15 GCP BigQuery Interview Questions and Answers

Google Cloud Platform’s 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 looking to derive insights from massive datasets. BigQuery’s seamless integration with other GCP services and its ability to process petabytes of data in seconds make it a critical tool for data engineers and analysts.

This article provides a curated selection of interview questions focused on GCP BigQuery. Reviewing these questions will help you deepen your understanding of BigQuery’s capabilities and prepare you to discuss its features and applications confidently in an interview setting.

GCP BigQuery Interview Questions and Answers

1. Explain the different data types supported in BigQuery.

BigQuery supports a variety of data types to accommodate different kinds of data:

• Numeric Types: INT64 for 64-bit integers, FLOAT64 for 64-bit floating-point numbers, and NUMERIC for high-precision decimal numbers.
• String Types: STRING for variable-length character data.
• Boolean Type: BOOL for true or false values.
• Timestamp Types: TIMESTAMP for exact points in time, DATE for calendar dates, TIME for time of day, and DATETIME for a combination of date and time without a time zone.
• Geography Type: GEOGRAPHY for geospatial data.
• Array Types: ARRAY for an ordered list of zero or more elements of any non-ARRAY type.
• Struct Types: STRUCT for a collection of fields, each with a type and a name.
• Bytes Type: BYTES for binary data.

2. Explain the difference between partitioning and clustering.

Partitioning in BigQuery divides a large table into smaller, more manageable pieces called partitions, typically based on a specific column like a timestamp or date. This reduces the amount of data scanned during queries, improving performance and reducing costs. Clustering organizes data within a table based on column values, useful for columns with high cardinality. It sorts data based on clustering columns, speeding up query performance by reducing the data scanned. Clustering is often used with partitioning for further optimization.

3. How does BigQuery’s pricing model work?

BigQuery’s pricing model includes:

• Storage Costs: Charges for data stored in tables, calculated monthly. Active storage (data modified in the last 90 days) is more expensive than long-term storage (data not modified for over 90 days).
• Query Costs: Based on the number of bytes processed. Users can choose on-demand pricing or flat-rate pricing for a fixed monthly fee.
• Streaming Inserts: Charges for streaming data into tables, based on bytes inserted.
• Data Transfers: Charges for transferring data into BigQuery from other services or external sources, varying by source and destination.
• Additional Features: BI Engine and Data Transfer Service have their own pricing structures.

4. Write a SQL query using a window function to rank rows within partitions of a result set.

Window functions in SQL perform calculations across a set of table rows related to the current row. They are used for ranking, aggregating, and other calculations over partitions of data. In BigQuery, window functions are useful for tasks like ranking rows within partitions.

Example:

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

The RANK() function ranks employees within each department based on salary in descending order. The PARTITION BY clause divides the result set by department, and the ORDER BY clause specifies the order within each partition.

5. How does BigQuery integrate with other GCP services like Cloud Storage and Dataflow?

BigQuery integrates with other GCP services like Cloud Storage and Dataflow for efficient data workflows and analytics. It can directly query data stored in Cloud Storage without loading it into BigQuery, eliminating data duplication and reducing storage costs. Dataflow, a managed service for stream and batch data processing, can transform and enrich data before loading it into BigQuery, ensuring scalable and efficient data processing.

6. What are the key security features in BigQuery, such as IAM roles and permissions?

BigQuery offers security features for data protection and access control, primarily through Identity and Access Management (IAM). IAM roles and permissions manage access to BigQuery resources.

IAM Roles and Permissions: Predefined roles like Viewer, Editor, and Owner have specific permissions. Custom roles can be created for tailored access controls.

Encryption: Data is encrypted at rest and in transit using AES-256 encryption. Users can manage encryption keys with Cloud Key Management Service (KMS).

Audit Logging: Detailed audit logs record actions on datasets, tables, and views, aiding in monitoring access and detecting unauthorized activities.

VPC Service Controls: Define a security perimeter around BigQuery resources to prevent data exfiltration, isolating resources and controlling access from external networks.

Data Loss Prevention (DLP): Integrates with Cloud DLP to discover, classify, and protect sensitive information in datasets.

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

To optimize query performance in BigQuery, consider these best practices:

• Partitioning and Clustering: Use partitioned tables to divide data into segments, reducing data scanned. Clustering organizes data within partitions for efficient queries.
• Query Optimization: Write efficient SQL queries by avoiding SELECT *, using appropriate JOINs, and filtering data early. Use subqueries and common table expressions (CTEs) wisely.
• Data Pruning: Use WHERE clauses to filter data early, reducing processed data and speeding up execution.
• Materialized Views: Store precomputed query results to speed up performance, especially for complex and frequent queries.
• Denormalization: Consider denormalizing data to reduce JOIN operations, improving query performance despite increased storage costs.
• Query Caching: Utilize BigQuery’s query caching feature to save time and reduce costs for repeated queries.
• Monitoring and Analysis: Regularly monitor query performance using BigQuery’s tools and analyze execution plans to identify bottlenecks.

8. How do you use the BigQuery Data Transfer Service to import data from external sources?

The BigQuery Data Transfer Service (DTS) automates data movement from external sources into BigQuery on a scheduled basis. It supports various data sources, including Google SaaS applications and external cloud storage systems.

To use DTS:

• Enable the API: Ensure the BigQuery Data Transfer Service API is enabled in your project.
• Create a Transfer Configuration: In the Google Cloud Console, navigate to BigQuery’s “Transfers” section, click “Create Transfer,” and choose a data source.
• Configure the Data Source: Provide necessary credentials and configuration details, including authentication and data location.
• Schedule the Transfer: Set the transfer schedule for data import frequency, such as daily or weekly.
• Monitor and Manage Transfers: Monitor status and manage errors through the BigQuery console.

9. Write a SQL query using BigQuery’s geospatial functions to find the distance between two points.

BigQuery provides geospatial functions for operations on geographical data. The ST_DISTANCE function calculates the distance between two geographical points.

Example:

SELECT ST_DISTANCE(
  ST_GEOGPOINT(-73.9857, 40.7484),  -- Point 1: Longitude, Latitude
  ST_GEOGPOINT(-118.2509, 34.0522)  -- Point 2: Longitude, Latitude
) AS distance;

ST_GEOGPOINT creates geographical points from longitude and latitude values, and ST_DISTANCE calculates the distance between them.

10. How do you monitor and log activities in BigQuery for auditing and troubleshooting?

Monitoring and logging activities in BigQuery for auditing and troubleshooting can be achieved using Google Cloud’s operations suite.

Stackdriver Logging (now part of Google Cloud’s operations suite) collects and analyzes logs. BigQuery writes audit logs to Stackdriver, including Admin Activity, Data Access, and System Event logs, providing information about data access and changes.

Stackdriver Monitoring creates dashboards and alerts based on BigQuery metrics, allowing real-time performance and usage monitoring. Alerts notify of unusual activity or performance issues for proactive troubleshooting.

BigQuery’s built-in audit logs, accessible from the console, include query execution details like query text, execution time, and resource usage, aiding in auditing and performance issue resolution.

11. Explain the concept of BigQuery Reservations and how they can be used to manage workloads.

BigQuery Reservations allow users to allocate slots, units of computational capacity, to specific projects or workloads, ensuring necessary resources for efficient operation.

Key components:

• Reservations: Pools of slots assigned to projects or workloads, allowing resource segregation.
• Assignments: Link projects or workloads to reservations, ensuring allocated slot usage.
• Slot Pools: Collections of slots dynamically allocated to reservations based on demand.

Using Reservations, organizations achieve better resource management, cost predictability, and performance optimization. For example, separate reservations for ETL processes, ad-hoc queries, and reporting workloads ensure necessary resources without interference.

12. Describe the process of setting up and using BigQuery BI Engine for faster query performance.

BigQuery BI Engine is an in-memory analysis service for sub-second query response times and high concurrency, ideal for business intelligence applications.

To set up and use BI Engine:

1. Enable the API: Enable the BigQuery BI Engine API in your project through the Google Cloud Console.
2. Allocate Capacity: Purchase BI Engine reservations for in-memory storage to accelerate queries.
3. Configure BI Tools: Integrate BI tools like Google Data Studio or Looker with BigQuery to leverage BI Engine for faster performance.
4. Optimize Queries: Ensure queries are optimized for BI Engine, using appropriate filters and aggregations.
5. Monitor Performance: Use BI Engine monitoring tools in the Google Cloud Console to track query performance and adjust capacity reservations as needed.

13. How does BigQuery handle schema changes in a table, and what are the best practices for managing schema evolution?

BigQuery handles schema changes by allowing certain modifications without a full table rewrite, such as adding new columns or relaxing column modes. However, removing columns or changing data types requires creating a new table with the desired schema and migrating data.

Best practices for managing schema evolution:

• Plan Ahead: Design schemas with future changes in mind, using flexible data types.
• Use Partitioned Tables: Manage large datasets and schema changes by isolating changes to specific partitions.
• Versioning: Maintain schema versions to track changes over time, using versioned table names or metadata.
• Backfill Data: Backfill existing data when adding new columns for consistency.
• Test Changes: Test schema changes on a smaller data subset before applying to production tables.
• Documentation: Document schema changes, including rationale and potential impacts on downstream processes.

14. What are the different types of joins supported in BigQuery, and when would you use each type?

BigQuery supports several types of joins for different data relationships and query outcomes:

• INNER JOIN: Returns rows with matching values in both tables, used for retrieving records with corresponding entries in both datasets.
• LEFT JOIN (or LEFT OUTER JOIN): Returns all rows from the left table and matched rows from the right table, with NULLs for unmatched right table columns. Useful for keeping all primary dataset records and including related data if available.
• RIGHT JOIN (or RIGHT OUTER JOIN): Returns all rows from the right table and matched rows from the left table, with NULLs for unmatched left table columns. Used when focusing on right table data.
• FULL JOIN (or FULL OUTER JOIN): Returns rows with matches in one of the tables, with NULLs for unmatched columns. Used for a complete view of both datasets, including unmatched records.
• CROSS JOIN: Returns the Cartesian product of two tables, combining every row from the first table with every row from the second. Rarely used due to large result sets but useful for generating data combinations.

15. Explain the role of BigQuery’s caching mechanism and how it impacts query performance and cost.

BigQuery’s caching mechanism optimizes query performance and reduces costs. When a query is executed, results are cached for 24 hours. If the same query is run again within this period, cached results are returned, improving performance and lowering costs since cached queries incur no additional charges.

The caching mechanism stores query results in temporary storage. When a query is executed, BigQuery checks for an identical query run within the last 24 hours. If found, cached results are returned immediately, benefiting repetitive queries like those in dashboards or reports.

Conditions for query caching eligibility:

• The query must be identical to a previously executed query.
• The query must not reference changed tables or views since the last execution.
• The query must not use non-deterministic functions, such as CURRENT_TIMESTAMP.