Mastering the Art of BigQuery Interviews: Ace Your Data Engineering Journey

In the ever-evolving world of data analytics, Google BigQuery stands as a powerhouse tool, enabling businesses to harness the full potential of their data. As more organizations embrace BigQuery for their data warehousing and analysis needs, the demand for skilled data engineers with a deep understanding of this platform continues to soar. If you’re aspiring to secure a coveted role as a BigQuery data engineer, mastering the art of interviewing is crucial. In this comprehensive guide, we’ll dive into the most commonly asked BigQuery interview questions, equipping you with the knowledge and confidence to ace your next interview.

Understanding Google BigQuery

Before we delve into the interview questions, let’s first explore the essence of Google BigQuery. BigQuery is a serverless, highly scalable, and cost-effective data warehouse solution offered by Google Cloud Platform (GCP). It enables businesses to execute SQL queries on massive datasets, empowering them to gain valuable insights, drive business transactions, and perform complex data analytics tasks.

One of the key strengths of BigQuery lies in its ability to support ANSI SQL, making it accessible to a wide range of users familiar with SQL. This powerful tool has garnered attention from industry giants like Twitter, who leverage BigQuery to predict package volumes for their various offerings accurately.

Diving into BigQuery Technical Interview Questions

Technical interviews are designed to assess your proficiency with BigQuery and your ability to navigate its architecture and components. Here are some common BigQuery technical interview questions you should prepare for:

1. What is Google BigQuery?

   • Google BigQuery is a cloud-based, fully managed data warehouse service that allows you to run SQL queries on massive datasets. It’s designed to process petabyte-scale data quickly and efficiently.

2. Describe the architecture of Google BigQuery.

   • The BigQuery 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: Facilitates the connectivity between CPUs and storage devices.
     • Borg: Regulates fault tolerance for the computation power of Dremel jobs.

3. What are the benefits of using GCP BigQuery?

   • Some key benefits of GCP BigQuery include:
     • Its Storage API supports Spark and Beam workloads, enabling better integration.
     • It reduces the need for code rewriting by supporting the standard SQL dialect.
     • Data can be replicated, and a seven-day change history can be maintained for restoration and comparison purposes.

4. What is the BigQuery Query Cache?

   • The BigQuery Query Cache is a temporary cached results table that stores query results during the first execution, enabling faster data retrieval.

5. What are some of the key components of Google BigQuery?

   • Google BigQuery comprises the following 12 components:
     • Opinionated Storage Engine
     • Serverless Service Model
     • IAM, Authentication & Audit Logs
     • Batch Ingest
     • UX, CLI, SDK, ODBC/JDBC, API
     • Streaming Ingest
     • The Free Pricing Tier
     • Federated Query Engine
     • Pay-Per-Query & Flat Rate Pricing
     • Enterprise-grade Data Sharing
     • Public, Commercial, and Marketing Datasets
     • Dremel Execution Engine & Standard SQL

6. How should data be loaded into BigQuery?

   • The BigQuery Data Transfer Service is the recommended tool for loading data into BigQuery efficiently. This tool allows you to quickly and seamlessly import data into BigQuery from various sources, including other Google Cloud Platform services.

7. What is BigQuery Storage?

   • BigQuery Storage represents data in rows, columns, and tables using a columnar storage format optimized for analytical queries. It supports comprehensive database transaction semantics (ACID) and can be replicated across multiple sites for high availability.

Mastering BigQuery SQL Interview Questions

SQL proficiency is a critical aspect of BigQuery interviews, as it is the primary language used for querying and manipulating data within the platform. Here are some SQL-focused BigQuery interview questions to prepare for:

1. How can you include an additional field in a SQL query for GCP BigQuery to add a suffix like “serial number” to identify duplicate IDs?

   • You can run the following BigQuery Standard SQL query:
     sql

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     SELECT *, id || '-' || ROW_NUMBER() OVER(PARTITION BY id) extra_columnFROM SampleTable

2. How can you resolve common SQL errors in BigQuery?

   • Use the Query Validator to check the syntax of your query. If you attempt to run a query with errors, it will fail and log the error in the Job details. The query validator will display a green checkmark when the query is error-free. Click “Run” to execute the query and view the results after the green checkmark appears.

3. What BigQuery query would you use to retrieve each user between two dates?

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   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

4. Can you highlight the difference between legacy SQL and standard SQL in BigQuery?

   • Standard SQL is the most recent and recommended approach for querying data in BigQuery. It is based on the SQL:2011 standard and offers several improvements over legacy SQL, such as better performance, greater support for SQL standard features, and improved compatibility with other SQL-based systems.
   • Legacy SQL is an older approach to querying data in BigQuery, based on the SQL:2003 standard. While still supported for backward compatibility, it is generally recommended to use standard SQL whenever possible.

Preparing for BigQuery Interview Questions for Experienced Professionals

As you progress in your data engineering career, you’ll likely face more advanced BigQuery interview questions that test your practical experience and problem-solving abilities. Here are some examples:

1. Why is Google Cloud Storage required as a secondary storage layer while loading data into BigQuery?

   • Google Cloud Storage is used as an intermediary storage layer for importing data into BigQuery due to its cost-effective cloud data storage pricing. Using Google Cloud Storage can significantly reduce the high storage costs incurred by other cloud storage providers.

2. What are the various methods available for accessing BigQuery once configured?

   • Once configured, BigQuery can be accessed in several ways:
     • The Google Cloud Console, a web-based administration and data analysis interface.
     • The BigQuery command-line tool, which allows you to communicate with BigQuery via the command line and issue queries.
     • Integration with various third-party tools that offer additional features and capabilities.

3. What is the best approach to ensure GDPR compliance when storing data in BigQuery?

   • Encrypting the data before storing it in BigQuery is the best way to ensure compliance with GDPR regulations. BigQuery offers various encryption strategies, and you can choose the one that best suits your organization’s needs. Additionally, consider implementing a data access control system to ensure that only authorized individuals can access sensitive information.

4. When should you use BigQuery instead of more established databases like MongoDB or MySQL?

   • BigQuery is a powerful platform designed to process and analyze vast amounts of data efficiently. It is particularly well-suited for scenarios where you need to run complex queries on massive datasets. If you are working with a large volume of data that needs to be handled quickly and effectively, BigQuery can be an excellent solution.

Scenario-based BigQuery Interview Questions

Scenario-based questions are often used in BigQuery interviews to assess your practical problem-solving skills and hands-on experience with the platform. Here are a few examples:

1. You have a table (tableX) with more than 20 columns and over 1 million rows of data, and there are 80,000 duplicate records in one of the columns (troubleColumn). How will you delete the duplicate records from your faulty column while keeping the original table name?

   • To remove duplicates while preserving the original table name, you can run a query that rewrites the table:
     sql

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     SELECT *FROM (  SELECT    *,    ROW_NUMBER()      OVER (PARTITION BY Fixed_Accident_Index)      row_number  FROM Accidents.CleanedFilledCombined)WHERE row_number = 1

2. While trying to delete records from a table that was updated using the GCP BigQuery Node.js table insert function and created through the GCP Console, you encounter the following error: “UPDATE or DELETE DML statements are not supported over table stackdriver-360-150317:my_dataset.users with streaming buffer”. Can you offer a solution to resolve this issue?

   • When streaming to a partitioned table, the data in the streaming buffer has a NULL value for the _PARTITIONTIME pseudo column. You can perform a simple WHERE query to determine if the table contains a streaming buffer or check the tables.get response for a section named streamingBuffer.
   • After the initial streaming insertion into a table, the streamed data is immediately available for real-time analysis but may take up to 90 minutes before it is accessible for copy/export and other operations. You may need to wait up to 90 minutes for the buffer to be fully committed to the cluster. You can run queries to check if the streaming buffer is empty.

3. BigQuery requests the Google Cloud Storage URI (gs://) whenever you try to load data from Google Cloud Storage into it. Can you find the URL using the Google Developers Console for browsers?

   • The URL must be in the format gs://....

Preparing for BigQuery interviews requires a combination of theoretical knowledge and practical experience. In addition to studying these interview questions, it’s highly recommended to explore real-world BigQuery projects and hands-on exercises to strengthen your skills and showcase your expertise during the interview process.

Remember, the key to acing your BigQuery interviews lies in continuous learning, practice, and a genuine passion for data analytics and cloud technologies. With dedication and perseverance, you can position yourself as a strong candidate for coveted data engineering roles in the world of BigQuery and Google Cloud Platform.