15 BigQuery Interview Questions and Answers

BigQuery is a fully-managed, serverless data warehouse that enables super-fast SQL queries using the processing power of Google’s infrastructure. It is designed to handle large-scale data analytics, making it a popular choice for organizations dealing with vast amounts of data. BigQuery’s ability to seamlessly integrate with other Google Cloud services and its support for standard SQL make it an essential tool for data engineers and analysts.

This article provides a curated selection of BigQuery interview questions and answers to help you prepare effectively. By familiarizing yourself with these questions, you can gain a deeper understanding of BigQuery’s functionalities and demonstrate your expertise during technical interviews.

BigQuery Interview Questions and Answers

1. Write a SQL query to select all columns from a table named sales where the amount is greater than 1000.

To select all columns from a table named sales where the amount is greater than 1000, use:

SELECT * 
FROM sales 
WHERE amount > 1000;

2. Write a SQL query to perform an inner join between two tables, customers and orders, on the customer_id field.

To perform an inner join between customers and orders on the customer_id field in BigQuery, use:

SELECT 
    customers.customer_id,
    customers.customer_name,
    orders.order_id,
    orders.order_date
FROM 
    `project.dataset.customers` AS customers
INNER JOIN 
    `project.dataset.orders` AS orders
ON 
    customers.customer_id = orders.customer_id;

3. How would you use a window function to calculate a running total of sales in a sales table?

Window functions in BigQuery are useful for calculations like running totals. To calculate a running total of sales in a sales table, use:

SELECT
  date,
  sales,
  SUM(sales) OVER (ORDER BY date) AS running_total
FROM
  sales;

4. Write a SQL query to create a partitioned table based on a DATE column named order_date.

Partitioned tables in BigQuery improve query performance and reduce costs. To create a partitioned table based on a DATE column named order_date, use:

CREATE TABLE my_dataset.my_partitioned_table
(
    order_id INT64,
    order_date DATE,
    customer_id INT64,
    amount FLOAT64
)
PARTITION BY order_date;

5. How do you create and call a stored procedure that takes a parameter and returns a result set?

To create and call a stored procedure in BigQuery that takes a parameter and returns a result set:

-- Create the stored procedure
CREATE PROCEDURE my_dataset.my_procedure(param INT64)
BEGIN
  SELECT name, age
  FROM my_dataset.my_table
  WHERE age > param;
END;

-- Call the stored procedure
CALL my_dataset.my_procedure(30);

6. What are some best practices for optimizing query performance in BigQuery?

To optimize query performance in BigQuery, consider:

• Partitioning: Use partitioned tables to reduce data scanned during queries.
• Clustering: Cluster tables based on frequently used columns.
• Query Optimization: Write efficient SQL queries by avoiding SELECT * and using appropriate JOINs.
• Materialized Views: Store results of complex queries for faster access.
• Data Sharding: Distribute data across multiple tables or datasets.
• Monitoring and Analysis: Use BigQuery’s tools to monitor and optimize query performance.

7. Describe the different pricing models available in BigQuery and how you can manage costs effectively.

BigQuery offers two pricing models: on-demand and flat-rate.

1. On-Demand Pricing: Charged based on data processed by queries, suitable for variable workloads.

2. Flat-Rate Pricing: Fixed fee for dedicated query processing capacity, ideal for consistent workloads.

To manage costs effectively:

• Optimize Queries: Write efficient SQL queries to minimize data processed.
• Use Table Partitioning: Limit data scanned by partitioning tables.
• Monitor and Set Budgets: Use cost management tools to monitor spending.
• Use Data Studio or BI Engine: Cache query results to reduce processing.
• Leverage Free Tier: Utilize BigQuery’s free tier for cost savings.

8. How would you implement row-level security in BigQuery?

Row-level security in BigQuery can be implemented using authorized views. Create a view that filters data and grant access to the view instead of the table.

Example:

-- Create a table
CREATE TABLE my_dataset.sales_data (
    sales_id INT64,
    region STRING,
    amount FLOAT64
);

-- Insert sample data
INSERT INTO my_dataset.sales_data (sales_id, region, amount)
VALUES (1, 'North', 100.0), (2, 'South', 200.0), (3, 'East', 150.0);

-- Create an authorized view that filters data based on region
CREATE VIEW my_dataset.region_view AS
SELECT sales_id, region, amount
FROM my_dataset.sales_data
WHERE region = 'North';

-- Grant access to the view
GRANT SELECT ON TABLE my_dataset.region_view TO '[email protected]';

9. Write a SQL query to load data from a CSV file stored in Google Cloud Storage into a BigQuery table.

To load data from a CSV file stored in Google Cloud Storage into a BigQuery table, use:

LOAD DATA
INTO `project_id.dataset_id.table_id`
FROM FILES (
  format = 'CSV',
  uris = ['gs://bucket_name/file_name.csv']
)

10. How do you export data from a BigQuery table to a Google Cloud Storage bucket?

To export data from a BigQuery table to a Google Cloud Storage bucket, use the bq command-line tool:

bq extract --destination_format=CSV 'project_id:dataset_id.table_id' gs://bucket_name/file_name.csv

Or use the BigQuery API in Python:

from google.cloud import bigquery

client = bigquery.Client()

# Define the table and the destination URI
table_ref = client.dataset('dataset_id').table('table_id')
destination_uri = 'gs://bucket_name/file_name.csv'

# Configure the extract job
extract_job = client.extract_table(
    table_ref,
    destination_uri,
    location='US',  # Location must match that of the source table
)

# Wait for the job to complete
extract_job.result()

print('Exported {} to {}'.format(table_ref, destination_uri))

11. Write a SQL query to calculate the distance between two geographical points using BigQuery GIS.

BigQuery GIS provides functions to handle geographical data. To calculate the distance between two geographical points, use the ST_DISTANCE function:

SELECT ST_DISTANCE(
  ST_GEOGPOINT(-73.9857, 40.7484),  -- Point 1: Longitude, Latitude
  ST_GEOGPOINT(-118.2500, 34.0522)  -- Point 2: Longitude, Latitude
) AS distance;

12. Explain how you would set up a streaming data pipeline to ingest real-time data into BigQuery.

To set up a streaming data pipeline to ingest real-time data into BigQuery:

1. Data Source: Identify the source of your real-time data.

2. Data Streaming Service: Use Google Cloud Pub/Sub to collect and stream the data.

3. Data Processing: Use Google Cloud Dataflow to process the streaming data.

4. BigQuery Integration: Configure Dataflow to write the processed data into BigQuery.

Example:

• Create a Pub/Sub topic for data sources to publish messages.
• Set up a Dataflow job to read from the Pub/Sub topic and write to a BigQuery table.
• Configure the BigQuery table schema to match the processed data.

13. Write a complex SQL query that uses CTEs (Common Table Expressions) and window functions to analyze sales data.

To analyze sales data using CTEs and window functions in BigQuery, use:

WITH SalesData AS (
    SELECT
        sales_id,
        product_id,
        sale_date,
        sale_amount,
        ROW_NUMBER() OVER (PARTITION BY product_id ORDER BY sale_date) AS sale_rank
    FROM
        sales_table
),
AggregatedSales AS (
    SELECT
        product_id,
        sale_date,
        sale_amount,
        SUM(sale_amount) OVER (PARTITION BY product_id ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_total,
        AVG(sale_amount) OVER (PARTITION BY product_id ORDER BY sale_date ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) AS moving_avg
    FROM
        SalesData
)
SELECT
    product_id,
    sale_date,
    sale_amount,
    running_total,
    moving_avg
FROM
    AggregatedSales
ORDER BY
    product_id,
    sale_date;

14. Describe how to use table wildcard functions in BigQuery.

Table wildcard functions in BigQuery allow querying multiple tables with a common prefix. This is useful for partitioned tables or tables created daily. The TABLE_DATE_RANGE and TABLE_SUFFIX functions enable operations across these tables.

Example:

SELECT
  *
FROM
  `project.dataset.table_prefix*`
WHERE
  _TABLE_SUFFIX BETWEEN '20220101' AND '20220131'

15. Explain BigQuery’s automatic caching mechanism and how it affects query performance and cost.

BigQuery’s automatic caching mechanism optimizes query performance and reduces costs. When a query is executed, results are stored in a cache for 24 hours. If the same query is run again within this period, results are retrieved from the cache, leading to faster performance and no additional charges.

The caching mechanism works by identifying identical queries. If the query text, project, and other parameters are the same, BigQuery will use the cached results. However, if any part of the query changes, the cache will not be used, and the query will be re-executed.

Not all queries are eligible for caching. Queries using non-deterministic functions or referencing external data sources are not cached. Queries exceeding certain resource limits may also bypass the cache.