20 AWS Athena Interview Questions and Answers

AWS Athena is a serverless interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. In this article, we will review some of the most common interview questions related to AWS Athena. By preparing for these questions, you can increase your chances of impressing the interviewer and landing the job.

AWS Athena Interview Questions and Answers

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

1. What is AWS Athena?

AWS Athena is a serverless interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

2. Can you explain the architecture of AWS Athena?

AWS Athena is a serverless query service that allows you to analyze data stored in Amazon S3 using standard SQL. It is designed to be highly scalable and cost-effective, and it integrates with other AWS services such as Amazon S3, Amazon Glue, and Amazon QuickSight.

3. How much does it cost to use AWS Athena?

AWS Athena is a pay-per-use service, so you only pay for the queries that you run. Prices vary depending on the amount of data scanned per query, but start at $5 per TB.

4. Is there a free tier available for AWS Athena?

Yes, there is a free tier available for AWS Athena. This free tier gives you the ability to run up to 5 GB of data per month through Athena.

5. How are datasets in AWS Athena stored?

Datasets in AWS Athena are stored in Amazon S3 in a proprietary format that is optimized for efficient querying.

6. What do you understand about columnar data formats like ORC and Parquet?

Columnar data formats are a type of data storage that organizes data into columns instead of rows. This can be helpful when working with large data sets because it can improve query performance. ORC and Parquet are two of the most popular columnar data formats.

7. Can you give me some examples of real-world applications where I can use AWS Athena?

AWS Athena can be used for a variety of tasks, including data analysis, data warehousing, and log analysis. It is a particularly useful tool for analysts who need to quickly query and analyze data stored in Amazon S3.

8. Do you know what partitioning is and how it works in context with AWS Athena?

Partitioning in AWS Athena is a way of dividing data up into smaller pieces so that queries can run faster and more efficiently. Partitioning can be done on any column in a table, and it is especially useful for columns that have a lot of data or that are frequently queried. Partitioning works by creating separate partitions for each value in the partitioning column, and then storing the data in those partitions. When a query is run, only the partitions that are relevant to the query are scanned, which can greatly reduce the amount of time it takes to run the query.

9. Can you tell me more about table metadata as used by AWS Athena?

When you create a table in Athena, you have the option of including table metadata. This metadata can be used to provide information about the table, such as the column names and data types, that can be used by Athena when querying the table. This metadata can be stored in an external file or in the table itself, and can be updated as needed.

10. What kinds of queries can be run using AWS Athena?

AWS Athena supports a variety of standard SQL queries, including data manipulation language (DML) statements and data definition language (DDL) statements. Additionally, Athena supports complex queries that can include joins, aggregations, and window functions.

11. What’s your understanding of catalogs in the context of AWS Athena?

A catalog is a collection of databases and tables that are used to store data. In AWS Athena, a catalog is used to keep track of the location of the data that you want to query. In order to query data in Athena, you must first create a catalog.

12. How does AWS Athena compare with Amazon Redshift?

Both Amazon Redshift and AWS Athena are data warehousing solutions that can be used to analyze data in the cloud. However, there are some key differences between the two. Amazon Redshift is a fully managed data warehouse service, while AWS Athena is an interactive query service that is used to query data stored in Amazon S3. Amazon Redshift is designed for larger data sets and can be used for OLAP (online analytical processing) workloads, while AWS Athena is designed for smaller data sets and is better suited for OLAP workloads.

13. How does AWS Athena differ from Hadoop Hive?

AWS Athena is a serverless query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Athena is easy to use. Simply point to your data in Amazon S3, define the schema, and start querying using standard SQL. Athena is fast. Athena uses Presto with full standard SQL support and works with a variety of data formats, including CSV, JSON, ORC, Avro, and Parquet.

14. Why should I use AWS Athena instead of AWS Glue?

AWS Athena is a serverless interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. With Athena, there is no need to set up or manage any infrastructure, so you can start analyzing your data immediately. Athena is also highly scalable, so you can run queries on large datasets without having to worry about provisioning or managing any resources. Finally, Athena is very cost-effective, as you only pay for the queries that you run.

15. What is PrestoDB? How does it work in conjunction with AWS Athena?

PrestoDB is an open source distributed SQL query engine that is used in conjunction with AWS Athena. Athena uses PrestoDB to query data stored in Amazon S3. PrestoDB is designed to be fast and efficient, and it supports a variety of data formats including CSV, JSON, and Avro.

16. Which programming languages can I use when running SQL queries in AWS Athena?

You can use any programming language that is compatible with JDBC or ODBC drivers. This includes languages like Java, Python, Node.js, and more.

17. Is it possible to create user-defined functions in AWS Athena? If yes, then how?

Yes, it is possible to create user-defined functions in AWS Athena. You can do this by using the CREATE FUNCTION command. This command allows you to specify the name of the function, the input and output types, the function body, and any other required parameters.

18. What happens if you need to execute an ETL process on top of your existing data sets before loading them into AWS Athena?

In this case, you would need to first export your data sets from their current location into Amazon S3, and then use AWS Glue to ETL them into the format required by Athena.

19. What are the differences between AWS Athena and Google BigQuery?

Both AWS Athena and Google BigQuery are cloud-based data warehouses that allow users to query large data sets. However, there are some key differences between the two. First, Athena uses a serverless architecture, meaning that users only pay for the queries they run. BigQuery, on the other hand, charges a monthly fee for storage and usage. Second, Athena uses the Presto query engine, while BigQuery uses BigQuery SQL. This can impact performance, as Presto is generally faster. Finally, Athena integrates with other AWS services, while BigQuery integrates with other Google Cloud Platform services.

20. Can you give me some examples of query aggregates that can be executed in AWS Athena?

Some examples of query aggregates that can be executed in AWS Athena include: COUNT, SUM, MIN, MAX, and AVG.