AWS Athena is a serverless interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL. This guide provides a comprehensive overview of Athena‘s capabilities, features, use cases, best practices, limitations, pricing, and more so you can see if it‘s the right fit for your data analytics needs.
What is AWS Athena?
Athena lets you run ad-hoc queries using ANSI SQL against data stored in Amazon S3 without needing to setup infrastructure like you would with services like Presto or Hive.
It works directly with a variety of data formats like CSV, JSON, ORC, Avro, and Parquet that are stored in S3. The query results are also stored in S3 buckets defined by the user.
Key capabilities and benefits of Athena include:
- Serverless architecture – No infrastructure to setup or manage, auto scaling
- Uses standard SQL – ANSI SQL makes query writing accessible
- Works directly with S3 data – No data movement needed
- Supports open formats – Works with CSV, JSON, ORC, Avro, Parquet
- Fast performance – Parallel query execution delivers fast results
- Pay per query pricing – Pay only for the queries you run
- Secure – Encryption, data access controls via IAM
Athena is ideal for ad-hoc data analytics and data discovery directly against data stored in S3. Use cases include log analytics, business intelligence, reporting, and more.
Key Features and Capabilities
Athena comes packed with features that enable fast and flexible queries against semi-structured and unstructured data.
Serverless Architecture
The serverless architecture removes the need to setup and manage infrastructure. Athena handles parallel query execution and scaling automatically so you don‘t have to worry about configuring and managing clusters.
You also pay only for the queries you run rather than paying for idle capacity. The serverless architecture helps keep costs down while still providing excellent performance.
ANSI SQL Support
Athena uses standard ANSI SQL for writing queries. This makes it highly accessible to anyone already familiar with SQL. You can write queries with complex joins, expressions, and more just like any other SQL engine.
Compatibility with ANSI SQL also makes it easy to migrate queries from tools like Presto and Hive to Athena.
Integration with Amazon S3
Rather than loading data into a separate data warehouse or analytics engine, Athena queries data directly inside S3 buckets. This eliminates delays and costs associated with moving data.
Query results are also stored directly in S3. This makes Athena a fantastic tool for ad-hoc analysis on S3-resident data.
Open Data Formats
Athena works with common structured and semi-structured data formats including:
- CSV – Comma separated values format
- JSON – Popular semi-structured format
- ORC – Optimized row columnar format
- Avro – Data serialization system
- Parquet – Columnar storage format
Support for open formats provides flexibility in the types of data Athena can query.
Security
Athena integrations with a variety of AWS security services helps keep your data safe. Features include:
- Encryption – Encrypt data at rest and in transit
- IAM access controls – Use identity policies to restrict data access
- VPC integration – Ensure queries execute within your VPC
These features limit access to sensitive data and provide preventative measures against leaks.
Performance
While serverless, Athena provides excellent performance by running queries in parallel. It‘s common to get query results back in seconds, even for queries that scan large datasets.
Athena also implements best practices internally that maximize performance like caching commonly used data in memory and optimizing joins.
Various configuration parameters can also help accelerate performance further for specific use cases.
Getting Started with Athena
We‘ll now walk through getting started with Athena using the AWS Management Console.
Step 1 – Setup Query Result Location
The first thing you need to do is setup an S3 bucket location where Athena will store query results.
- Open the Athena console and choose Settings
- Under Query result location select an existing S3 bucket
- Alternatively create a new S3 bucket just for Athena query results
Make sure the bucket has proper permissions so Athena can read/write query output files.
Step 2 – Create a Database
Similar to traditional SQL databases, Athena organizes saved queries and tables using databases. Let‘s create a database to get started.
- From the left sidebar choose Query Editor
- Click on the dropdown next to the database name
- Select Create database and give your database a name
Take note that Athena also comes preconfigured with a sample database called sampledb you can use to run practice queries over public data sets.
Step 3 – Create a Table
With a database setup, you next need to create tables mapped to the underlying data formats within S3 you want to query.
For example, let‘s say we have a CSV file stored in S3 we want to analyze. We would create a table with columns mapped to the headers defined in the CSV.
Here is an example create table statement:
CREATE EXTERNAL TABLE my_table (
id BIGINT,
name STRING,
salary FLOAT,
department STRING
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ‘,‘
LOCATION ‘s3://my-bucket/csv-data/‘;This maps the my_table to data stored in the csv-data folder in the S3 bucket my-bucket.
At this point you can start writing SQL queries against my_table to explore the data!
Let‘s try a simple query:
SELECT * FROM my_table LIMIT 10;This will return the first 10 rows from my_table so we can verify everything is working correctly.
Step 4 – Write Queries & Analyze Data!
With a database and tables configured, you can now start writing SQL queries against your data.
Athena lets you run queries with aggregations, joins across data sources, window functions for time series analysis, and more!
Some examples of what you can do:
- Analyze sales by region over time
- Join web server logs with customer data
- Calculate usage analytics from product event data
- Explore raw user behavior data to find trends
- Report on operational metrics on dynamic dashboards
- Discover product issues from support tickets
Just about anything you can do in standard SQL is supported by Athena. It‘s a fantastic ad-hoc analytics tool for data discovery and reporting directly on S3 resident data.
The serverless architecture means all query execution and scaling is automatically managed so you can focus purely on the analysis.
Athena Use Cases
Athena‘s flexibility, performance, and ease of use make it a great fit for numerous analytics use cases below both big data and small.
Log Analytics
Server logs and application logs contain rich information but analyzing them traditionally requires complex extract and load pipelines.
With Athena you can analyze raw application and server logs directly in S3 using standard SQL queries. This unlocks faster insights without moving data around.
Example log sources:
- Web server access logs
- Application audit logs
- API call logs
- Mobile app event logs
Business Intelligence
Traditional BI requires loading data into a data warehouse or lake. Athena eliminates loading time by enabling SQL queries directly on source data.
It‘s a lightweight tool accessible to every skill level in the organization to derive insights from data in S3 across use cases like:
- Sales reporting
- Operational metrics
- Marketing campaign analytics
- User behavior analysis
- A/B testing evaluation
Faster queries and time to insight gives organizations an information edge to outpace competitors.
Data Discovery & Profiling
Many data lake initiatives start with ingesting raw data into S3 from across the business. That data then needs to be explored and understood to determine downstream processing.
Athena allows data teams to query raw datasets directly in S3 to understand:
- What data exists
- The structure of the data
- What kinds of analysis are possible
- What quality issues exist
- How much data exists over time
These data discovery queries help chart the best path forward for follow-on data transformation and loading into analytics tools.
Athena Integrations
A key benefit of Athena is its ability to integrate across many other AWS and third-party services:
This enables more advanced analytics use cases by combining Athena with tools like:
- AWS Glue – Catalog metadata from Glue Crawlers to query data sources
- Amazon QuickSight – Visually explore data queried by Athena
- Amazon SageMaker – Generate datasets for machine learning
- AWS Lambda – Execute custom ETL transformations with queries
- JDBC/ODBC Drivers – Connect BI tools like Tableau directly using SQL
These represent just some of the possible integrations available to enhance Athena‘s capabilities within your analytics stack.
Athena vs Other Options
How does Athena compare with alternative query engines? Here we compare to two popular options: Google BigQuery and Presto.
Athena vs BigQuery
The key differences between Athena and Google BigQuery are:
- Pricing – Athena charges per query vs BigQuery per data scanned
- Performance – BigQuery offers more consistency in complex queries
- Formats – Athena supports more open source formats
- Ecosystem – BigQuery has deeper GCP native integrations
So BigQuery makes sense for GCP-centric organizations, while Athena better fits those with open data formats working primarily in AWS.
Both offer serverless architectures with strong SQL support, so ease of use is comparable. Performance can vary per workload between the two.
Athena vs Presto
The open-source Presto project offers similar SQL query capabilities but does require running your own clusters.
Key differences:
- Serverless – Athena fully manages the infrastructure
- Cost – Presto requires cluster resources even when idle
- Ease of use – Athena setup takes minutes vs Presto‘s complexity
- Formats – Both support open data formats like ORC and Parquet
So Athena makes sense for users who don‘t want the admin overhead and like the pay-per-query pricing. Presto can work for those needing maximum customization or open source flexibility.
Athena Limitations
While Athena delivers a lot of value, it does come with some limitations to be aware of relative to alternatives:
- Limited back-end code extensibility and user defined functions
- Less mature complex query performance and optimizations
- No native indexing capabilities
- No transactional consistency (eventual consistency model)
- Data sampling and summary statistic challenges
- Join performance inconsistencies at large data scale
These limitations may affect advanced analytical use cases that require high complexity queries across very large datasets.
For large-scale data warehousing needs, options like BigQuery, Redshift, and Snowflake have more enterprise capabilities.
But Athena handles pretty much any level of ad-hoc querying very well and continues improving to handle the more advanced areas over time.
Athena Pricing
Athena bills based on the amount of data scanned by each query you run. So you only pay for what you use rather than pre-purchasing capacity.
There is no charge for DDL statements like CREATE TABLE or data definition queries that scan little data. Complex queries that process more data incur higher charges.
The pay-per-query pricing model brings some key benefits:
- No minimum fees or upfront commitments
- Pay for exact usage rather than pre-purchased capacity
- Handles any size workload without overpaying
- Easy to budget per project vs system-wide
Current Athena pricing sits at $5 per TB of data scanned. So a 10 GB query would cost $0.05. They also offer volume discounts that can drop the rate to under $1 per TB depending on usage.
Many Athena users see monthly costs below $100 given the ad-hoc nature avoiding heavy scans. But costs can rise into the thousands for large enterprises executing heavy workloads.
Understanding query patterns and sizing allows forecasting spends. Athena also integrates with cost allocation tags and AWS Budgets for tracking.
Best Practices for Using Athena
Follow these best practices when working with Athena to maximize performance and lower cost:
- Use columnar formats – ORC, Parquet, Avro compress better and reduce scans
- Partition data – Split data by date, region etc to prune scanning
- Convert CSV to another format – If possible as CSV has no compression
- Use partitioning – Speed queries by limiting data scans to necessary partitions
- Encrypt data – Protect data at rest and enable encryption in transit
- Implement access controls – Utilize IAM, S3 policies to restrict access
Pay attention to query performance in the Athena console and identify slow running queries. Lookup optimization strategies for poorly performing queries by searching places like AWS Big Data Blog and community posts.
New Athena Features
Athena continues rapidly evolving with new capabilities in every major release.
Some recently added features include:
- Federated SQL support to combine data across catalogs
- Automatic workgroup creation for easier collaboration
- Support for querying data directly from JDBC sources
- Ability to handle nested data structures likestructs and arrays
- Added geospatial functions anddatatype support
For the latest on upcoming Athena features check the Athena product roadmap.
Frequently Asked Questions
Here are answers to some common questions users have about AWS Athena:
Q: Does Athena replace traditional data warehouses like Redshift?
Athena excels more at ad-hoc analysis rather than the heavy workloads and concurrency requirements data warehouses are built for. View Athena as complementary to warehouses, ideal for exploring S3 data.
Q: Is Athena fully compatible with Apache Presto SQL?
Athena utilizes the Presto architecture internally but has limitations and differences compared to open source PrestoDBSQL. Certain advanced syntax may not work the same.
Q: Can Athena handle petabyte scale data volumes?
Yes, Athena routinely handles querying petabyte scale datasets with proper partitioning. Performance depends more on query complexity than purely data volume.
Q: Does Athena work with data stored on-premise or in other clouds?
Athena can query data in other locations using federated queries. But performance depends on available network bandwidth between Athena and the remote data store.
Q: How do I improve Athena performance and lower costs?
Key optimization strategies include:
- Partitioning data
- Using columnar formats
- Compressing data
- Avoiding full table scans
- Applying data classification
Improving query patterns reduces scanned data volumes and delivers faster results.
Summary
AWS Athena offers an easy way to analyze and gain insights from data stored in S3. Its serverless architecture removes infrastructure burdens while still providing fast SQL queries.
Athena works with open data formats directly in S3 buckets without needing ETL or data movement. This enables ad-hoc analysis at any data scale.
While limitations exist in advanced analytics use cases, Athena excels at what it‘s designed for – giving SQL users an easy way to analyze S3 data on demand.
If you need to analyze or report on S3 resident data, Athena should be high on your list to evaluate.
