GCP BigQuery SQL Interview Questions and Answers | GCP Data Analyst | GCP Data Engineer 2025

Top most important interview questions and answers for GCP BigQuery, covering both theoretical and practical aspects, including SQL query examples:

---

Theoretical Questions

1. What is Google BigQuery?

• Answer: BigQuery is a fully managed, serverless, and highly scalable data warehouse provided by Google Cloud Platform (GCP). It allows you to store, query, and analyse large datasets using SQL-like queries. It enables super-fast SQL queries using the processing power of Google’s infrastructure. It is designed for analysing large datasets and supports real-time analytics.
• It uses Dremel technology for executing queries in a distributed manner.
• Data is stored in a columnar format, which enables fast scanning and aggregation.
• BigQuery supports batch and streaming data ingestion.

2. What are the key features of BigQuery?

• Answer:
  • Serverless architecture: No infrastructure management required.
  • Scalable: Handles petabytes of data.
  • Fast: Uses Google’s Dremel engine for low-latency queries.
  • Cost-effective: Pay-as-you-go pricing model.
  • Columnar storage format for faster querying
  • Integration: Works seamlessly with other GCP services like Dataflow, Dataproc, and AI Platform.
  • Supports standard SQL.
  • Built-in machine learning (BigQuery ML)

3. What is the difference between BigQuery and traditional relational databases?

• Answer:
  • BigQuery is designed for OLAP (Online Analytical Processing), while traditional databases are often used for OLTP (Online Transaction Processing).
  • BigQuery is columnar, making it faster for analytical queries.
  • BigQuery is serverless, whereas traditional databases require manual scaling and maintenance.

What are the different types of tables in BigQuery?

Answer:

• Native Tables – Stored in BigQuery storage
• External Tables – Reference data from sources like Cloud Storage or Google Drive
• Materialized Views – Precomputed query results stored for faster access
• Partitioned Tables – Dividing large tables by date or other fields
• Clustered Tables – Organizing data within tables based on specific columns

4. What is a BigQuery dataset?

• Answer: A dataset is a container for tables, views, and models in BigQuery. It is the top-level organizational unit in BigQuery and is used to group related tables and control access permissions.

5. How does BigQuery handle partitioning and clustering?

• Answer:
  • Partitioning: Divides a table into smaller segments based on a specific column (e.g., date). Improves query performance and reduces costs.
  • Clustering: Sorts data within partitions based on one or more columns. Further optimizes query performance by reducing the amount of data scanned.

6. What is the difference between BigQuery Standard SQL and Legacy SQL?

• Answer:
  • Standard SQL is the default and recommended SQL dialect in BigQuery. It is ANSI-compliant and supports advanced features like arrays, structs, and nested fields.
  • Legacy SQL is an older dialect and is not recommended for new projects.

7. What is the role of BigQuery Storage API?

• Answer: The BigQuery Storage API allows you to read data from BigQuery tables at high speed and in parallel. It is useful for applications that need to process large datasets programmatically.

8. How does BigQuery pricing work?

• Answer:
  • Storage costs: Based on the amount of data stored.
  • Query costs: Based on the amount of data processed by queries.
  • Streaming costs: Ingesting data via streaming APIs incurs additional costs.
  • Flat-rate pricing: Option to pay a fixed monthly fee for unlimited queries.

What is the difference between partitioning and clustering in BigQuery?

Answer:

Feature	Partitioning	Clustering
Purpose	Divides data into logical segments	Organizes data based on column values
How it’s defined	Based on date/time or integer column	Based on specific columns
Query performance	Faster queries when filtering on partition column	Faster queries when filtering, grouping, or ordering by cluster column
Cost	Lower storage cost for partition pruning	No additional storage cost

---

Practical Questions

1. Write a SQL query to create a table in BigQuery.

   CREATE TABLE my_dataset.my_table (
       id INT64,
       name STRING,
       created_at TIMESTAMP
   );

2. Write a SQL query to load data from Google Cloud Storage (GCS) into BigQuery.

   LOAD DATA OVERWRITE my_dataset.my_table
   FROM FILES (
       format = 'CSV',
       uris = ['gs://my-bucket/my-file.csv']
   );

3. Write a SQL query to export data from BigQuery to Google Cloud Storage.

   EXPORT DATA OPTIONS (
       format = 'CSV',
       uri = 'gs://my-bucket/exported-data-*.csv'
   ) AS
   SELECT * FROM my_dataset.my_table;

4. Write a SQL query to partition a table by date.

   CREATE TABLE my_dataset.my_partitioned_table (
       id INT64,
       name STRING,
       event_date DATE
   )
   PARTITION BY event_date;

5. Write a SQL query to cluster a table by a specific column.

   CREATE TABLE my_dataset.my_clustered_table (
       id INT64,
       name STRING,
       event_date DATE
   )
   PARTITION BY event_date
   CLUSTER BY name;

6. Write a SQL query to find duplicate rows in a table.

   SELECT id, name, COUNT(*)
   FROM my_dataset.my_table
   GROUP BY id, name
   HAVING COUNT(*) > 1;

7. Write a SQL query to calculate the running total of a column.

   SELECT id, event_date, value,
          SUM(value) OVER (ORDER BY event_date ROWS UNBOUNDED PRECEDING) AS running_total
   FROM my_dataset.my_table;

8. Write a SQL query to join two tables.

   SELECT a.id, a.name, b.order_id, b.order_date
   FROM my_dataset.table_a a
   JOIN my_dataset.table_b b
   ON a.id = b.customer_id;

9. Write a SQL query to find the top 5 most frequent values in a column.

   SELECT name, COUNT(*) AS frequency
   FROM my_dataset.my_table
   GROUP BY name
   ORDER BY frequency DESC
   LIMIT 5;

10. Write a SQL query to calculate the average value of a column for each day.

   SELECT event_date, AVG(value) AS average_value
   FROM my_dataset.my_table
   GROUP BY event_date;

11. Write a SQL query to use a window function to rank rows.

WINDOW functions perform calculations across a set of rows related to the current row. Example

   SELECT id, name, value,
          RANK() OVER (ORDER BY value DESC) AS rank
   FROM my_dataset.my_table;

SELECT
  employee_id,
  department,
  salary,
  RANK() OVER(PARTITION BY department ORDER BY salary DESC) AS rank
FROM employees;

12. Write a SQL query to delete rows from a table.

   DELETE FROM my_dataset.my_table
   WHERE event_date < '2023-01-01';

13. Write a SQL query to update rows in a table.

   UPDATE my_dataset.my_table
   SET name = 'Updated Name'
   WHERE id = 123;

14. Write a SQL query to create a view.

   CREATE VIEW my_dataset.my_view AS
   SELECT id, name
   FROM my_dataset.my_table
   WHERE event_date = '2023-10-01';

15. Write a SQL query to use ARRAY and STRUCT data types.

   SELECT id, name,
          ARRAY(SELECT value FROM UNNEST(scores)) AS scores_array,
          STRUCT(city, country) AS location
   FROM my_dataset.my_table;

---

Advanced Questions

1. How do you optimize query performance in BigQuery?

• Answer:
  • Use partitioning and clustering tables.
  • Avoid SELECT *; only query the columns you need.
  • Use approximate functions (e.g., APPROX_COUNT_DISTINCT) for large datasets.
  • Optimize joins by filtering data early.
  • Use materialized views for frequently queried data ( to store precomputed results).

2. What are nested and repeated fields in BigQuery?

• Answer:
  • Nested fields: A field that contains a STRUCT.
  • Repeated fields: A field that contains an ARRAY.
  • Example:
    SELECT id, name, ARRAY(SELECT value FROM UNNEST(scores)) AS scores_array, STRUCT(city, country) AS location FROM my_dataset.my_table;

3. How do you handle large result sets in BigQuery?

• Answer:
  • Use pagination with LIMIT and OFFSET.
  • Export results to GCS for large datasets.
  • Use the BigQuery Storage API to read data programmatically.

4. What is the difference between BigQuery BI Engine and Looker?

• Answer:
  • BigQuery BI Engine: An in-memory analysis service that accelerates SQL queries and integrates with BI tools.
  • Looker: A data exploration and visualization tool that connects to BigQuery and other data sources.

5. What is the purpose of the ARRAY data type in BigQuery?

Answer:

• An ARRAY is an ordered list of values of the same data type.
• Useful for handling repeated fields and nested data structures.
• Example:

SELECT [1, 2, 3, 4] AS numbers;

Result:

numbers
[1, 2, 3, 4]

6. What is UNNEST in BigQuery? How is it used with arrays?

Answer:

• UNNEST is used to flatten arrays into individual rows.
• Example:

WITH sample_data AS (
  SELECT ['apple', 'banana', 'orange'] AS fruits
)
SELECT fruit
FROM sample_data, UNNEST(fruits) AS fruit;

Result:

fruit
apple
banana
orange

7. How do you create and query a nested record in BigQuery?

Answer:

• Nested records use STRUCT and ARRAY types.
• Example:

sqlCopyEditWITH person AS (
  SELECT STRUCT('John' AS first_name, 'Doe' AS last_name) AS full_name
)
SELECT full_name.first_name, full_name.last_name FROM person;

Result:

first_name	last_name
John	Doe

8. How do you handle NULL values in BigQuery?

Answer:

• Use IFNULL, COALESCE, or SAFE_CAST to handle NULL values. Example:

sqlCopyEditSELECT IFNULL(NULL, 'default') AS result;

Result:

result
default

---

9. What is a STRUCT in BigQuery?

Answer:

• A STRUCT is a container of named fields.
• Example:

sqlCopyEditSELECT STRUCT('Alice' AS first_name, 'Johnson' AS last_name) AS person;

Result:

person.first_name	person.last_name
Alice	Johnson

---

10. How do you create a partitioned table in BigQuery?

Answer:

• Partition by a DATE or TIMESTAMP column:

sqlCopyEditCREATE TABLE my_dataset.sales
(
  order_id INT64,
  order_date DATE
)
PARTITION BY order_date;

11. How does BigQuery handle schema changes?

Answer:

• Add new columns – Supported without downtime.
• Relax column modes – Change from REQUIRED → NULLABLE.
• Rename or delete columns – Not supported directly (create a new table).

---

12. Explain the use of MERGE in BigQuery.

Answer:

• MERGE is used to insert, update, or delete rows based on a condition. Example:

MERGE INTO target_table AS t
USING source_table AS s
ON t.id = s.id
WHEN MATCHED THEN
  UPDATE SET t.value = s.value
WHEN NOT MATCHED THEN
  INSERT (id, value) VALUES (s.id, s.value);

13. How would you extract the top 3 highest sales per region using BigQuery?

Answer:

sqlCopyEditSELECT *
FROM (
  SELECT
    region,
    sales,
    RANK() OVER(PARTITION BY region ORDER BY sales DESC) AS rank
  FROM sales_data
)
WHERE rank <= 3;

---

14. How would you clean up duplicate rows in a table?

Answer:

sqlCopyEditCREATE OR REPLACE TABLE clean_table AS
SELECT DISTINCT * FROM original_table;

15. How do you handle JSON data in BigQuery?

Answer:

• Use JSON_EXTRACT or JSON_QUERY to parse JSON fields. Example:

SELECT JSON_EXTRACT(json_column, '$.key') AS value FROM my_table;

(Due to technical issues, the search service is temporarily unavailable.)

16. UNNEST() in BigQuery

What is UNNEST()?

• UNNEST() is a function in BigQuery used to flatten arrays into individual rows. It is commonly used when working with nested and repeated fields (arrays) in BigQuery.
• When you have a column with an array type, UNNEST() allows you to expand the array into multiple rows, making it easier to query and analyze the data.

Example of UNNEST()

Suppose you have a table my_table with the following schema:

id INT64,
name STRING,
scores ARRAY<INT64>

You can use UNNEST() to flatten the scores array:

SELECT id, name, score
FROM my_table,
UNNEST(scores) AS score;

This query will return a row for each value in the scores array.

---

17. Arrays in BigQuery

What are Arrays?

• Arrays in BigQuery are ordered lists of elements of the same data type.
• They are useful for storing multiple values in a single column, such as a list of tags, scores, or categories.
• Arrays are represented as ARRAY<data_type> in BigQuery.

Example of Working with Arrays

1. Creating a Table with an Array Column:

   CREATE TABLE my_dataset.my_table (
       id INT64,
       name STRING,
       scores ARRAY<INT64>
   );

2. Inserting Data with Arrays:

   INSERT INTO my_dataset.my_table (id, name, scores)
   VALUES (1, 'Alice', [85, 90, 78]),
          (2, 'Bob', [92, 88, 91]);

3. Querying Arrays:

• To access individual elements of an array:
  sql SELECT id, name, scores[OFFSET(0)] AS first_score FROM my_dataset.my_table;
• To count the number of elements in an array:
  sql SELECT id, name, ARRAY_LENGTH(scores) AS num_scores FROM my_dataset.my_table;

3. Filtering Rows Based on Array Values:

   SELECT id, name
   FROM my_dataset.my_table
   WHERE 90 IN UNNEST(scores);

---

18. Identifying Source Data Errors During Ingestion into the Stage Layer

When ingesting data into a staging layer in BigQuery, errors can occur due to issues like:

• Invalid data types
• Missing required fields
• Schema mismatches
• Duplicate rows
• Data corruption

Steps to Identify Source Data Errors

1. Use Data Validation Queries:

• Write SQL queries to validate the data after ingestion.
• Example: Check for null values in required fields.
  sql SELECT * FROM my_dataset.stage_table WHERE required_column IS NULL;

1. Use ERROR Columns in BigQuery:

• When using LOAD DATA or INSERT statements, BigQuery provides an errors column in the job metadata that contains details about any errors encountered during ingestion.
• Example:
  sql SELECT * FROM `region-us`.INFORMATION_SCHEMA.JOBS WHERE job_type = 'LOAD' AND error_result IS NOT NULL;

1. Use WRITE_TRUNCATE or WRITE_APPEND with Validation:

• When loading data, use WRITE_TRUNCATE or WRITE_APPEND with validation queries to ensure data quality.
• Example:
  sql LOAD DATA INTO my_dataset.stage_table FROM FILES ( format = 'CSV', uris = ['gs://my-bucket/my-file.csv'] ) WITH PARTITION BY event_date AND WRITE_TRUNCATE;

1. Use TRY Functions for Error Handling:

• Use TRY functions to handle errors during data transformation.
• Example:
  sql
• SELECT id, name, TRY_CAST(value AS INT64) AS value_int FROM my_dataset.stage_table;

1. Log Errors to a Separate Table:

• Create a separate table to log errors encountered during ingestion.
• Example:
  sql
• INSERT INTO my_dataset.error_log (job_id, error_message, timestamp) SELECT job_id, error_result.message, creation_time FROM `region-us`.INFORMATION_SCHEMA.JOBS WHERE error_result IS NOT NULL;

1. Use Dataflow or Cloud Functions for Pre-Validation:

• Use tools like Google Dataflow or Cloud Functions to validate data before loading it into BigQuery.
• Example: Validate CSV files in GCS before ingestion.

1. Check for Schema Mismatches:

• Compare the schema of the source data with the target table.
• Example:
  sql SELECT column_name, data_type FROM `my_dataset`.INFORMATION_SCHEMA.COLUMNS WHERE table_name = 'stage_table';

1. Use SAFE Functions:

• Use SAFE functions to handle null or invalid values gracefully.
• Example:
  sql
• SELECT id, name, SAFE_DIVIDE(total, count) AS average FROM my_dataset.stage_table;

---

Example: Identifying Errors in a Staging Table

Suppose you have a staging table stage_table and want to identify rows with errors:

-- Check for null values in required columns
SELECT *
FROM my_dataset.stage_table
WHERE required_column IS NULL;

-- Check for invalid data types
SELECT *
FROM my_dataset.stage_table
WHERE SAFE_CAST(column_name AS INT64) IS NULL;

-- Check for duplicate rows
SELECT column_name, COUNT(*)
FROM my_dataset.stage_table
GROUP BY column_name
HAVING COUNT(*) > 1;

-- Log errors to a separate table
INSERT INTO my_dataset.error_log (job_id, error_message, timestamp)
SELECT job_id, error_result.message, creation_time
FROM `region-us`.INFORMATION_SCHEMA.JOBS
WHERE error_result IS NOT NULL;

---

By using these techniques, you can effectively identify and handle errors in source data during ingestion into the staging layer in BigQuery.

---

These questions and answers should help you prepare for both theoretical and practical aspects of GCP BigQuery interviews.

• Mastering GCP BigQuery: UNNEST() Function and Arrays Explained with Examples
• How to Use UNNEST() in BigQuery to Flatten Arrays for Better Data Analysis
• Working with Arrays in BigQuery: A Complete Guide for Data Engineers
• Identifying and Handling Data Errors in BigQuery During Ingestion
• Top Techniques to Validate and Debug Data in BigQuery Staging Layer
• BigQuery Arrays and UNNEST(): Simplify Complex Data Queries
• Error Handling in BigQuery: Best Practices for Data Ingestion
• How to Use BigQuery for Data Validation and Error Logging
• BigQuery Tips: Flatten Arrays with UNNEST() and Optimize Queries
• Data Ingestion in BigQuery: How to Identify and Fix Source Data Errors

TAGS : GCP BigQuery BigQuery UNNEST(), BigQuery Arrays, Data Ingestion in BigQuery, BigQuery Error Handling, BigQuery Data Validation, BigQuery Staging Layer, BigQuery SQL Queries, BigQuery Best Practices, Google Cloud Platform, Secondary Tags:, Flatten Arrays in BigQuery, BigQuery Nested Data, BigQuery Partitioning and Clustering, BigQuery Data Quality, BigQuery Data Engineering, BigQuery Data Transformation, BigQuery Data Analysis, BigQuery Data Loading, BigQuery Data Errors, BigQuery Troubleshooting, Long-Tail Keywords:, How to use UNNEST() in BigQuery, Working with arrays in BigQuery, BigQuery flatten nested data, Identify data errors in BigQuery, Validate data in BigQuery staging layer, BigQuery error logging best practices, Optimize BigQuery queries with UNNEST(), BigQuery data ingestion tips, Debugging BigQuery data errors, BigQuery arrays and structs explained