Top 18 GCP BigQuery Interview Questions for 2025

What is Google BigQuery?

Google BigQuery is a fully managed, serverless data warehouse that enables scalable analysis over vast datasets using SQL. It's a powerful product of Google Cloud Platform (GCP) designed to process massive read-only data collections quickly and efficiently.

BigQuery allows you to execute SQL queries to solve business problems, analyze data in memory using machine learning, and create analytical reports with real-time evaluations - all without managing infrastructure.

Fundamental BigQuery Interview Questions

1. What is Google BigQuery and what are its key features?

BigQuery is a serverless, highly scalable data warehouse with integrated machine learning capabilities from Google Cloud Platform. Its key features include:

• Fast SQL querying over petabyte-scale datasets
• No infrastructure management required
• Automatic data replication for high availability
• Built-in machine learning capabilities
• Real-time analytics and data streaming
• Integration with other GCP services
• Pay-as-you-go pricing model

2. Explain the architecture of Google BigQuery

Google BigQuery's architecture consists of four major components:

• Dremel: Facilitates the creation of execution trees from SQL queries
• Colossus: Provides columnar storage with compression mechanisms for efficient data storage
• Jupiter: Ensures connectivity between CPU and storage
• Borg: Regulates fault tolerance for Dremel jobs' computational power

3. What are the advantages of using BigQuery over traditional databases?

BigQuery offers several advantages over traditional databases:

• Serverless architecture: No need to provision or manage infrastructure
• Automatic scaling: Handles queries of any size automatically
• Separation of storage and compute: Pay separately for what you use
• High performance: Can query terabytes in seconds and petabytes in minutes
• Built-in ML capabilities: Perform machine learning directly within the warehouse
• Cost-effective: Pay-as-you-go model without upfront costs
• Data sharing and collaboration: Easy to share datasets and collaborate

Technical BigQuery Interview Questions

4. How does BigQuery handle data loading?

You can load data into BigQuery through various methods:

• Upload data files using the BigQuery web UI
• Load data from local files or Google Cloud Storage using the command-line tool
• Stream data in real-time using the BigQuery API
• Use BigQuery Data Transfer Service for automated data loading from various sources
• Import data from other Google services like Google Analytics or Google Ads

5. What is partitioning in BigQuery and why is it important?

Partitioning in BigQuery is a method of dividing large tables into smaller, more manageable segments based on a specific criterion such as date, ingestion time, or integer values.

Partitioning is important because it:

• Improves query performance by limiting the amount of data scanned
• Reduces costs by only scanning relevant partitions
• Enables more efficient data organization
• Makes time-series data analysis more effective
• Simplifies data lifecycle management

6. What is clustering in BigQuery and how does it differ from partitioning?

Clustering in BigQuery is a technique where data is automatically organized based on the contents of specified columns.

Key differences from partitioning:

• Partitioning creates distinct segments, while clustering organizes data within partitions
• You can cluster on up to four columns
• Clustering works well for high-cardinality columns
• Partitioning has a limit of 4,000 partitions, while clustering has no such limit
• Partitioning works best for time-based or limited-range columns

7. How would you optimize a slow-running query in BigQuery?

To optimize a slow-running query in BigQuery:

• Use partitioning and clustering to reduce data scanned
• Limit the columns selected (avoid SELECT *)
• Filter data early in the query
• Use approximate aggregations when exact counts aren't needed
• Materialize commonly used subqueries into tables or views
• Use appropriate data types to minimize storage and processing
• Review the query execution plan to identify bottlenecks
• Consider denormalizing data for analytical queries

8. What are BigQuery slots and how do they affect query performance?

BigQuery slots are units of computational capacity used to execute SQL queries. The number of slots determines the level of parallelism and thus affects query execution speed—more slots can lead to faster processing times.

Users can choose between on-demand pricing (where slots are allocated dynamically) or flat-rate pricing for dedicated slot capacity. Effective management of slots, especially in a shared environment, is crucial for optimizing performance and costs.

BigQuery SQL Questions

9. How would you create a view in BigQuery?

To create a view in BigQuery, you can use:

Views in BigQuery are virtual tables defined by a SQL query. They don't store data but provide a way to organize and reuse complex queries.

10. How would you identify and remove duplicate records in a BigQuery table?

To identify duplicates:

To remove duplicates while keeping the original table name:

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

Standard SQL in BigQuery is the newer, preferred approach for querying data. It's based on the SQL:2011 standard and offers several advantages over Legacy SQL:

• Better performance
• Greater support for SQL standard features
• Better compatibility with other SQL-based systems
• More advanced functions and operators
• Support for complex data types like ARRAY and STRUCT

Legacy SQL, based on the SQL:2003 standard, is still supported for backward compatibility but is generally not recommended for new projects.

Advanced BigQuery Interview Questions

12. How does BigQuery ML work, and what types of models can you create?

BigQuery ML allows you to create and execute machine learning models using standard SQL queries. It enables data scientists and analysts to build models directly where their data is stored.

Models you can create in BigQuery ML include:

• Linear regression for forecasting
• Binary and multiclass logistic regression for classification
• K-means clustering for segmentation
• Time series forecasting models
• Matrix factorization for recommendation systems
• TensorFlow models (imported)
• XGBoost models for advanced classification and regression

13. How would you design a data pipeline that loads data into BigQuery?

A well-designed data pipeline for BigQuery would include:

1. Data Source Identification: Determine where the data is coming from (databases, apps, IoT devices, etc.)
2. Extraction: Use appropriate tools like Dataflow or Dataproc to extract data
3. Transformation: Clean, validate, and transform data to match BigQuery schema
4. Loading: Choose the optimal loading method (batch vs. streaming)
5. Scheduling: Set up Cloud Composer or Cloud Scheduler for automation
6. Monitoring: Implement monitoring and alerting for pipeline health
7. Error Handling: Design robust error handling and retry mechanisms
8. Cost Optimization: Implement strategies to minimize costs
9. Security: Ensure proper access controls and encryption

14. How would you ensure GDPR compliance when storing data in BigQuery?

To ensure GDPR compliance when storing data in BigQuery:

• Encrypt sensitive data before storing it in BigQuery
• Implement column-level security for personally identifiable information (PII)
• Use data access control systems to limit access to authorized personnel
• Set up appropriate data retention policies and automated deletion
• Implement audit logging to track who accesses what data
• Create processes for handling data subject access requests
• Consider using BigQuery's data masking features for sensitive information
• Document your compliance measures and data processing activities

15. What best practices would you follow for cost control in BigQuery?

Best practices for BigQuery cost control include:

• Partition and cluster tables appropriately to reduce data scanned
• Use the query validator and dry run to estimate costs before running queries
• Implement cost controls and alerts to monitor and cap daily spending
• Leverage BigQuery's caching to avoid re-running the same queries
• Consider using flat-rate pricing for predictable workloads
• Optimize queries to reduce data processed
• Use views for common query patterns
• Implement proper table lifecycle management
• Regularly archive or delete unused data

Scenario-Based BigQuery Interview Questions

16. You have a dataset with millions of records that needs to be updated daily. What's the most efficient way to handle this in BigQuery?

For efficiently updating millions of records daily:

1. Create a partitioned table based on date
2. Load new data into a separate staging table
3. Use a merge statement to update the target table:
4. Schedule this operation using Cloud Composer or Cloud Scheduler
5. Monitor performance and adjust partitioning strategy if needed

17. Your organization needs to analyze streaming data in real-time. How would you implement this using BigQuery?

To analyze streaming data in real-time with BigQuery:

1. Set up a data streaming pipeline using Pub/Sub to ingest real-time data
2. Use Dataflow to process and transform streaming data
3. Stream data directly into BigQuery using the streaming API
4. Create materialized views to pre-compute common aggregations
5. Implement a dashboard using Data Studio or Looker to visualize real-time insights
6. Set up alerts based on specific conditions in the data

18. You need to optimize query performance for a dashboard that uses the same dataset repeatedly. What approach would you take?

To optimize dashboard query performance:

1. Create materialized views for commonly used queries
2. Implement appropriate partitioning and clustering on the tables
3. Pre-aggregate data into summary tables for dashboard metrics
4. Use BigQuery BI Engine for interactive analysis
5. Optimize query patterns to minimize data scanned
6. Consider caching dashboard results at the application level
7. Schedule data refreshes during off-peak hours

Conclusion‍

Preparing for a BigQuery interview requires understanding both the fundamentals and advanced features of this powerful data warehouse. By mastering these common interview questions, you'll demonstrate your expertise and readiness for roles that involve working with BigQuery.
Remember that practical experience working with BigQuery will give you an edge in interviews. If possible, work on real projects or create sample projects that showcase your skills in data loading, querying, optimization, and integration with other GCP services.
Good luck with your interview preparation, and may your future be filled with successful BigQuery queries!