: A simple STRUCT containing only integers.

• STRUCT<123 STRUCT<4 INT64, 5 INT64>>: This example demonstrates a nested STRUCT. Here, a STRUCT named ‘123’ with two integer fields named ‘4’ and ‘5’ is nested within another STRUCT.

• STRUCT<123 STRUCT<4 INT64, 5 INT64>>: A STRUCT that includes an ARRAY named 'some_array' consisting of integers.

• STRUCT<NULL>: A STRUCT that only contains NULL values.

STRUCTs are versatile as they can encapsulate various Google BigQuery data types and can integrate different types into a single structure. They are particularly useful for representing complex data records.

• CAST: A function used to convert the data type of the expression into another specified data type.

• (value1, value2, ...): A tuple of values which are the elements of the struct. These values can be literals, expressions, or column names.

• AS STRUCT<Type1, Type2, ...>: Specifies that the tuple should be converted into a STRUCT data type with specified field types for each element in the tuple.

• AS alias: This part of the query assigns an alias to the output column for easier reference in the result set.
  

Suppose a human resources department manages data involving employee details that must be grouped for certain operations, such as generating comprehensive reports or logs. Each employee has an ID and a name that are frequently accessed together.

Here:

• 12345, 'John Doe': These values are cast into a struct. 12345 is an employee ID, and 'John Doe' is the employee's name.

• AS STRUCT<INT64, STRING>: Converts these values into a structured data type with two fields: an INT64 for the integer ID and a STRING for the name.

• AS employee_info: This alias names the resulting struct column employee_info, making it clear that this column contains structured information about an employee.
  

ARRAY data types in BigQuery are ordered lists of 0 or more elements of any non-ARRAY type. They are versatile and support a variety of data types, allowing for flexible data storage and manipulation.

ARRAYs are represented using angle brackets (< and >). However, some restrictions exist, such as the inability to nest ARRAYs directly.

• [element1, element2, ...]: This is a list of values that you want to cast. These values should be compatible with the specified array type.
• AS ARRAY: This instructs SQL to convert the list of values into an array of the specified type Type.

An e-commerce platform stores data about products, including a set of tags for each product. These tags help categorize products and enhance search functionality. Tags are initially stored as strings but need to be handled as arrays for more flexible operations like searches and filters.

Here:

• ['electronics', 'sale', 'laptop']: These are the tags associated with a product, such as categories or keywords that describe the product.
• AS ARRAY: Converts the list of string tags into an array of strings. This format supports more complex queries and operations like matching any tag in the array against user searches.
• AS product_tags: This alias renames the resulting array column to product_tags, making it clear that this column contains arrays of tags associated with each product.

💡Learn how to handle and manipulate arrays efficiently, enhancing your data analysis and streamlining your workflows. Explore our latest guide on mastering Array Functions in BigQuery.

The BYTES data type in BigQuery stores binary data, distinct from string data types. Although many functions that work on strings also work on BYTES, they should not be interchanged. BYTES are represented using single, double, or triple quotation marks but must always start with the prefix 'B' or 'b'. This data type is essential for handling raw binary information, such as images or audio files.

• 'string_value': This is the string literal or variable you want to convert into a bytes type.
• AS BYTES: This instructs SQL to convert the string into a bytes data type typically used to store binary data.

Suppose a software security firm needs to store sensitive information in a database, such as encryption keys or hashed passwords. For security reasons, these items, generated and used as strings, must be stored in a binary format to prevent easy readability or tampering.

Here:

• encryption_key_string: This represents a string that might contain an encryption key. It is assumed to be a placeholder for the actual string values of keys that need to be securely stored.
• AS BYTES: This function converts the encryption key's string format into a bytes type, which is a more secure way to handle sensitive data in databases.
• AS encrypted_key: This alias renames the resulting bytes column to encrypted_key, making it clear that this column contains encryption keys in a secure, binary format.

The GEOGRAPHY data type in BigQuery represents spatial data such as points, lines, and polygons on the Earth's surface. All geographical positions are represented in the WGS84 reference ellipsoid, the same system used in GPS. This means longitude and latitude coordinates can be directly used in BigQuery to find specific locations. Geography functions in BigQuery are prefixed with ST_ and categorized based on their use cases.

• ST_GEOGFROMTEXT('WKT'): A function that converts a Well-Known Text (WKT) representation of a geographic object into a geographic data type. 'WKT' here should be replaced with the actual text representation of the geographic data.
• AS GEOGRAPHY: This instructs SQL to convert the geographic data expressed in WKT format into the GEOGRAPHY data type, which stores and manipulates geographic information.

Suppose an urban planning department needs to analyze geographic data related to city landmarks for planning and development projects. They use geographic coordinates of landmarks, which are stored in text format, and need to convert these into a GEOGRAPHY data type for spatial analysis and mapping.

Here:

• ST_GEOGFROMTEXT('POINT(-122.4194 37.7749)'): Converts the Well-Known Text (WKT) representation of a point (in this case, the geographic coordinates of a city landmark in San Francisco) into a geographic object.
• AS GEOGRAPHY: Converts the geographic object into the GEOGRAPHY data type, suitable for spatial operations and analysis.
• AS landmark_location: This alias names the resulting GEOGRAPHY column landmark_location, indicating that it contains the geographic location of landmarks in a format suitable for mapping and spatial queries.

In BigQuery, the TIME data type is specifically designed for recording times of day without referencing particular dates or time zones. It captures hours, minutes, seconds, and optional fractional seconds. This level of detail is crucial for scenarios that demand time-specific precision, such as event scheduling or logging transaction times. The canonical format of the TIME data type is [H]H:[M]M:[S]S[.DDDDDD].

The DATE data type in SQL is used to store a calendar date, which is distinct from a time zone. This type does not correspond to a specific 24-hour period; rather, it signifies a date that spans different times depending on the time zone. For instance, if the date '2021-12-25' is set in the context of CST (Central Standard Time, UTC-6), it represents the time from 6 p.m. on December 24th to 6 p.m. on December 25th. However, under IST (Indian Standard Time, UTC+5:30), the same date encompasses 11:30 a.m. on December 25th to 11:30 a.m. on December 26th.

Here:

• time_string: This is the literal string representing a time value you want to convert into a TIME data type.
• AS TIME: This instructs SQL to convert the string into a TIME data type, which stores the time of day without date information.
• AS alias: This part of the query assigns an alias to the output column for easier reference in the result set.

A healthcare clinic uses a scheduling system to manage appointment times. Appointment times are stored as strings in the database and must be converted to TIME data type for time-specific operations and comparisons, such as scheduling overlaps and generating daily appointment reports.

Here:

• '14:30:00': This string represents a typical appointment time (2:30 PM) stored in the database. It is specified in a 24-hour format.
• AS TIME: This Converts the appointment time string format into a TIME data type, enabling more accurate time calculations and comparisons within SQL queries.
• AS appointment_time: This alias names the resulting TIME column appointment_time, clarifying that this column contains specific times of appointments formatted suitably for time-based operations.

Here:

• 'date_string': This is the string literal that represents a calendar date, which you want to convert into a DATE data type.
• AS DATE: This instructs SQL to convert the string into a DATE data type, which stores calendar dates without time or timezone information.
• AS alias: This part of the query assigns an alias to the output column for easier reference in the result set.

An e-commerce platform needs to analyze sales performance over specific dates to identify trends and make strategic decisions. Sales data is initially logged with timestamp information, but for trend analysis, only the date part is needed.

Here:

• '2022-11-24': This string represents a specific date, which in this context might correspond to a significant sales event such as Black Friday. The date is formatted in the ISO standard (YYYY-MM-DD).
• AS DATE: Converts the string format of the date into a DATE data type, focusing analysis solely on the date, excluding time components.
• AS sales_date: This alias names the resulting DATE column sales_date, clarifying that this column contains the dates of sales records formatted appropriately for date-based analyses.

The DATETIME data type in BigQuery stores both date and time values without time zone information. This is ideal for recording precise moments that don't require timezone conversion, such as logging events in a controlled system environment or storing historical data where the time zone is constant.

• 'datetime_string': This is the string literal that represents a specific date and time, which you want to convert into a DATETIME data type.
• AS DATETIME: This instructs SQL to convert the string into a DATETIME data type, which is used to store both date and time information without time zone details.
• AS alias: This part of the query assigns an alias to the output column for easier reference in the result set.

Suppose an event management company uses a database to schedule and track events. The company records each event's exact date and time, initially stored as strings. They need to convert these strings into a more structured DATETIME format to facilitate scheduling, reminders, and conflict checks for operational purposes.

Here:

• '2023-10-05 14:00:00': This string represents the planned start time of an event. The date and time are formatted as YYYY-MM-DD HH:MM:SS, providing clarity on the exact moment the event is scheduled to begin.
• AS DATETIME: Converts the string format into a DATETIME data type, ensuring that the date and time are stored in a format conducive to time-based calculations and comparisons.
• AS event_start_time: This alias names the resulting DATETIME column event_start_time, indicating that this column contains the start times of events in a structured format.

The TIMESTAMP data type in BigQuery is used to store both date and time information with timezone awareness. This data type is crucial for tracking events across different time zones without losing the context of time. It is displayed in UTC and can be granular to the level of milliseconds.

• 'timestamp_string': This is the string literal that represents a specific date and time, possibly including timezone information, which you want to convert into a TIMESTAMP data type.
• AS TIMESTAMP: This instructs SQL to convert the string into a TIMESTAMP data type, which is used to store both date and time information including time zone details.
• AS alias: This part of the query assigns an alias to the output column for easier reference in the result set.

Suppose a global company organizes webinars attended by participants from around the world. To manage these events effectively, they store the start times of webinars in a database. The start times include time zone information to accommodate the global audience and are initially stored as strings. They need to convert these strings into a TIMESTAMP format to manage timings across different time zones precisely.

Here:

• '2023-08-15 10:00:00-5:00': This string represents the scheduled start time of a webinar, including a time zone offset of UTC-5 hours. The format includes the date, time, and time zone offset, providing precise scheduling details.
• AS TIMESTAMP: This function converts the string format into a TIMESTAMP data type, which is crucial for ensuring accurate scheduling across different time zones.
• AS webinar_start_time: This alias names the resulting TIMESTAMP column webinar_start_time, clearly indicating that this column contains the start times of webinars in a format that includes time zone information.

💡Learn how to handle and manipulate time-based data accurately, enhancing your data analysis and ensuring precise time-related operations. Check out our latest guide on mastering timestamp functions in BigQuery.

In BigQuery, the JSON data type stores structured data in a flexible, text-based format. JSON (JavaScript Object Notation) is well-suited for handling complex data structures involving nested and hierarchical data, such as configurations, settings, and records with multiple attributes and values.

This data type allows you to store and query data in a semi-structured form. It is extremely versatile for web-based data and applications where data schemas evolve.

The JSON_TYPE function in examines the primary JSON value type and converts it to a SQL string. It can identify and return types such as "object", "array", "string", "number", "boolean", and "null", detailing the fundamental nature of the JSON data.

Here:

• json_expr: This is the JSON expression for which the type is to be determined.

Suppose you're working with data that includes various JSON elements, and you want to classify these elements by type for better data management.

Here:

• UNNEST(...) AS json_val: This part creates a temporary table-like structure from an array of JSON literals.
• JSON_TYPE(json_val): This function call determines the type of each JSON literal.
• AS type: This labels the column showing the result of the JSON_TYPE function.

While versatile, BigQuery data types have limitations and size considerations. Understanding these are essential for optimizing your data storage and query performance. Each data type in BigQuery has its storage requirements, precision constraints, and operational characteristics, which can significantly impact how your data is processed and stored.

⚠️ Limitations: The NUMERIC data type in BigQuery is precise up to 38 digits, with a scale of 9, making it a reliable choice for financial and accounting calculations where precision is crucial. However, it's important to be aware of the limitations when performing arithmetic operations that require precision beyond nine decimal places.

📖 Example: The NUMERIC data type might introduce rounding errors due to its precision limit in fields such as scientific research or complex financial models involving currency conversion and compound interest calculations over extended periods.

✅ Considerations: To solve these issues, careful consideration of the operations performed on NUMERIC fields is necessary. Utilizing software-level solutions or restructuring calculations can help maintain accuracy in scenarios where the inherent precision of the NUMERIC type may not suffice.

⚠️ Limitations: Both the STRING and BYTES data types in BigQuery can store up to 2 MB of data per cell. However, it's generally advised to avoid using BigQuery to store large blobs of text or binary data.

📖 Example: For instance, lengthy documents, serialized large datasets, or high-resolution image data encoded in base64 may easily exceed this size limit. The same goes for binary data, such as audio files or video clips, which can quickly surpass the capacity of a single BYTES cell.

✅ Considerations: A better approach is to use external storage solutions like Google Cloud Storage to handle very large text or binary data. You can store the actual data externally and maintain a reference or URL to these objects in your BigQuery tables. This strategy not only circumvents the size limitations but also optimizes the performance of your BigQuery operations by keeping the dataset lean and more focused on structured data.

⚠️Limitations: While there is no explicit limit on the number of fields within a STRUCT or elements within an ARRAY, the total size of any single row, which may include STRUCT or ARRAY types, must not exceed the maximum row size limit of 100 MB. This overarching constraint is crucial to consider because complex nested structures can substantially increase the size of your data.

📖 Example: For example, a deeply nested STRUCT or a large ARRAY can lead to slower performance and higher resource utilization during data processing. 

✅ Considerations: To solve these issues, it's advisable to optimize data structures by minimizing unnecessary nesting and considering alternative data modeling techniques, such as splitting large arrays or structures into separate tables or columns, to maintain efficient querying and manage costs effectively.

⚠️ Limitations: In BigQuery, the INT64 data type supports integer values ranging from -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807. This extensive range accommodates the needs of most applications, from basic inventory counts to complex numerical analyses in financial or scientific domains.

📖 Example: The INT64 data type allows tracking large quantities of items within the acceptable range. However, attempting to store a value like 10,000,000,000,000,000,000,000 in an INT64 column would exceed its limit, causing overflow errors and inaccurate inventory reports.

✅ Considerations: To maintain data integrity and avoid overflow errors, always verify that the values used in your INT64 operations remain within the defined range. Implement checks and validations in your data processing workflows to ensure compliance with these limits.

When modeling data in BigQuery, aiming for best practices enhances query performance and cost efficiency. If implemented, these best practices can help streamline operations and ensure the environment is optimized for handling large-scale data analytics effectively.

When designing your schema in BigQuery, aligning with typical query patterns is important to maximize performance. Denormalizing your data is beneficial as it reduces the need for joins; BigQuery operates more efficiently with fewer, larger tables rather than many smaller ones. Using nested and repeated fields, such as STRUCT and ARRAY data types, you can maintain relational data within a single table, eliminating the need for separate tables.

Selecting the correct data type for each column in BigQuery is crucial for optimizing storage and query performance. More precise data types generally require less storage space and enhance query efficiency. For example, using INTEGER instead of FLOAT can save space and improve performance when dealing with whole numbers. By carefully choosing the most appropriate data types, you can significantly reduce costs and increase the efficiency of your queries.

Partitioning and clustering your tables in BigQuery can significantly enhance query performance and reduce costs. By partitioning tables based on a date or another frequently queried column, you can minimize the amount of data scanned during each query, making the process more efficient and cost-effective. This approach not only speeds up data retrieval but also reduces the resources needed for query execution, thereby optimizing both performance and cost.

Managing costs effectively in BigQuery is crucial, given that its pricing model is primarily based on the amount of data processed by queries. To minimize costs, it is advisable to avoid using SELECT * queries that process all columns; instead, specify only the columns needed for your analysis.

Utilizing the LIMIT clause or preview features can help test queries without incurring the cost of processing large datasets. These strategies ensure more controlled and efficient use of BigQuery, optimizing performance and costs.

Data modeling in BigQuery is inherently iterative. Begin by crafting a model based on your initial understanding, then test this model using actual queries and real-world workloads to identify areas for improvement. Utilize the INFORMATION_SCHEMA views to access valuable metrics on table storage and query performance. Through continuous testing and refinement, you ensure that your BigQuery models remain efficient and responsive to the evolving needs of your data analysis tasks.

Master these essential BigQuery functions to boost your data analysis skills. These key functions, help you streamline your workflows and achieve more accurate results.

• Conditional Expressions: Implement advanced conditional logic in your queries with BigQuery’s conditional expressions for more dynamic data processing.
• Numbering Functions: Add row numbers or ranks to your result sets using BigQuery’s numbering functions, essential for ordered data analysis.
• Navigation Functions: Traverse through rows in your result sets effectively using BigQuery’s navigation functions like LEAD and LAG.
• Data Manipulation Language (DML): Learn how to use DML in BigQuery for inserting, updating, and deleting data in your datasets efficiently.
• Conversion Functions: Convert data types seamlessly in BigQuery using conversion functions to ensure data compatibility and integrity.
• Aggregate Functions: Summarize and aggregate your data effectively with BigQuery’s aggregate functions, including COUNT, SUM, AVG, MIN, and MAX.

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