10 AWS Athena Interview Questions and Answers

AWS Athena is a powerful, serverless query service that allows users to analyze data directly in Amazon S3 using standard SQL. It eliminates the need for complex ETL processes and infrastructure management, making it an efficient tool for data analysis and business intelligence. With its seamless integration with other AWS services, Athena is a valuable asset for organizations looking to leverage their data more effectively.

This article provides a curated selection of interview questions designed to test your knowledge and proficiency with AWS Athena. By working through these questions and understanding the underlying concepts, you will be better prepared to demonstrate your expertise and problem-solving abilities in a technical interview setting.

AWS Athena Interview Questions and Answers

1. Describe the process of setting up an Athena query to analyze data stored in S3.

To set up an Athena query to analyze data stored in S3, follow these steps:

1. Prepare Your Data in S3: Ensure your data is in a format Athena supports, such as CSV, JSON, Parquet, or ORC, and organize it logically.

2. Create a Database in Athena: Use the Athena console to create or select a database to organize tables.

3. Define a Table Schema: Specify the schema of the table that maps to your data in S3, including column names, data types, and the data location. For example:

CREATE EXTERNAL TABLE IF NOT EXISTS my_table (
    id INT,
    name STRING,
    age INT
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
WITH SERDEPROPERTIES (
    'serialization.format' = ','
) LOCATION 's3://my-bucket/my-folder/';

4. Run Queries: Execute SQL queries against the table using the Athena console, AWS SDK, or AWS CLI.

5. Optimize Performance: Consider partitioning your data based on common query filters and use columnar data formats like Parquet or ORC.

2. Write a SQL query to find the top 5 most frequent values in a column named ‘user_id’ from a table named ‘user_activity’.

To find the top 5 most frequent values in a column named ‘user_id’ from a table named ‘user_activity’ in AWS Athena, use the following SQL query:

SELECT user_id, COUNT(*) as frequency
FROM user_activity
GROUP BY user_id
ORDER BY frequency DESC
LIMIT 5;

This query groups records by ‘user_id’, counts occurrences, orders by frequency, and limits results to the top 5.

3. How do you partition data in Athena, and why is it important?

Partitioning data in AWS Athena involves dividing your data into smaller pieces based on column values to improve query performance and reduce costs. By partitioning, Athena can skip scanning entire partitions that don’t match the query criteria, speeding up execution.

For example, partitioning a large dataset of logs by date allows Athena to scan only relevant partitions for specific date queries.

Here’s how to create a partitioned table in Athena:

CREATE EXTERNAL TABLE logs (
    id STRING,
    message STRING
)
PARTITIONED BY (date STRING)
STORED AS PARQUET
LOCATION 's3://your-bucket/logs/';

After creating the table, load the partitions:

MSCK REPAIR TABLE logs;

4. Write a SQL query to create a new table in Athena that partitions data by year and month.

Partitioning in AWS Athena divides data into parts based on column values, enhancing query efficiency by reducing scanned data. When partitioned by year and month, Athena skips irrelevant data, optimizing queries.

Here’s an example SQL query to create a table partitioned by year and month:

CREATE EXTERNAL TABLE IF NOT EXISTS my_table (
    id INT,
    name STRING,
    value DOUBLE
)
PARTITIONED BY (year INT, month INT)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
WITH SERDEPROPERTIES (
    'serialization.format' = '1'
) LOCATION 's3://my-bucket/my-data/'
TBLPROPERTIES ('has_encrypted_data'='false');

5. Write a SQL query to convert a JSON file stored in S3 into a table in Athena.

To convert a JSON file stored in S3 into a table in AWS Athena, create an external table specifying the JSON file location in S3, define the schema, and use the appropriate SerDe.

Example SQL query:

CREATE EXTERNAL TABLE my_table (
    id INT,
    name STRING,
    age INT,
    address STRUCT<
        street: STRING,
        city: STRING,
        state: STRING,
        zip: STRING
    >
)
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
WITH SERDEPROPERTIES (
    'ignore.malformed.json' = 'true'
)
LOCATION 's3://your-bucket/path-to-json-files/';

In this query:

• CREATE EXTERNAL TABLE creates a new table in Athena.
• The table schema includes columns like id, name, age, and address.
• ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe' specifies the SerDe for JSON.
• LOCATION 's3://your-bucket/path-to-json-files/' indicates the S3 location of the JSON files.

6. Write a SQL query to join two tables in Athena: ‘orders’ and ‘customers’, where ‘orders.customer_id’ matches ‘customers.id’.

To join two tables in AWS Athena, use the SQL JOIN clause. For joining ‘orders’ with ‘customers’ where ‘orders.customer_id’ matches ‘customers.id’, use an INNER JOIN:

SELECT 
    orders.order_id,
    orders.order_date,
    customers.customer_name,
    customers.customer_email
FROM 
    orders
INNER JOIN 
    customers
ON 
    orders.customer_id = customers.id;

7. How do you optimize query performance in Athena? Provide at least three strategies.

To optimize query performance in AWS Athena, consider these strategies:

• Partitioning Data: Partitioning reduces scanned data by allowing Athena to skip irrelevant partitions, enhancing query speed.
• Compressing Data: Compressing data reduces the amount read from S3, improving query speed and reducing storage costs. Formats include GZIP, Snappy, and Zlib.
• Using Appropriate File Formats: Columnar formats like Parquet and ORC optimize analytical queries, reducing scanned data by reading only necessary columns.

8. Write a SQL query to calculate the average order value from a table named ‘orders’, grouped by ‘customer_id’.

To calculate the average order value from a table named ‘orders’, grouped by ‘customer_id’, use the SQL AVG function with the GROUP BY clause:

SELECT customer_id, AVG(order_value) AS average_order_value
FROM orders
GROUP BY customer_id;

9. Write a SQL query to filter out records from a table named ‘transactions’ where the ‘amount’ is greater than 1000 and the ‘status’ is ‘completed’.

To filter out records from a table named ‘transactions’ where the ‘amount’ is greater than 1000 and the ‘status’ is ‘completed’, use the following SQL query:

SELECT * 
FROM transactions 
WHERE amount > 1000 
AND status = 'completed';

10. Write a SQL query to find duplicate records in a table named ‘transactions’ based on the ‘transaction_id’ column.

To find duplicate records in a table named ‘transactions’ based on the ‘transaction_id’ column, use a SQL query that groups records by ‘transaction_id’ and filters groups with more than one record:

SELECT transaction_id, COUNT(*)
FROM transactions
GROUP BY transaction_id
HAVING COUNT(*) > 1;

This query:

• Retrieves ‘transaction_id’ and record count for each ‘transaction_id’.
• Specifies the ‘transactions’ table.
• Groups records by ‘transaction_id’.
• Filters groups with a count greater than 1, indicating duplicates.