20 BigQuery Interview Questions and Answers

BigQuery is a powerful tool for data analysis and warehousing. If you’re applying for a position that involves working with BigQuery, you can expect to be asked questions about your experience and knowledge during the interview process. In this article, we’ll review some of the most common BigQuery interview questions and provide tips on how to answer them.

BigQuery Interview Questions and Answers

Here are 20 commonly asked BigQuery interview questions and answers to prepare you for your interview:

1. What is BigQuery?

BigQuery is a cloud-based data warehouse service that allows you to store, query, and analyze large data sets. It is a fully managed service that is designed to be scalable and easy to use.

2. How does BigQuery work under the hood?

BigQuery is a powerful tool that allows you to quickly and easily query large datasets. But how does it work?

BigQuery uses a columnar storage format called Capacitor. This format allows for quick and efficient scanning of large datasets. When you submit a query, BigQuery will first scan the relevant columns in the dataset to find the data that you are looking for. This makes BigQuery very fast and scalable.

3. Can you explain what a columnar database is in context with BigQuery?

A columnar database is a database that stores data in columns instead of rows. BigQuery is a columnar database, which means that it stores data in columns instead of rows. This makes it well-suited for storing and querying large amounts of data.

4. What are the advantages of using BigQuery?

BigQuery is a powerful tool that allows you to quickly query large datasets. It is also easy to use, scalable, and has a low learning curve.

5. Is it possible to perform real-time queries on data stored in BigQuery? If yes, then how?

Yes, it is possible to perform real-time queries on data stored in BigQuery. This can be done by using the BigQuery streaming API, which allows you to stream data into BigQuery in real time.

6. Can you explain the difference between standard SQL and legacy SQL?

Standard SQL is the newer, recommended way of querying data in BigQuery. It is based on the SQL:2011 standard and has a number of advantages over legacy SQL, including improved performance, better support for standard SQL features, and easier compatibility with other SQL-based systems. Legacy SQL is the older way of querying data in BigQuery and is based on the SQL:2003 standard. It is still supported for backwards compatibility, but you should generally use standard SQL when possible.

7. What’s the best way to load data into BigQuery?

The best way to load data into BigQuery is by using a tool called BigQuery Data Transfer Service. This tool allows you to quickly and easily load data into BigQuery from a variety of sources, including other Google Cloud Platform services.

8. What do you understand about sharding when working with BigQuery?

Sharding is a process of splitting up data into smaller pieces so that it can be more easily managed and processed. When working with BigQuery, sharding can be used to improve performance by distributing the data across multiple machines.

9. How can you estimate query costs before executing them?

You can use the BigQuery pricing calculator to estimate query costs before executing them. The pricing calculator takes into account the size of the data being queried, the complexity of the query, and the number of bytes processed.

10. Why is Google Cloud Storage used as an intermediate storage layer for loading data into BigQuery?

Google Cloud Storage is used as an intermediate storage layer for loading data into BigQuery because it is a cost-effective way to store data in the cloud. By using Google Cloud Storage, you can avoid having to pay for expensive storage fees associated with other cloud storage providers. Additionally, Google Cloud Storage is highly scalable, so it can easily accommodate the storage needs of large data sets.

11. What is the significance of partitioning tables in BigQuery?

Partitioning tables in BigQuery can help improve query performance by allowing the query engine to more easily narrow down the data that it needs to scan. For example, if you have a table that contains data for multiple years, you could partition the table by year. Then, when you run a query that only needs data from a specific year, the query engine can skip over the other partitions, which can save time and resources.

12. What types of reports can be generated from data stored in BigQuery?

Reports that can be generated from data stored in BigQuery include:
-Sales reports
-Inventory reports
-Customer reports
-Product reports
-Marketing reports
-Financial reports

13. In which situations should we use BigQuery instead of traditional databases like MongoDB or MySQL?

BigQuery is a powerful tool that can handle large scale data analysis and processing. It is especially well suited for situations where you need to perform complex queries on large data sets. If you are working with a large amount of data that needs to be processed quickly and efficiently, then BigQuery is a good option to consider.

14. Is it possible to export data from BigQuery? If yes, then how can that be achieved?

Yes, it is possible to export data from BigQuery. There are a few different ways to do this, but the most common method is to use the BigQuery command-line tool to export your data to a Google Cloud Storage bucket.

15. How much data can BigQuery handle?

BigQuery is designed to handle very large data sets. It can process up to 100 terabytes of data per day and can store up to 10 petabytes of data.

16. What are the different ways to access BigQuery once it has been set up?

There are a few different ways to access BigQuery once it has been set up. The most common way is through the Google Cloud Console, which provides a web-based interface for managing and interacting with BigQuery. Alternatively, you can use the BigQuery command-line tool, which allows you to issue commands and interact with BigQuery from the command line. Finally, there are a number of third-party tools that provide additional functionality and integrations with BigQuery.

17. What is the process of setting up BigQuery?

The process of setting up BigQuery is fairly simple. You first need to create a project in the Google Cloud Console, and then you can enable the BigQuery API. Once the API is enabled, you can create a dataset and start running queries.

18. What is the best way to ensure compliance with GDPR regulations when storing data in BigQuery?

The best way to ensure compliance with GDPR regulations when storing data in BigQuery is to encrypt the data before it is stored. BigQuery supports a number of encryption methods, so you can choose the one that best fits your needs. You should also consider using a data access control system to restrict access to the data to only those who need it.

19. What is the maximum size of a table that can be created in BigQuery?

There is no maximum size for a table that can be created in BigQuery.

20. What are some common errors you may run into when creating new datasets in BigQuery?

Some common errors you may run into when creating new datasets in BigQuery include:

– forgetting to include a required field
– including a field that is not compatible with the BigQuery data type
– trying to insert a value that is too large for the field