The Top 15 Google BigQuery Interview Questions for 2026

Google BigQuery is one of the most popular cloud data warehouses today, enabling organizations to store and analyze massive amounts of data with ease. As more companies adopt BigQuery, demand is growing for BigQuery experts to build and manage these data warehousing solutions.

If you‘re a data professional interviewing for a role involving BigQuery, it‘s crucial to prepare for the types of questions you may encounter. In this post, we‘ll cover the 15 most common BigQuery interview questions, updated for 2023. We‘ll delve into key BigQuery concepts, query writing, architecture, and more. Let‘s get started!

BigQuery Basics

First, let‘s review some fundamental information about BigQuery that you should know:

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

BigQuery is a fully-managed, serverless data warehouse that enables super-fast SQL querying using the processing power of Google‘s infrastructure. Some key features include:

• Scalable, high-performance querying of massive datasets, up to petabytes
• Serverless architecture that automatically scales storage and compute
• Highly available and durable storage with built-in replication and failover
• Real-time data streaming and querying
• Built-in machine learning capabilities
• Integration with other Google Cloud services and external tools
• Security and compliance certifications like HIPAA, ISO, and more

2. What are the main benefits of using BigQuery?

Some of the biggest advantages of BigQuery are:

• Minimal infrastructure management since there are no servers to provision or maintain
• Automatic scaling of storage and compute to handle any data volume or concurrent queries
• Ability to query multi-petabyte datasets in seconds with optimized columnar storage and distributed query engine
• Real-time data ingestion and analysis to power live dashboards with the most current data
• Lower compute costs with automated storage optimization and granular on-demand pricing
• Robust access control and security with Cloud Identity and Access Management (IAM) and virtual private clouds

BigQuery Architecture and Data Model

To work effectively with BigQuery, it‘s important to understand its underlying architecture and how data is structured. Here are some common questions:

3. Explain BigQuery‘s architecture and how it enables fast querying.

BigQuery leverages Google‘s Dremel query execution engine and Colossus file system to rapidly process queries across massive datasets. Some key architectural components are:

• Columnar data storage: Data is stored in a columnar format optimized for fast scans and aggregations
• Tree architecture: Queries are broken into small sub-queries that are distributed across many machines and executed in parallel
• Shared multi-tenancy: Resources are dynamically allocated across users while maintaining isolation
• Separation of compute and storage: Decoupling allows independent scaling and per-second billing

This architecture enables interactive analysis of datasets containing billions of rows in seconds. Queries are automatically optimized with intelligent caching, indexing, and data pruning.

4. How is data modeled and structured in BigQuery?

BigQuery supports both relational tables and nested/repeated fields for semi-structured data. This flexible model enables:

• Denormalization to store related fields together for fast retrieval
• Nested and repeated fields to avoid joins and support NoSQL-like data
• Support for arrays and structs as column types

Datasets are top-level containers for tables and views. Tables define how data is structured with a schema containing column names, data types, and modes. Views are virtual tables defined by SQL queries that are executed on-demand.

5. How does BigQuery handle schema changes and updates?

BigQuery is designed to support schema evolution as data and requirements change:

• New columns can be added without impacting existing queries
• Columns can be marked as NULLABLE to support optional values
• Table schemas can be updated to relax existing fields from REQUIRED to NULLABLE
• Schema updates are atomic and require no down time
• BigQuery supports automatic schema detection for supported file formats

However, some operations like removing or renaming columns require creating a new table. You can copy existing data into the new table using a CREATE TABLE AS SELECT statement.

Querying and Analyzing Data with BigQuery

Of course, one of the most important skills for a BigQuery professional is writing efficient and complex queries to analyze data. Let‘s look at some questions that test querying knowledge:

6. What are the main clauses used in BigQuery SQL?

BigQuery supports standard SQL (2011) and a legacy SQL dialect. In standard SQL, the main clauses are:

• SELECT: Retrieves rows from a table and enables filtering, grouping, and aggregation
• FROM: Specifies the tables to query
• WHERE: Filters rows based on specific conditions
• GROUP BY: Groups rows by one or more columns to calculate aggregates
• HAVING: Filters grouped rows based on aggregate conditions
• ORDER BY: Sorts results by one or more columns
• LIMIT: Restricts the number of rows returned

BigQuery also supports set operators like UNION, INTERSECT, and EXCEPT to combine results from multiple queries. Common table expressions (CTEs) allow defining named subqueries for reuse and readability.

7. What are the supported data types in BigQuery?

BigQuery supports the following data types:

• Integer: Signed 64-bit integers
• Float: 64-bit IEEE binary floating-point values
• Numeric: Exact numeric values with 38 digits of precision and 9 decimal digits of scale
• Boolean: True or false values
• String: Variable-length character data up to 10MB
• Bytes: Variable-length binary data up to 10MB
• Date: Represents a logical calendar date
• Time: Represents a time, independent of a specific date
• Datetime: Represents a point in time as a combined date and time
• Timestamp: Represents an absolute point in time with microsecond precision
• Geography: Represents a set of points on the Earth‘s surface
• JSON: Represents data formatted as JSON
• Array: A collection of any data type, including nested arrays
• Struct: A container of named fields, each of which can be any data type

8. How do you handle NULL values in BigQuery?

By default, BigQuery treats NULLs as a distinct value that is greater than all other values. You can check for NULL using the IS NULL operator. Some specific NULL handling tips:

• Aggregations like COUNT and SUM ignore NULL values, while AVG skips rows with NULL
• String functions generally return NULL if any argument is NULL
• JOIN conditions with NULL on both sides never match; use IS NOT DISTINCT FROM to join NULL values
• ORDER BY places NULL values at the end for ascending sort and start for descending
• COALESCE returns the first non-NULL value in a list of expressions
• IFNULL returns the second argument if the first is NULL

9. What are some best practices for optimizing BigQuery queries?

BigQuery automatically optimizes queries, but there are some best practices to ensure peak performance:

• Filter results using partitioned columns and clustering keys
• Use window functions instead of self-joins for ranking and row comparisons
• Avoid repeatedly transforming the same data by materializing results in a new table or a CTE
• Minimize data scanned by filtering early, selective columns, and approximate aggregation
• Use LIMIT to preview results and reduce costs of unintentional scans
• Avoid cross joins on large tables which can explode the result set
• Take advantage of BigQuery BI Engine to accelerate dashboard queries

Data Ingestion and Export

BigQuery supports several methods for ingesting data from different sources and exporting data for further analysis. Here are some questions on data movement:

10. What are the main ways to load data into BigQuery?

BigQuery offers multiple options for batch and streaming data ingestion:

• Batch loading files (CSV, JSON, Avro, etc.) from Cloud Storage or local sources
• Streaming data one record at a time via API inserts or client libraries
• Loading data from Google Drive, sheets, cloud databases, or external sources using Cloud Data Fusion and partner connectors
• Scheduled transfers from Amazon S3, Google Ad Manager, Campaign Manager, and search consoles
• Importing data from Teradata, Amazon Redshift, or other databases using BigQuery Data Transfer Service

Administrators can also create Reservations to allocate a minimum number of slots to queries in a project for guaranteed performance.

11. How do you export data from BigQuery?

BigQuery enables exporting data to:

• Google Cloud Storage in CSV, JSON, or Avro formats
• Google Sheets for smaller result sets
• An external database or application using federated data sources and EXTERNAL_QUERY
• To Google Data Studio or other BI tools using pre-built connectors

Data can be unloaded from a table, view, or query result. BigQuery also supports scheduled exports to automate workflows.

BigQuery Administration and Security

When managing BigQuery deployments, there are important administration and security capabilities to understand:

12. How do you control access to data in BigQuery?

BigQuery provides several methods to secure data access:

• Identity and Access Management (IAM) to grant permissions at the project, dataset, table, and column levels
• Authorized views to share query results with particular users or groups without giving access to underlying tables
• Table ACLs to grant granular privileges like read or write access to specific users
• Column-level security to restrict access to sensitive fields
• Data masking to obfuscate column values in query results

IAM offers predefined roles for common use cases like data viewer, editor, and owner. Administrators can also define custom roles with specific permissions.

13. What options exist for monitoring and troubleshooting BigQuery?

BigQuery integrates with Google Cloud‘s operations suite to monitor performance and resolve issues:

• Cloud Monitoring provides dashboards and alerts to track key metrics like query execution, slot utilization, and table/partition sizes over time
• Cloud Logging records all queries, job statuses, and error messages for debugging and auditing
• BigQuery‘s query plan explanation and timeline help troubleshoot slow queries
• Reservations monitor slot usage and allocate dedicated resources to mission-critical workloads
• BigQuery‘s information schema views provide metadata about tables, jobs, and data access

In addition, BigQuery offers built-in data validation during ingestion to identify and handle invalid records based on table schemas.

BigQuery Scenario Questions

Finally, BigQuery interviews often include scenario-based questions that test your ability to design an effective solution given a problem statement. For example:

14. Your company wants to analyze user interactions with their website over the last 12 months, which includes over 5 billion events. How would you design a BigQuery data pipeline to ingest, process, and serve this data to analysts and data scientists?

For a large event stream, I would recommend:

1. Implement an event tracking system (Google Tag Manager or Segment) to capture user interactions and send them to BigQuery via the streaming API as JSON records

2. Define a BigQuery table to store the raw events with a schema matching the JSON (using STRUCT fields for nested data). Partition the table by event date to optimize queries.

3. Create a real-time dashboard in Google Data Studio to monitor key metrics like unique users, page views, sessions, etc. This can query the raw event table directly.

4. Run a daily batch ETL job (Dataflow or Cloud Composer) to transform and aggregate the raw events into an analytics table. Materialize common dimensions like sessions, users, and content. Partition by event date and cluster by core dimensions.

5. Grant the analytics team access to the aggregated tables in BigQuery and connect them to their preferred BI and data science tools.

6. Automate table retention based on the company‘s data lifecycle policy. Consider archiving older data in Cloud Storage.

This approach leverages BigQuery‘s streaming ingestion, automatic scaling, partitioning, and federated access capabilities to build a flexible event analytics pipeline.

15. You need to provide daily sales reports to regional managers in your company. Each manager should only see data for their respective region. How can you implement this in BigQuery while minimizing query costs?

To enable self-serve regional reporting, I would:

1. Create a base sales fact table partitioned by date and clustered by region. This will collocate data for each region and optimize queries.

2. Create a view for each region that filters the base table to only rows for that region.

3. Grant each manager access to their respective regional view. This limits data access without having to create copies.

4. Provide a tool like Google Data Studio for managers to build and view their own sales reports based on the regional views.

5. Schedule a daily job to update the base sales table and keep the views fresh.

By using views instead of separate tables, you avoid redundant storage and reduce the administrative overhead. Partitioning and clustering the base table ensures even complex queries remain performant and cost-effective.

Conclusion and Additional Resources

Acea BigQuery interview requires thorough knowledge of BigQuery‘s core features, SQL dialect, performance best practices, and practical applications. By studying the questions and concepts covered in this post, you‘ll be well prepared to demonstrate your BigQuery expertise.

Some additional resources to level up your BigQuery skills:

• Google‘s BigQuery documentation and quick start tutorials
• Kaggle‘s BigQuery datasets and integration guide
• "BigQuery: The Definitive Guide" by Valliappa Lakshmanan and Jordan Tigani
• BigQuery certification courses on Coursera and Qwiklabs
• Sample BigQuery projects on GitHub

Best of luck with your BigQuery interview! Feel free to comment with any other questions you encountered in your interview process.