Unlock the Power of BigQuery: Top Interview Questions and Answers Revealed

In today’s data-driven world, companies are increasingly relying on cloud-based data warehousing solutions to manage and analyze their ever-growing datasets. Google BigQuery, a serverless, highly scalable, and cost-effective solution, has emerged as a leading choice for businesses seeking to extract valuable insights from their data. As the demand for skilled BigQuery professionals rises, acing the interview process has become crucial for those aspiring to land their dream job in this field.

In this comprehensive guide, we’ll explore the top BigQuery interview questions and provide expert-approved answers to help you confidently navigate the interview process. Whether you’re a seasoned data engineer or just starting your journey, this article will equip you with the knowledge and strategies you need to impress potential employers.

Understanding BigQuery: An Overview

Before delving into the interview questions, let’s briefly introduce BigQuery and its key features:

  • Serverless Architecture: BigQuery is a fully managed, serverless data warehouse, eliminating the need for manual infrastructure management and scaling.
  • Scalability: With the ability to process petabytes of data in seconds, BigQuery can handle even the most demanding data analytics workloads.
  • Cost-Effective: BigQuery’s pay-as-you-go pricing model ensures you only pay for the resources you consume, making it a cost-effective solution for businesses of all sizes.
  • Built-in Machine Learning: BigQuery ML allows you to build and operationalize machine learning models directly within the data warehouse, streamlining the data science workflow.
  • Geospatial Analysis: BigQuery GIS enables location-based analytics, empowering businesses to uncover valuable insights from their geospatial data.

Now that you have a basic understanding of BigQuery, let’s dive into the most commonly asked interview questions and their corresponding answers.

Technical BigQuery Interview Questions

  1. Explain the architecture of Google BigQuery.

BigQuery’s architecture consists of four main components:

  • Dremel: Facilitates the creation of execution trees from SQL queries.
  • Colossus: Supports columnar storage and provides compression mechanisms for efficient data storage.
  • Jupiter: Improves connectivity between CPUs and storage.
  • Borg: Manages fault tolerance for Dremel job computation power.
  1. How does data loading work in Google BigQuery?

BigQuery supports multiple data loading methods, including:

  • Web UI: Upload data files through the BigQuery web interface.
  • Command-line tool: Load data from a local file or a Google Cloud Storage bucket.
  • API: Import data from various sources using the BigQuery API.
  1. Can you create views in Google BigQuery? If so, how?

Yes, you can create views in Google BigQuery. The process involves:

  1. Creating a dataset.

  2. Within the dataset, you can create a view using the BigQuery web UI, CLI, or API.

  3. What is the difference between BigQuery and SQL?

While both BigQuery and SQL are query languages, there are some key differences:

  • Architecture: BigQuery is a cloud-based, auto-scaling architecture, while SQL Server uses a client-server architecture with manual scaling.
  • Performance: BigQuery can process petabyte-scale data in seconds, thanks to its distributed architecture and columnar storage.
  • Pricing Model: BigQuery follows a pay-as-you-go model, while SQL Server requires licensing and infrastructure costs.
  1. What strategies do you use to optimize query performance in BigQuery?

Some strategies to optimize query performance in BigQuery include:

  • Utilizing native User-Defined Functions (UDFs) instead of JavaScript UDFs.
  • Leveraging window functions to retrieve the latest record.
  • Optimizing table joining patterns.
  • Partitioning and clustering tables for efficient data retrieval.

BigQuery SQL Interview Questions

  1. How can you include an additional field in a BigQuery SQL query to identify duplicate IDs with a suffix like ‘serial number’?

To include an additional field that identifies duplicate IDs with a suffix, you can use the following BigQuery Standard SQL query:


SELECT *, id || '-' || ROW_NUMBER() OVER (PARTITION BY id) extra_columnFROM SampleTable;
  1. What BigQuery query would you use to retrieve each user between two dates?

To retrieve each user between two dates, you can use the following BigQuery SQL query:


SELECT  TIMESTAMP_TRUNC(timestamp, DAY) AS Day,  user_id,  COUNT(1) AS NumberFROM `table`WHERE timestamp >= '2023-12-28 00:00:00 UTC'  AND timestamp <= '2023-12-27 23:59:59 UTC'GROUP BY 1, 2ORDER BY Day;
  1. How do you handle a patient who is not compliant with their post-treatment instructions?

When dealing with a non-compliant patient, it’s essential to emphasize the importance of following post-treatment instructions and address any concerns they may have. Provide written or verbal reminders, follow-up calls, and ensure they understand the potential risks of non-compliance. Maintain a professional and empathetic approach throughout the process.

Advanced BigQuery Interview Questions

  1. Why is it necessary to use Google Cloud Storage as a secondary storage layer when loading data into BigQuery?

Google Cloud Storage is used as an intermediary storage layer when loading data into BigQuery due to its cost-effective pricing for cloud data storage. Compared to other cloud storage providers, using Google Cloud Storage can significantly reduce the high costs associated with cloud storage.

  1. What are the various methods to access BigQuery once configured?

Once configured, you can access BigQuery through:

  • Google Cloud Console: A web-based interface for managing and analyzing data in BigQuery.
  • BigQuery Command-line Tool: Allows you to communicate with BigQuery via the command line and issue queries.
  • Third-party Tools: BigQuery can be integrated with various third-party tools that offer additional features and capabilities.
  1. What is the best way to ensure GDPR compliance when storing data in BigQuery?

The most effective way to ensure GDPR compliance when storing data in BigQuery is to encrypt the data before storing it. BigQuery supports various encryption methods, allowing you to choose the one that best suits your needs. Additionally, consider implementing a data access control system to limit access to authorized individuals only.

  1. How do you fix common SQL errors in BigQuery?

To fix common SQL errors in BigQuery, you can use the Query Validator tool. It checks the syntax of your query and displays a green checkmark when there are no errors. If errors are present, the tool will log them in the Job details. After confirming the correct syntax, click the “Run” button to execute the query and view the results.

Remember, preparation is key to acing any interview. Familiarize yourself with BigQuery’s documentation, practice coding exercises, and stay up-to-date with the latest trends and developments in the field. Combining theoretical knowledge with practical experience will give you a competitive edge and increase your chances of landing your dream job as a BigQuery professional.

How to check your BigQuery Performance? Google Cloud Interviews Questions Discussions


What is the basics of Google BigQuery?

BigQuery supports both descriptive and predictive analytics. To query your data directly to answer some statistical questions, you can use the Google Cloud console. To visually explore the data, such as for trends and anomalies, you can use tools like Tableau or Looker that integrate with BigQuery.

What SQL language is used in BigQuery?

BigQuery supports the GoogleSQL dialect, but a legacy SQL dialect is also available. If you are new to BigQuery, you should use GoogleSQL as it supports the broadest range of functionality. For example, features such as DDL and DML statements are only supported using GoogleSQL.

What are the capabilities of BigQuery?

BigQuery is Google Cloud’s fully managed and completely serverless enterprise data warehouse. BigQuery supports all data types, works across clouds, and has built-in machine learning and business intelligence, all within a unified platform.

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