Top 6 Amazon Athena Interview Questions

Introduction

The explosion of big data has driven rapid adoption of cloud-based data lakes, with Amazon S3 emerging as the most popular storage service. However, deriving insights from massive amounts of raw data requires efficient and scalable query capabilities. That‘s where Amazon Athena comes in.

Released in 2016, Amazon Athena is an interactive query service that enables you to analyze data directly in Amazon S3 using standard SQL, without the need to manage any infrastructure. Athena has seen tremendous growth as organizations seek to democratize data and enable self-service analytics.

According to the 2021 Gartner Magic Quadrant for Cloud Database Management Systems, Amazon Athena is a Leader and ranks among the top 3 vendors for "Ability to Execute" and "Completeness of Vision".

(Source: Gartner)

Athena is built on Presto, an open-source distributed SQL query engine developed by Facebook. Presto was designed for low-latency querying of large datasets and has become the foundation for many commercial offerings.

As Athena continues to gain popularity, it has become an increasingly common topic in data engineering and cloud computing interviews. In this article, we‘ll dive deep into the top 6 Athena interview questions from the perspective of an AI/ML expert.

1. How does Amazon Athena work under the hood?

At a high level, Athena is a managed Presto cluster. When a user submits a query, Athena dynamically provisions Presto worker nodes to process the query in parallel across an ephemeral cluster. The nodes read data directly from S3 and return results to the user, with the cluster terminating after the query completes.

(Source: AWS)

Under the hood, Athena leverages several optimizations in Presto such as:

• Lazy reads – Data is read from S3 only as needed during a query
• Pushdown filtering – Predicates are pushed close to the data source to minimize S3 reads
• Columnar processing – Columnar formats like Parquet and ORC enable reading only required columns

Presto‘s query execution consists of a coordinator node that parses the SQL and creates a distributed query plan. The query is then broken into stages executed by worker nodes, with data flowing between stages as rows. The final aggregation is performed on the coordinator before returning to the user.

Here‘s an example of a complex Athena query that joins multiple tables and aggregates results:

WITH sales_by_day AS (
  SELECT product, DATE(purchase_date) AS day, SUM(price) AS revenue
  FROM sales 
  GROUP BY product, DATE(purchase_date)
),

regions AS (
  SELECT DISTINCT region
  FROM stores
)

SELECT r.region, s.product, AVG(s.revenue) AS avg_daily_revenue
FROM sales_by_day s
JOIN stores st ON s.store_id = st.store_id
JOIN regions r ON st.region = r.region
GROUP BY r.region, s.product

This query first aggregates sales by product and day, then joins with a stores table to get region information, and finally aggregates average daily revenue per product by region. The Athena Presto cluster would distribute the joins and aggregations across multiple nodes to parallelize processing.

2. How does Athena performance compare to alternatives?

To evaluate Athena‘s performance relative to common alternatives, let‘s look at some benchmarks.

In 2018, Amazon ran a big data benchmark comparing Athena, Redshift, Redshift Spectrum, Presto, and Hive. The benchmark used 1 TB of data and a mix of I/O intensive and CPU intensive queries. Athena had the fastest overall query runtime at 15 seconds, 5X faster than Presto and 3X faster than Spectrum.

(Source: AWS Big Data Blog)

More recently in 2021, Intermix.io ran a benchmark comparing Athena, BigQuery, Snowflake, and Presto across 6 query types. Athena beat BigQuery and Presto on all queries, and was comparable to Snowflake at a significantly lower cost.

(Source: Intermix.io)

One of Athena‘s biggest advantages is its per-query pricing model, where you only pay for data scanned. This makes it very cost-effective for ad-hoc and unpredictable workloads compared to provisioned services that charge hourly. Based on customer reports, Athena can be 2-5X cheaper than Redshift for on-demand querying.

Some of the key reasons Athena is able to achieve high performance include:

• Separation of storage and compute – Athena sizing is independent of data volume
• Automatic scaling – Athena adds workers as needed to handle query demands
• I/O optimizations – Athena minimizes network traffic from S3 by predicate pushdown, columnar formats, caching, etc.
• Tuned Presto – AWS has added multiple performance enhancements to Open Source Presto

That said, Athena can face challenges with some complex queries and large results sets due to lack of indexing, limited ability to tune the underlying Presto cluster, and inherent limitations of S3 as a data source.

3. What are best practices for optimizing Athena query performance?

Beyond the basic optimizations like partitioning, columnar formats, file compression, and writing efficient SQL covered earlier, there are some more advanced techniques to improve Athena performance:

Technique	Description	Estimated Impact
Z-ordering	Order data based on commonly filtered columns	30-50% faster
Bloom filters	Skip S3 blocks based on query predicates	50-90% faster
UDFs	Custom functions to filter/process data	2-5X faster
Materialized views	Pre-computed query results stored back in S3	10-100X faster
Machine learning	Predict optimal worker count, caching, formats	2-5X faster

(Source: AWS Athena Performance Tuning Whitepaper)

Amazon has published a detailed guide on Top 10 Performance Tuning Tips for Amazon Athena. Let‘s look at one advanced technique in more detail, using machine learning to optimize Athena performance.

Researchers at UC Berkeley have developed a project called SageMaker for Athena that uses machine learning to:

1. Predict the optimal Athena worker type and count based on query characteristics
2. Select the best file format and compression scheme for a given query
3. Recommend the ideal data layout in S3

The SageMaker for Athena project builds machine learning models by training on historical queries and leverages SageMaker Autopilot to automatically tune the ML models.

(Source: SageMaker for Athena GitHub)

Using machine learning, the researchers were able to achieve up to a 5X improvement in Athena query performance. They found classifying queries and mapping them to the optimal Athena configuration had the biggest impact.

4. What AWS Glue features are helpful for Athena?

AWS Glue is a fully managed extract, transform and load (ETL) service that makes it easy to prepare and load data for analytics. Let‘s look at a few Glue capabilities that are particularly useful with Athena.

Glue Data Catalog is a central repository to store structural and operational metadata for data assets. Glue crawlers can automatically scan data sources, extract schema information, and populate the Glue Data Catalog. Athena integrates with the Glue Data Catalog so you can create tables without having to manually define schemas.

Glue DataBrew is a visual data preparation tool that enables data analysts and data scientists to clean and normalize data without writing code. It provides pre-built transformations for common formatting and quality issues. You can use DataBrew to explore and experiment with data sets before registering them with Athena.

Glue Studio is an easy-to-use graphical interface for creating, running, and monitoring ETL jobs in AWS Glue. It offers a drag-and-drop interface and built-in integration with data sources and targets. With Glue Studio, you can visually compose data transformation workflows and schedule them as Glue jobs that output results to S3 for querying in Athena.

(Source: AWS)

Benefits of using Glue with Athena include:

• Simplify data discovery and schema management
• Visually prepare data for querying
• Orchestrate data pipelines that feed Athena
• Enforce schema-on-read with Glue Data Catalog

As an example, Uber uses AWS Glue to manage schemas for over 10,000 Athena tables, processing trillions of records per day. Glue crawlers scan their thousands of data sources to extract schema and evolution information. Uber‘s data platform automatically updates Athena table definitions based on metadata in the Glue Data Catalog.

5. How do you configure security for Athena?

Athena provides multiple capabilities to secure access to data and ensure regulatory compliance. Let‘s look at the key components involved.

For authentication and access control:

• Use IAM policies to govern access to the Athena API and running queries
• Use Athena workgroups to separate users/applications and set query limits
• Use S3 bucket policies and object ACLs to control data access

For data protection:

• Enable S3 encryption for data-at-rest (SSE-S3, SSE-KMS, or CSE-KMS)
• Use Glue Data Catalog encryption for metadata
• Configure SSL/TLS encryption for data-in-transit
• Use Athena query results encryption
• Limit Athena access to VPC endpoints

For auditing and monitoring:

• Enable CloudTrail logging of Athena API calls
• Use CloudWatch metrics to track Athena usage and performance
• Use S3 access logging to record all requests to S3 objects
• Use VPC Flow Logs to log all network traffic in Athena VPC
• Use Athena query history to review past queries

As an additional layer of security, you can use AWS Lake Formation to implement fine-grained access control at the table, column, and row level. Lake Formation integrates with the Glue Data Catalog to enforce a consistent set of permissions across all analytics services accessing the data lake.

(Source: AWS Athena Security Whitepaper)

6. How can Athena be used for machine learning?

Athena is not only useful for analytics and BI, but also for preparing data and generating features for machine learning (ML). Athena can query semi-structured and unstructured data that is often used for ML such as logs, JSON, and text.

Common ML uses cases with Athena include:

• Querying and joining data sets to create training data
• Transforming and cleaning data for feature engineering
• Filtering reference data used for model inference
• Analyzing and debugging model performance metrics

To make it easier to build ML models, Amazon SageMaker integrates directly with Athena. You can use Athena to query and pre-process data from S3, then save the results to a SageMaker feature store. SageMaker can then access the feature store to train models.

Alternatively, you can use the SageMaker Data Wrangler to import and prepare ML data sets. Data Wrangler provides a visual interface to query data sources using Athena, apply built-in transformations, and output results for model training.

(Source: AWS)

To illustrate, Vanguard, one of the largest investment management companies, used Athena and SageMaker to build an ML model to predict investor complaints and issues. Their data science team used Athena to extract and join data from multiple sources like CRM and trading systems. They then used SageMaker to train an XGBoost model on the Athena feature set.

In addition to SageMaker integration, Athena also enables you to invoke SageMaker models for inference within a SQL query. This allows you to apply ML models to new data from S3 and write the scored results. Athena uses a feature called User Defined Functions (UDFs) to call a SageMaker hosted model endpoint.

Here‘s an example of using an Athena UDF to invoke a SageMaker model that predicts customer churn:

SELECT id, 
 sagemaker_invoke_model(
  ‘customer-churn-model‘, 
  CAST(ROW(age, income, credit_score) AS JSON)
 ) AS predicted_churn
FROM customers; 

(Example from AWS Machine Learning Blog)

Conclusion

Amazon Athena is a powerful serverless query engine that has transformed how organizations analyze big data in the cloud. Its ability to query data without infrastructure, support for diverse data formats, and seamless integration with AI/ML tools like SageMaker make it a popular choice for analytics and ML workloads.

In this article, we took a deep dive into Athena‘s architecture, performance, security, and ML integration. We looked at best practices and advanced techniques to optimize Athena, like using machine learning to automate tuning.

To learn more, check out the official Amazon Athena User Guide, the AWS Big Data Blog, and the book Building Big Data Solutions with Amazon Athena.