Mastering BigQuery SQL: A Comprehensive Guide for Data Professionals

Google BigQuery SQL

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Modern Data Management Guide

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Modern Data Management Guide

In a time when data drives decisions, Google BigQuery is a cornerstone for data analysts, enabling swift and scalable analysis of massive datasets. BigQuery SQL is a powerful tool for analyzing data, much like traditional SQL, but with some key differences that make it perfect for handling large-scale datasets.

Users can use familiar SQL commands to interact with Google BigQuery, a cloud-based data warehouse.

This guide serves as a comprehensive resource for mastering BigQuery SQL, catering to professionals eager to enhance their analytical capabilities. From basic syntax to advanced data manipulation techniques, this guide promises to elevate your proficiency in navigating BigQuery's environment.

Understanding BigQuery SQL Syntax

What sets BigQuery apart is its ability to efficiently process massive amounts of data at lightning speed. Thanks to its distributed architecture, BigQuery can run parallel queries across multiple servers, making it possible to analyze petabytes of data in just seconds.

One notable feature of BigQuery SQL is its support for nested and repeated data structures, which enables users to handle complex data types like arrays and structs with ease. This feature is particularly handy when dealing with semi-structured data formats such as JSON.

Additionally, BigQuery offers advanced functions and tools tailored for data analysis, such as window functions for performing calculations on groups of rows and BigQuery ML for building machine learning models directly within SQL queries.

Overall, BigQuery SQL combines the simplicity and versatility of traditional SQL with the scalability and performance required for modern data analytics tasks, making it an invaluable tool for data professionals working with large and complex datasets in the cloud.

Basic Query Techniques in BigQuery SQL

BigQuery SQL supports a range of SQL statements and query techniques that enable powerful data analysis and manipulation capabilities within Google's cloud-based big data analytics platform.

We’d like to start with the simple queries for BigQuery SQL in this part. We’ve also prepared a video for you covering the Basics of SQL Querying with BigQuery.

Using SELECT & FROM Statements to Retrieve Data

In BigQuery, the SELECT and FROM statements are fundamental for retrieving data from specified tables within your datasets.

By using SELECT, you can specify the exact columns you want to pull, while FROM indicates the table from which this data should be extracted.

SELECT Statement Syntax:

SELECT column1, column2, ... 
FROM table_name;
  • SELECT: This clause specifies the columns that you want to retrieve from the database. You can retrieve one or multiple columns.
  • FROM: This clause specifies the table from which to retrieve the data.

SELECT Everything Syntax:

You can also SELECT all columns from the table by using an asterisk *.

SELECT * FROM table_name;

SELECT Statement Example:

Imagine you want to retrieve the names and ages of all employees from the employees' table in your company's database.

SELECT name, age 
FROM employees;


  • SELECT name, age: Specifies that you want to retrieve the name and age columns.
  • FROM employees: Specifies that these columns are to be retrieved from the employees' table.

Using AS Statement to Name Data

The AS statement in BigQuery is used to assign aliases to columns or tables within your SQL queries, enhancing readability and simplifying the output.

By renaming data on the fly, AS helps in managing complex queries, especially when dealing with JOINs or subqueries where column name conflicts might arise. This makes the data manipulation and reporting processes more straightforward and comprehensible.

AS Statement Syntax:

SELECT column_name 
AS alias_name 
FROM table_name;
  • SELECT: This clause determines the columns to be retrieved from the database.
  • FROM: This clause identifies the table from which the data will be extracted.
  • AS: This keyword is used to rename a column or a table using an alias. This is helpful for improving the readability of the results or when you need to join tables and avoid column name conflicts.

AS Statement Example:

Suppose you want to simplify the column names of the employee_id and first_name when retrieving data from the employees table, perhaps for a report that will be read by non-technical staff.

  employee_id AS id, 
  first_name AS name 


  • employee_id AS id: Changes the column name from employee_id to id in the query results.
  • first_name AS name: Changes the column name from first_name to name in the query results.
  • FROM employees: Specifies that these columns are to be retrieved from the employees table.

Applying Filters Using WHERE

The WHERE clause is used to filter records before any groupings are made.

You can utilize WHERE to filter rows before performing operations like JOINs or aggregations.

WHERE Clause Syntax:

SELECT column1, column2
FROM table
WHERE condition;
  • column1, column2: These are placeholders for the specific columns you want to select from the table. You can specify one or more columns separated by commas.
  • FROM table: This is the name of the table from which you want to retrieve data. It could be a physical table or a result of another query.
  • Condition: Defines which rows should be included in the result.

WHERE Clause Example:

Suppose you aim to analyze transactions made within the last month in a financial database to identify recent financial activities.

Here's what your syntax will look like:

   transaction_date >= '2023-04-01';

In this example:

  • transaction_id: The unique identifier for each transaction.
  • amount: The monetary value of each transaction.
  • transaction_date: The date on which each transaction occurred.

The SQL query retrieves transaction details from the "transactions" table, specifically selecting the transaction ID, amount, and date. It applies a filter using the WHERE clause to include only transactions that occurred on or after April 1, 2023, facilitating analysis of recent financial activities.

Using GROUP BY for Aggregation

The GROUP BY function groups rows with identical values in specified columns into summary rows. It is used to summarize data by creating a grouped field based on one or more columns, which helps to avoid repetitions.

While commonly used with aggregation functions like "sum" or "max", it is also possible to use GROUP BY without these functions. This allows for retrieving unique, non-repeating values from the specified columns, simply by grouping them.

GROUP BY Statement Syntax:

FROM table
GROUP BY column1;
  • column1: This is the column based on which the data will be grouped. Each unique value in this column forms a group.
  • COUNT(column2): This is the aggregation function applied to the grouped data. It counts the number of occurrences of values in column2 within each group.
  • FROM table: Specifies the table from which data is retrieved.
  • GROUP BY column1: Groups the rows with identical values in column1 into summary rows.

GROUP BY Statement Example:

Suppose you want to analyze the total number of orders for each product in an online marketplace database to identify the most popular products.

Here's what your syntax will look like:

  COUNT(order_id) AS total_orders
FROM orders_table
GROUP BY product_id;

In this example:

  • SELECT product_id, COUNT(order_id) AS total_orders: This part selects the product_id and counts the number of occurrences of each order_id from the orders table, aliasing the result as total_orders.
  • FROM orders_table: Specifies that the data will be retrieved from the orders table.
  • GROUP BY product_id: Groups the results by the product_id column. This means that all rows with the same product_id will be grouped together, and the aggregation function (COUNT) will be applied to each group separately.

The SQL query aggregates order data from the "orders_table" table, counting the total number of orders for each product identified by the "product_id" column. This analysis aids in identifying the most popular products in the online marketplace based on order frequency.

Sequencing Results with ORDER BY

The ORDER BY in BigQuery is used to sort the results of a query based on one or more columns in ascending or descending order. This is particularly useful when organizing data in a specific sequence for reporting or analysis purposes.

ORDER BY Syntax:

SELECT column1, column2
FROM table
ORDER BY column1 DESC;
  • column1, column2: This part of the syntax specifies which columns from the database table you want to retrieve. In this case, column1 and column2 are the columns selected.
  • FROM table: Specifies from which table in the database you want to retrieve the data.
  • ORDER BY column1 DESC: ORDER BY clause is used to sort the result set returned by the SELECT statement.
  • DESC: It specifies the direction of the sort. DESC means descending order, from the highest value to the lowest. If you wanted to sort in ascending order (lowest to highest), you would use ASC.

ORDER BY Example:

Suppose your company needs to sort products based on their stock quantity in an inventory management system to prioritize restocking efforts, and they want you to analyze that.

Here's what your syntax will look like:

SELECT product_id, stock_quantity
FROM product_inventory
ORDER BY stock_quantity DESC;

In this example:

  • SELECT product_id, stock_quantity: Specifies the columns to retrieve.
  • FROM inventory: Specifies the table from which to retrieve data.
  • ORDER BY stock_quantity DESC: Orders the result set by the stock_quantity column in descending order.

The SQL query retrieves product IDs and their corresponding stock quantities from the "inventory" table. It orders the results based on stock quantity in descending order, facilitating prioritization of restocking efforts by placing products with higher quantities at the top of the list.

Advanced Query Techniques in BigQuery SQL

BigQuery SQL is relatively simple, but it also supports a range of advanced query techniques like JOIN, UNION, using Subqueries, performing calculations, aggregating and so much more.Knowing the complex techniques, you enable data analysis and manipulation capabilities within Google's cloud-based big data analytics platform.

These advanced techniques allow users to perform complex analytics at scale.

BigQuery's integration with machine learning and geospatial functions further enhances its utility, enabling sophisticated queries that can include predictive insights and spatial analysis directly within the SQL queries.

This flexibility makes BigQuery an invaluable tool for data analysts looking to derive deep insights from large datasets efficiently.

Executing Complex JOINs

JOINs are fundamental in SQL for combining rows from two or more tables based on a related column. To execute complex JOIN operations, we need to specify the columns to retrieve from the joined tables, alias the tables for clarity, and define the JOIN condition using the ON keyword.

JOIN Syntax:

SELECT A.column1, B.column2
FROM tableA A
JOIN tableB B ON A.key = B.key;
  • A.column1, B.column2: Columns selected from the joined tables.
  • tableA, tableB: The tables being joined.
  • A.key = B.key: Condition that specifies how rows from the tables are combined.

JOIN Example:

Suppose, in a retail database, you need to analyze customer orders along with their corresponding product details to understand purchase patterns and product popularity.

Here's what your syntax will look like:

   customers_table as customers
   orders_table as orders 
   customers.customer_id = orders.customer_id;

In this example:

  • This selects the customer's name from the "customers" table.
  • orders.order_date: This selects the order date from the "orders" table.
  • orders.amount: This selects the order amount from the "orders" table.

The SQL query produces a list of customer names, order dates, and amounts by joining the customers and orders tables on the customer_id. This output helps analyze customer purchase patterns and product popularity.

💡Understanding the differences between JOIN and UNION operations in BigQuery can significantly enhance your data manipulation capabilities. Check out our complete guide on the differences between BigQuery JOIN and UNION to optimize your queries and improve your data processing efficiency.

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Understanding the Difference Between JOIN and UNION in BigQuery SQL

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Incorporating Calculations in Queries

SQL enables real-time calculations within queries, facilitating data transformation or deriving new insights, with arithmetic operations executed directly in the SELECT clause.

Calculations in Queries Syntax:

   column1, (column2 * column3) 
   AS new_column
FROM table;
  • SELECT column1, (column2 * column3) AS new_column: Specifies the columns to retrieve and incorporates a calculation (column2 * column3) to create a new column.

Calculations in Queries Example:

Imagine you are a data analyst in an online business company and have a database with two key fields for each item in an order: quantity and unit_price. To optimize the inventory and sales strategy, you need to regularly calculate the total price for each item in every order, which will help you understand sales trends and customer preferences.

Here's what your syntax will look like:

   quantity * unit_price 
   AS total_price

In this example:

  • order_id: This field identifies the specific order. It helps in grouping or referencing items that belong to the same transaction.
  • item_id: This is the identifier for a specific item within an order, useful for inventory tracking and analysis.
  • quantity: Represents how many units of the item were purchased in a single order.
  • unit_price: Indicates the cost of a single unit of the item.
  • quantity * unit_price AS total_price: This expression calculates the total cost for the item in the order by multiplying the quantity by the unit price. The result is aliased as total_price for easy reference in the results.
  • project.dataset.orders: Specifies the BigQuery table containing the order data. The project is the Google Cloud project ID, dataset is the collection within the project, and orders is the specific table name.
  • order_id: This part of the query sorts the results by the order ID. Sorting by order ID makes the output easier to understand and analyze because all items from the same order are listed together.

Each element of this query works together to provide a comprehensive view of each item's total price within its respective order, preparing the data for further analysis or reporting.

Aggregating Data by Day, Week, and Month

BigQuery empowers trend analysis and reporting by facilitating aggregation over time intervals through its robust date functions. You can efficiently perform time-based aggregation by combining date functions with GROUP BY.

Aggregating Syntax:

  unit FROM date_column) 
  AS period, SUM(metric) AS total
FROM table
GROUP BY period;
  • ELECT EXTRACT(unit FROM date_column) AS period, SUM(metric) AS total: Specifies the columns to retrieve.
  • FROM table: Specifies the table from which to retrieve data.
  • GROUP BY period: Groups the result set based on the extracted time unit (unit) from the date_column.

Aggregating Example:

Imagine you are a data analyst working for a retail company. You must analyze monthly sales performance to identify trends, seasonal variations, and promotion opportunities. To do this, you decide to aggregate the sales data by month to see how much revenue was generated in each month of the year.

Here's what your syntax will look like:

  MONTH FROM order_date) 
  AS month, SUM(sales_amount) 
  AS monthly_sales
FROM sales_data
GROUP BY month
ORDER BY month;

In this example:

  • SELECT EXTRACT(MONTH FROM order_date) AS month, SUM(sales_amount) AS monthly_sales: Specifies the columns to retrieve and aggregate monthly sales data.
  • GROUP BY month: Groups the result set by the extracted month from the order_date column.
  • ORDER BY month: Orders the result set by month.

The SQL query extracts the month from the order date and calculates the sum of sales amounts for each month, aliasing the result as "monthly_sales." It then groups the data by month and orders the result set accordingly. This enables analysis of monthly sales data, providing insights into sales trends over time.

Embedding Queries within Other Queries

In BigQuery, embedding queries within other queries, often called subqueries or nested queries, is a powerful technique for organizing complex data operations.

This method allows you to perform more detailed and layered data analysis by using the result of one query as an input for another. Subqueries can be used in various parts of a SQL statement, including the SELECT, FROM, and WHERE clauses.

Subqueries Syntax:

  column1, (
    SELECT SUM(column2) 
    FROM table2 
    WHERE table2.column3 = table1.column3)
FROM table1;
  • column1: This is a column selected from table1. It represents a piece of data you are retrieving from each row in table1.
  • (SELECT SUM(column2) FROM table2 WHERE table2.column3 = table1.column3): This is a subquery that calculates the sum of column2 from another table (table2). The result of this subquery is returned in line with each row's column1 value from table1.
  • SUM(column2): This is an aggregate function that sums up all the values of column2 from table2 where the condition specified in the WHERE clause is true.
  • FROM table2: Specifies that column2 is being retrieved from table2.
  • WHERE table2.column3 = table1.column3: This condition links table2 with table1. The subquery filters rows in table2 where column3 matches column3 in the currently processed row of table1.

Subqueries Example:

Imagine you're a data analyst at a retail company that wants to enhance customer relationship management by understanding each customer's spending habits. You aim to calculate the average amount spent per customer across all their transactions to tailor marketing strategies and provide personalized offers based on spending behavior.

Here's what your syntax will look like:

SELECT customer_id, (
  SELECT AVG(amount) 
  FROM transactions 
  WHERE transactions.customer_id = customers.customer_id) 
    AS avg_spend
FROM customers;

In this example:

  • SELECT customer_id, (SELECT AVG(amount) FROM transactions WHERE transactions.customer_id = customers.customer_id) AS avg_spend: Specifies the columns to retrieve (customer_id), and embeds a subquery to calculate the average spending of each customer from another table (transactions) based on the customer_id.
  • customer_id: The column selected from the main table (customers).
  • (SELECT AVG(amount) FROM transactions WHERE transactions.customer_id = customers.customer_id) AS avg_spend: The subquery that calculates the average amount spent by each customer. It's correlated with the main query through the condition transactions.customer_id = customers.customer_id.

The SQL query retrieves the customer ID and embeds a subquery to calculate the average spending of each customer based on transactions linked to their ID. This nested query correlates with the main query through the condition "transactions.customer_id = customers.customer_id," enabling the calculation of average spending per visit for each customer.

Expanding RECORD Arrays with UNNEST

BigQuery's UNNEST function facilitates the flattening of arrays within records, allowing individual elements to be accessed for queries. By employing UNNEST, arrays can be expanded into a set of rows, enhancing data exploration and analysis.

UNNESTing Arrays Syntax:

SELECT column1, element
FROM table,
UNNEST(array_column) AS element;
  • column1: This is the first column selected from the table.
  • element: This is the alias for each element of the unnested array. It is created in the UNNEST clause.
  • FROM: This keyword is used to specify the table from which to retrieve the data.
  • table: This is the name of the table from which you are selecting data.
  • UNNEST: This function is used to expand an array into a set of rows. It takes an array column and returns a row for each element in the array.
  • array_column: This is the name of the column that contains array data.
  • AS element: This assigns an alias (element) to each element in the array. The alias can be used in the SELECT clause or elsewhere in the query.

UNNESTing Arrays Records Example:

As a database manager at an e-commerce company, you need to improve search and filtering by creating a detailed list of product tags, showing each tag as a separate record for better categorization and functionality.

Here's what your syntax will look like:

SELECT product_id, tag
FROM products,
UNNEST(tag) AS tag;

In this example:

  • product_id: This field is selected from the products table and identifies each product.
  • UNNEST(tag) AS tag: This function is used to expand the tags array into a table of its own, where each tag associated with a product is shown in a separate row alongside the corresponding product_id.

The SQL query retrieves the product ID and expands the tags array from the products table into individual rows using the UNNEST function, generating a separate row for each element in the array.

Exploring Window Functions

Window functions in BigQuery SQL provide powerful capabilities for performing complex calculations across sets of rows that are related to the current row.

These functions are essential for analytics tasks that require an understanding of data in context, such as calculating running totals, averages, or rankings without grouping the data.

Exploring Window Functions Syntax:

SELECT column1, 
  ORDER BY column4)
FROM table;
  • column1: This field is selected directly from the table and returned in the output. It isn't involved in any calculations or window functions, but it's included to provide context or identifiers alongside the calculated averages.
  • AVG(column2): This calculates the average of column2. The AVG function is an aggregate function used here within the context of a window function.
  • OVER: This keyword introduces the window specification. It defines how the rows are considered for the window function.
  • PARTITION BY column3: This part of the window specification groups the rows into partitions based on unique values in column3. The average is calculated separately for each group.
  • ORDER BY column4: Within each partition defined by column3, this orders the rows based on column4. The order can affect the calculation if the window frame (the set of rows considered for each calculation) is further restricted. However, no specific frame is mentioned here, so it defaults to the entire partition.
  • FROM table: Specifies the table from which to retrieve the data. This is where column1, column2, column3, and column4 are stored.

Window Functions Calls Example:

As an e-commerce data analyst, you analyze customer behavior by calculating the running total of each customer's purchases. This helps identify spending trends, signal loyalty, and trigger targeted marketing campaigns.

Here's what your syntax will look like:

  order_total, SUM(order_total) 
  OVER (
  ORDER BY order_date) 
  AS running_total
FROM orders;

In this example:

  • order_id: This field uniquely identifies each order.
  • userId: Identifies the customer who placed the order.
  • order_total: Represents the total amount spent on the order.
  • SUM(order_total): This is the aggregate function used to sum the order totals.
  • OVER: This keyword specifies the window over which the SUM operates.
  • PARTITION BY userId: The window is partitioned by the customer_id, meaning the running total is calculated separately for each customer.
  • ORDER BY order_date: Within each partition, the data is ordered by the order_date. This ensures that the running total is accumulated in the order in which the orders were placed.
  • AS running_total: The result of the window function is returned in a new column called running_total, which shows the cumulative spending by each customer up to each respective order.

The SQL query calculates the running total sales by customer, utilizing the SUM function and the OVER clause to compute a cumulative total of order totals for each customer, partitioned by customer ID and ordered by the date of order.

Data Manipulation with BigQuery SQL

Efficient data manipulation is essential for maintaining data integrity and optimizing performance in BigQuery.

Whether it's inserting, updating, or deleting data, understanding the unique ecosystem of BigQuery is crucial.

This section delves into all the fundamental data manipulation commands, providing insights into managing data within BigQuery efficiently.

Data Ingesions with BigQuery SQL

Inserting data into BigQuery requires understanding its distributed architecture and storage mechanisms. Implementing best practices ensures high throughput and minimal latency, optimizing performance.

Data Ingesions Syntax:

  table_name (column1, column2, ...)
VALUES (value1, value2, ...);
  • table_name: The name of the table where data will be inserted.
  • column1, column2, ...: The columns in the table where data will be inserted.
  • value1, value2, ...: The corresponding values to be inserted into each column.

Data Ingesions Example:

Suppose you want to insert sales data for January 2024 with a sales amount of $5000 into the sales_data table.

Here's what your syntax will look like:

  daily_sales (date, revenue)
  ('2024-01-01', 5000), 
  ('2024-01-02', 6000), 
  ('2024-01-03', 7000);

In this example:

  • INSERT INTO - Command to insert data into a specific table.
  • daily_sales - Name of the table where data is being inserted.
  • (date, revenue) - Column names where the data will be inserted.
  • VALUES - Specifies the data to be inserted.
  • ('2024-01-01', 5000), ('2024-01-02', 6000), ('2024-01-03', 7000) - Data values for each row, matching the specified columns.

The SQL query inserts sales data for January 2024 into the "daily_sales" table, specifying dates and corresponding revenues. This operation aids in tracking sales amounts for specific dates, such as $5000 on January 1st, $6000 on January 2nd, and $7000 on January 3rd.

Data Update with BigQuery SQL

One of the key features of BigQuery is its ability to perform data updates, allowing users to modify stored data in an efficient and controlled manner.

Whether you need to update specific rows, change existing values based on complex conditions, or handle transactional data changes, BigQuery provides a robust platform that integrates seamlessly with other Google Cloud services for enhanced analytics and informed decision-making.

Data Update Syntax:

UPDATE table_name
SET column1 = value1, column2 = value2, ...
WHERE condition;
  • table_name: The name of the table where data will be updated.
  • column1, column2, ...: The columns to be updated.
  • value1, value2, ...: The new values to replace the existing ones.
  • condition: The condition that specifies which rows will be updated.

Data Update Example:

Suppose you need to adjust the prices of products in your e-commerce platform's inventory based on market trends.

Here's what the syntax will look like:

UPDATE product_inventory
SET price = price * 1.1
WHERE category = 'Electronics';

In this example:

  • product_inventory: Table storing product information.
  • price: Column representing the price of products.
  • category: Column identifying the category of products.

The SQL query updates the prices of products in the "product_inventory" table under the 'Electronics' category by multiplying existing prices by 1.1 to reflect a 10% increase and maintain market competitiveness.

Data Deletion with BigQuery SQL

Data deletion in BigQuery is primarily accomplished using the DELETE statement, part of its SQL dialect. This allows you to selectively remove rows from a table based on specific conditions.

This feature is particularly useful in managing data hygiene and compliance with data retention policies.

Data Deletion Syntax:

DELETE FROM table_name
WHERE condition;
  • table_name: The name of the table from which data will be deleted.
  • condition: The condition that specifies which rows will be deleted.

Data Deletion Example:

Suppose your e-commerce platform discontinues certain product lines, and you need to remove them from your inventory data.

Here's what your syntax will look like:

DELETE FROM product_inventory
WHERE status = 'Discontinued';

In this example:

  • product_inventory: Table storing product information.
  • status: Column indicating the status of products.

This SQL query deletes products from the "product_inventory" table with a status of 'Discontinued', reflecting the e-commerce platform's decision to remove them from inventory.

This action ensures streamlined product management processes and maintains accurate inventory records by retaining only active products.

Transaction Management with BigQuery SQL

BigQuery allows multi-statement transactions within a single query or across several queries by utilizing sessions.

This feature enables you to execute multiple data manipulation operations, like inserting or deleting rows in one or more tables, and commit or roll back these changes atomically.

Transaction Management Syntax:

  • BEGIN TRANSACTION: Starts a transaction.
  • COMMIT: Commits the transaction, making all changes permanent.

Transaction Management Example:

Suppose you are an analyst for an e-commerce platform and need to update sales data for January 2024 and delete erroneous data for February 2024. You want to ensure that these operations are executed reliably and as a single unit to maintain data integrity.

Here's what your syntax will look like:

UPDATE sales_data
SET sales_amount = 5500
WHERE sales_month = '2024-01';
DELETE FROM sales_data
WHERE sales_month = '2024-02';

In this example:

  • BEGIN TRANSACTION: This command initiates the transaction, marking the beginning of a sequence of SQL statements that should be treated as a single unit of work.
  • UPDATE sales_data SET sales_amount = 5500 WHERE sales_month = '2024-01';

This statement updates sales data for January 2024, setting the sales amount to $5500.

  • DELETE FROM sales_data WHERE sales_month = '2024-02';

This statement deletes erroneous sales data for February 2024.

  • COMMIT TRANSACTION: Finalizes the transaction, confirming the successful execution of all enclosed SQL statements and permanently applying the changes to the database.

The SQL example showcases transaction management in BigQuery. It starts by updating January 2024 sales data to $5500, then deletes erroneous February 2024 data.

Finally, it commits the transaction, ensuring reliable execution of both operations as a single unit to maintain data integrity.

Working with Common Table Expressions (CTEs) and UNPIVOT

CTEs and the UNPIVOT operator offer advanced methods for organizing and transforming data. CTEs in BigQuery are temporary result sets that enhance query organization and readability.

They allow complex queries to be broken down into modular pieces for easier optimization. The UNPIVOT operator in BigQuery transforms column-oriented data into row-oriented data.

Combining CTEs and UNPIVOT in BigQuery empowers efficient data transformation and analysis, enabling the extraction of valuable insights with expressive and maintainable SQL queries.

Common Table Expressions (CTEs)

Common Table Expressions (CTEs) offer a way to structure SQL queries to be more maintainable and clear. They allow for the temporary result set that is defined within the execution scope of a single SELECT, INSERT, UPDATE, or DELETE statement. This can be particularly useful for breaking down complex queries into simpler, more manageable parts.

Common Table Expressions (CTEs) Syntax:

    SELECT column1, column2, ...
    FROM table_name
    WHERE condition
  • WITH CTE_Name AS: Begins the CTE and assigns it a name.
  • SELECT column1, column2, ...: Specifies the columns to be included in the CTE.
  • FROM table_name: Defines the table from which to pull the data.
  • WHERE condition: Applies conditions to filter the data for the CTE.
  • SELECT * FROM CTE_Name: Uses the CTE in a query.

CTEs are particularly useful for recursive queries, complex joins, and subqueries that need to be used multiple times within a single query. They improve readability and can make debugging easier.

Common Table Expressions (CTEs) Example:

Suppose, in an e-commerce company, you need to calculate the total revenue generated by each product category. However, the revenue calculation involves aggregating data from multiple tables and applying certain business logic.

Here's what your syntax will look like:

WITH ProductRevenue AS (
  SELECT category, 
  SUM(unit_price * quantity) AS total_revenue
  FROM orders
  JOIN products ON orders.item_id = products.item_id    
  WHERE status = 'Completed'
  GROUP BY category
SELECT * FROM ProductRevenue;

In this example:

  • WITH ProductRevenue AS ( ... ): This clause defines a temporary named result set, known as a Common Table Expression (CTE). The name of the CTE is ProductRevenue.
  • SELECT category, SUM(unit_price * quantity) AS total_revenue: This statement within the CTE calculates the total revenue for each category. It multiplies the price and quantity for each order and then sums these values per category.
  • FROM orders JOIN products ON orders.item_id = products.item_id: This part specifies the tables from which data is retrieved, orders and products. These tables are joined on the item_id, meaning data will be combined from both tables where the item_id matches.
  • WHERE status = 'Completed': This condition filters the data to include only those orders that have been completed.
  • GROUP BY category: This clause groups the results by the category of the products, which is necessary for the SUM function to calculate total revenue for each category separately.
  • SELECT * FROM ProductRevenue: After the CTE is defined, this statement retrieves all records from the ProductRevenue result set. It outputs the total revenue for each product category based on completed orders.

The overall SQL query calculates the total revenue for each product category from completed orders, combining data from the orders and products tables, and presents it in a simple format using the CTE.

Implementing UNPIVOT

The UNPIVOT operator is used to rotate columns into rows, essentially normalizing data, which can be especially helpful when you need to create a relation from columns that were not designed as part of the same data group.

The UNPIVOT operator is valuable when dealing with data that comes from pivoted tables or spreadsheets, where the schema represents data in a cross-tabulated format.


  non_pivoted_column, pivoted_column, value
  FROM table_name
) AS alias
  value FOR pivoted_column 
    IN (column1, column2, ...)
) AS alias;
  • non_pivoted_column: Columns that are not affected by the UNPIVOT operation.
  • pivoted_column: The column header that will represent the previously pivoted columns.
  • value: The value corresponding to the pivoted column.
  • IN (column1, column2, ...): Specifies the columns to be transformed from columns into rows.

UNPIVOT Example:

Suppose, In a sales database, you have a table where sales data is stored in a pivoted format, with separate columns for each quarter's sales. You want to analyze the quarterly sales data by converting it into rows for easier analysis.

Here's what your syntax will look like:

  item_id, quarter, revenue
  FROM sales
) AS sales_data
  revenue FOR quarter IN (
) AS unpivoted_sales;

In this example:

  • item_id, quarter, revenue: These are the specific columns that are being selected or retrieved.
  • FROM ( ... ) AS sales_data: This clause specifies the source of the data. The data is derived from a subquery that is temporarily named sales_data for use in the outer query.
  • SELECT item_id, Q1_sales, Q2_sales, Q3_sales, Q4_sales FROM sales: This is the subquery that selects specific columns from the sales table. It retrieves the Item ID and sales data for each quarter.
  • UNPIVOT ( ... ) AS unpivoted_sales: This operation is used to convert columns into rows, effectively normalizing data that was stored in a pivoted form (one row per product, with separate columns for each quarter's sales).
  • revenue FOR quarter IN (Q1_sales, Q2_sales, Q3_sales, Q4_sales): This part of the UNPIVOT operation specifies that the column names Q1_sales, Q2_sales, Q3_sales, and Q4_sales should be transformed into row values under the new revenue column, with the respective quarter's name stored in the quarter column.

This SQL statement transforms a table with sales data split across multiple columns (one for each quarter) into a format where each row represents sales for a specific quarter, making it easier to analyze sales trends over the year for each product.

Query Performance Optimization in BigQuery SQL

Optimizing query performance in BigQuery not only enhances efficiency but also reduces costs. This section provides practical tips to fine-tune your queries.

Minimize Data Processing

Reducing the data processed in each query can lead to cost savings and faster execution times.

First, when constructing SELECT statements, explicitly specify only the necessary columns. This approach reduces the amount of data transmitted and processed, enhancing query performance and cost efficiency.

Additionally, use the WHERE clause early in the query execution process. This allows you to filter out irrelevant rows right from the beginning, focusing the processing power on relevant subsets of data and preventing the unnecessary processing of data that does not meet your criteria.

By applying these methods, you can achieve more efficient data handling and better resource management in your database operations.

Refine Query Operations

Optimizing SQL operations can significantly enhance query performance. Consider employing strategic methods such as smart joins and efficient aggregations to enhance efficiency.

In smart joins, position the smallest dataset on the left side of a JOIN operation. This practice minimizes data shuffling and streamlines the merging process, reducing the overall computational load.

For efficient aggregations, it's beneficial to group by keys that exhibit lower cardinality. This approach reduces the number of unique groups the system needs to manage during the aggregation process, which can significantly speed up query execution. Both strategies help in optimizing query performance by reducing unnecessary data processing and improving the management of system resources.

Limit Query Output

Controlling the output of your queries can prevent performance bottlenecks. Several practical strategies can be implemented to effectively limit query output and manage large datasets.

Using the LIMIT clause is particularly useful when testing queries. Instead of processing the entire dataset, you can use LIMIT to retrieve a small sample, drastically reducing the amount of data processed and speeding up testing.

Implementing pagination is a smart approach for applications that need to handle large result sets. Pagination breaks the result set into manageable chunks or pages, allowing for efficient data retrieval and a better user experience. Both methods are key in optimizing database queries by minimizing resource load and improving response times.

Utilize BigQuery BI Engine Reservation

Leveraging the BI Engine in BigQuery can significantly enhance the performance of dashboards and interactive analyses. For instance, you can reserve capacity in BI Engine.

Your costs when utilizing BigQuery BI Engine are determined by the BI Engine capacity you procure for your project. Charges are assessed per Gb/hour and are priced regionally in accordance with on-demand pricing.

Procuring BigQuery BI Engine capacity involves creating a reservation linked to a designated project at the time of reservation creation. The BI Engine utilizes this reservation to ascertain the amount of data that can be cached.

By reserving capacity in the BI Engine, you can optimize performance for your analytical workloads. This involves storing frequently accessed data in memory, drastically reducing query times by allowing faster data retrieval than fetching from disk storage.

Avoid Inefficient SQL Patterns

Recognizing and avoiding inefficient SQL patterns is crucial for maintaining performance.

One key strategy is to avoid cross-joins unless they are absolutely necessary. Cross-joins can exponentially increase the amount of data processed, significantly slowing down query execution.

Another technique is to use approximate aggregations when exact counts are not necessary. Functions like APPROX_COUNT_DISTINCT can greatly speed up queries by providing a close approximation rather than an exact count.

These methods help in optimizing the efficiency of your SQL queries.

Define Table Schema Constraints

Proper schema definition is essential for improving both query performance and data integrity.

It is beneficial to define schemas explicitly during table creation to effectively define table schema constraints.

This avoids issues with ambiguous data types and ensures that each field is appropriately tailored to its expected data type.

Additionally, utilizing the REQUIRED constraint for fields that must not be empty can help ensure data completeness. This approach maintains data accuracy and optimizes database operations by preventing invalid data entries from the outset.

Practical Examples and Use Cases of SQL in BigQuery

Continuing our exploration of BigQuery SQL, we delve into some practical examples and use cases that showcase its robust capabilities in managing and analyzing large datasets.

Utilizing an e-commerce database, here are a few use cases that demonstrate how to retrieve specific data efficiently in BigQuery, highlighting the platform's capability to handle complex e-commerce datasets.

Use Case 1: Analyzing Sales Performance by Product Category

Suppose an e-commerce company wants to analyze sales performance across different product categories to optimize its marketing and inventory strategies.

Here's what the syntax will look like:

    order_date) AS year, 
    COUNT(order_id) AS total_orders, 
    SUM(sales) AS total_sales
FROM order_details
GROUP BY category, year
ORDER BY total_sales DESC;

In this use case:

  • category: Specifies the category of products.
  • EXTRACT(YEAR FROM order_date) AS year: Extracts the year from the order date.
  • COUNT(order_id) AS total_orders: Counts the total number of orders per category each year.
  • SUM(sales) AS total_sales: Sums up the sales amount per category each year.
  • GROUP BY category, year: Groups the results by product category and year.
  • ORDER BY total_sales DESC: Orders the results by total sales in descending order, highlighting the most profitable categories.
  • Outcome: This query helps the e-commerce company identify which product categories are performing well and which ones might need promotional boosts or inventory adjustments, aiding in strategic decision-making.

Use Case 2: Customer Segmentation for Targeted Marketing

Suppose an e-commerce platform wants to segment its customer base for targeted marketing campaigns based on their spending habits.

The SQL syntax will look like the following:

  COUNT(order_id) AS number_of_orders, 
  AVG(total_amount) AS average_order_value
WHERE order_date 
BETWEEN '2023-01-01' 
AND '2023-12-31'
GROUP BY customer_id
HAVING AVG(total_amount) > 100;

In this use case:

  • customer_id: Identifies each unique customer.
  • COUNT(order_id) AS number_of_orders: Counts the number of orders placed by each customer within the year.
  • AVG(total_amount) AS average_order_value: Calculates the average order value per customer.
  • WHERE: Filters orders made within the specified year.
  • GROUP BY customer_id: Groups the data by customer ID.
  • HAVING AVG(total_amount) > 100: Filters customers whose average spending exceeds $100.

Outcome: This analysis enables the marketing team to identify high-value customers and tailor marketing campaigns to enhance customer loyalty and increase average order values.

Use Case 3: Optimizing Logistics by Analyzing Shipping Times

Suppose an e-commerce business wants to improve customer satisfaction by analyzing and optimizing shipping times.

The SQL syntax will look like the following:

  AVG(DATE_DIFF(delivery_date, order_date, DAY)) 
  AS average_delivery_days
FROM table_shipping_details
GROUP BY shipping_zone
ORDER BY average_delivery_days;

In this use case:

  • shipping_zone: The geographical zone where the product is shipped.
  • AVG(DATE_DIFF(delivery_date, order_date, DAY)) AS average_delivery_days: Calculates the average number of days taken for delivery to each shipping zone.
  • GROUP BY shipping_zone: Groups the data by shipping zone.
  • ORDER BY average_delivery_days: Orders the results by the average delivery days, helping identify zones with slower delivery times.

Outcome: This query helps the logistics team to pinpoint regions with delayed shipping and potentially adjust shipping strategies or providers to enhance delivery efficiency and customer satisfaction.

Use Case 4: Tracking Product Returns and Customer Feedback

Suppose an e-commerce manager needs to monitor product returns and associated customer feedback to improve product quality and customer service.

The SQL syntax will look like the following:

  product_id, reason, 
  COUNT(return_id) AS number_of_returns
FROM returns
GROUP BY product_id, reason
ORDER BY number_of_returns DESC;

In this case:

  • product_id: The ID of the returned product.
  • reason: The reason for the product return.
  • COUNT(return_id) AS number_of_returns: Counts the number of returns for each reason per product.
  • GROUP BY product_id, reason: Groups the data by product ID and return reason.
  • ORDER BY number_of_returns DESC: Orders the products by the number of returns in descending order, highlighting the most problematic items.

Outcome: This analysis provides insights into common reasons for returns, guiding improvements in product quality or customer service interventions.

These SQL techniques in BigQuery can be adapted for various other industries beyond e-commerce, such as finance for risk assessments, healthcare for patient data analysis, and education for learning outcomes tracking, demonstrating the platform's extensive applicability for data-driven decision-making.

BigQuery SQL Best Practices

Following best practices is essential for maintaining efficient, reliable, and cost-effective BigQuery operations.

This segment covers a range of best practices from choosing the right data formats to advanced techniques like partitioning and clustering.

Choosing the Appropriate Data Format

Selecting the right data format is crucial for optimizing query speed and storage efficiency in BigQuery. Different data formats, like CSV, JSON, Avro, or Parquet, have unique strengths and use cases.

  • Avro and Parquet: Ideal for complex nested data structures; they provide better compression and performance.
  • CSV and JSON: Suitable for simpler, flat schemas, but may lead to higher costs and slower performance for large datasets.

Enhancing Performance through Partitioning

Partitioning divides your table into segments, typically based on time or a particular column, which can significantly improve performance and cost efficiency for large datasets.

  • Time-based Partitioning: Use for tables where queries are often filtered by date ranges.
  • Column-based Partitioning: Effective when queries frequently filter on a specific column.

Boosting Performance with Clustering

Clustering reorganizes data in a partitioned table based on the contents of one or more columns. This further enhances query performance by reducing the amount of data scanned.

  • Multi-Column Clustering: Use when your queries often involve filters on multiple columns.
  • Combine with Partitioning: Clustering is most effective when used with partitioned tables.

Effective Log Utilization

Logging in BigQuery helps track operations, understand query performance, and diagnose issues. Effective use of logs can improve system reliability and operational efficiency.

  • Audit Logs: Enable and monitor audit logs to track data access and changes, helping ensure security and compliance.
  • Performance Logs: Use logs to analyze query performance and identify potential optimizations.

By adhering to these best practices, businesses can ensure that their BigQuery deployments are efficient, cost-effective, but also scalable, and secure, allowing for better data management and analysis.

Common Pitfalls in BigQuery SQL Usage

Using BigQuery SQL effectively is crucial for data professionals who want to make the most of Google Cloud's big data tools.

Although BigQuery is a powerful platform for rapidly analyzing large datasets, it's easy to run into common issues that can slow down your queries or skew the data you're working with.

To improve your query performance and ensure your data insights are accurate, it's important to understand these challenges and know how to overcome them. Let's dive into some typical problems and share some tips on how to solve them.

The Risks of Overusing SELECT * Statements

Overuse of SELECT * can severely impact data processing efficiency, especially in environments with large datasets. This practice retrieves all columns from a table, many of which may not be necessary for the analysis at hand, thus consuming additional resources and prolonging processing time. To mitigate this risk, it's advisable to explicitly specify only the required columns in the SELECT statement, optimizing resource usage and query performance.

For instance, running a SELECT * on a 3TB table with ten columns of equal data volume will cost $15 as it scans the entire table.

By narrowing your query to only the three necessary columns, the cost drops to $4.50.

This highlights how targeted queries can significantly reduce expenses, especially when executed multiple times daily.

Mitigating the Impact of Unnecessary Large JOINs

Large JOINs, particularly when poorly optimized, can significantly diminish query performance by demanding extensive resources to match rows across multiple tables. To address this issue, it's essential to optimize JOIN operations by utilizing appropriate indexing, partitioning, and clustering strategies.

Suppose you have two large tables, sales (1TB) and products (500GB), and you wish to analyze data from both.

  project_id.dataset_id.sales_table AS s
  project_id.dataset_id.products AS p
  s.product_id = p.product_id;

Performing a direct join can be costly and slow. Instead of performing a large join, you can create a smaller lookup table and use it for your query just like the following example.

  project_id.dataset_id.product_lookup AS

Then, you can use the Lookup Table in the JOIN for further analysis.

  project_id.dataset_id.sales_table AS s
  project_id.dataset_id.product_lookup AS pl
  s.product_id = pl.product_id;

By reducing unnecessary operations, focusing on efficient data models, and targeting your queries, you can achieve better performance, lower costs, and more effective use of BigQuery’s capabilities.

Optimizing Frequent DML Operations

When working with BigQuery, it's important to remember that it is a data warehouse optimized for analytics rather than a traditional RDBMS designed for frequent data manipulation.

Attempting to perform operations commonly used in RDBMS, such as recreating tables or running extensive DML statements, can result in high costs.

Here is an example of a common inefficient DML Operation:


Instead of frequent deletions and recreations, consider using an additive model. In this approach, new rows are inserted with a timestamp to indicate the latest data and older rows are deleted periodically if historical data is not needed. This method takes advantage of BigQuery's strengths in handling large-scale data appends and analytics.

Example of an Additive Model Inserting New Rows with Timestamps:

INSERT INTO  (, updated_at)

Each new row is inserted with a CURRENT_TIMESTAMP() to indicate when it was added. To query the latest data, you can use a subquery to select rows with the latest timestamp, ensuring you always get the most recent data.

Furthermore, periodic cleanup of old data no longer needed reduces storage costs while keeping your dataset up-to-date.

By understanding and addressing these common pitfalls, users can significantly enhance the robustness and efficiency of their BigQuery SQL operations. These practices improve query speed, reduce costs and ensure that your data environment remains scalable and manageable.

Expand Your Knowledge with Bigquery Functions

Enhance your data analysis skills with these essential BigQuery functions. Mastering these key functions will streamline your workflows and yield more accurate results, boosting your efficiency and effectiveness in handling data.

  • String Functions: These functions allow for the manipulation and formatting of text data, enabling operations such as substring extraction and pattern matching.
  • Conditional Expressions: These expressions enable the execution of different actions based on specified conditions, including CASE and IF statements.
  • Navigation Functions: These functions allow access to data from subsequent or preceding rows without needing to perform self-joins. Examples include LEAD and LAG.
  • Numbering Functions: Provide unique numbers to rows within a partition of a result set, such as ROW_NUMBER, RANK, and DENSE_RANK.
  • Conversion Functions: Used to convert data from one type to another, ensuring compatibility and correct data formatting.
  • Timestamp Functions: Manage timestamp data types, enabling precise time calculations and formatting. Examples include CURRENT_TIMESTAMP, EXTRACT, and FORMAT_TIMESTAMP.
  • Datetime Functions: Work with datetime data types to handle specific operations involving both dates and times. Functions include DATETIME_ADD, DATETIME_SUB, DATETIME_DIFF, and FORMAT_DATETIME.

Build Powerful Reports with OWOX BI BigQuery Reports Extension

Integrating OWOX BI BigQuery Extensions can enhance BigQuery's reporting capabilities. This section provides a comprehensive overview of using the OWOX BI BigQuery Reports Extension to build powerful, insightful reports efficiently.


Access BigQuery Data at Your Fingertips

Make BigQuery corporate data accessible for business users. Easily query data, run reports, create pivots & charts, and enjoy automatic updates

Elevate Your Analytics

By following this guide, data professionals will acquire a robust understanding of BigQuery SQL, equipped with the knowledge to implement efficient, effective, and optimized queries and data solutions in their organizations.

This guide serves as an educational resource and a practical handbook for daily reference in professional settings.


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  • How do I connect BigQuery to my data visualization tools?

    Connect BigQuery to visualization tools by accessing BigQuery Console, creating a dataset, configuring permissions, generating credentials, and integrating with tools like Google Data Studio or Tableau. These steps ensure seamless data integration and visualization capabilities.

  • What's the difference between Legacy SQL and Standard SQL in BigQuery?

    Legacy SQL is older with different syntax and limitations, while Standard SQL offers modern features, ANSI compatibility, and better performance. Standard SQL is recommended for new queries due to its advanced functionality and compliance with SQL standards.

  • How can I optimize my BigQuery queries for cost and performance?

    Optimize BigQuery queries by using Standard SQL, avoiding SELECT *, partitioning tables, clustering data, optimizing joins, and using cached results. These practices enhance performance and reduce costs by efficiently managing query resources.

  • Can I use BigQuery for real-time data analysis?

    BigQuery isn't designed for real-time analysis due to its batch-processing nature, but streaming inserts can enable near-real-time insights with some latency. This method allows for timely data processing while maintaining BigQuery's robust analytical capabilities.

  • How do I use window functions in BigQuery SQL?

    To use window functions in BigQuery SQL, specify the columns and apply the window function using the OVER clause:

    SELECT column1,

      OVER (

        PARTITION BY column3 

        ORDER BY column4) AS window_sum
    FROM table;

    This syntax calculates aggregates over data partitions, allowing for complex analytical queries.

  • What are the best practices for structuring BigQuery datasets and tables?

    Structure BigQuery datasets and tables by separating data into logical datasets, using clear naming conventions, defining schemas, and employing partitioning and clustering. These practices improve query performance and data management efficiency.

  • How do I perform JOIN across datasets in BigQuery?

    Perform dataset JOINs in BigQuery using the following syntax:

    SELECT a.column1, b.column2
    FROM dataset1.table1 AS a
    JOIN dataset2.table2 AS b
    ON a.common_field = b.common_field;

    This combines data from different datasets based on a common field.

  • What are some methods to secure data in BigQuery?

    Secure data in BigQuery by managing permissions, encrypting data at rest and in transit, using IAM roles, audit logs, VPC Service Controls, and implementing data access controls. These methods ensure robust data protection and compliance with security standards.

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