athena emr cheat sheet

2 min read 21-10-2024

Athena EMR Cheat Sheet: A Comprehensive Guide for Data Analysts

Athena, Amazon's serverless query engine, offers a powerful and convenient way to analyze data stored in Amazon EMR (Elastic MapReduce). This cheat sheet provides a concise guide for data analysts working with Athena on EMR, covering essential commands, best practices, and useful tips.

Understanding the Basics

What is Athena?

Athena is a serverless query engine that allows you to analyze data directly in your S3 buckets without provisioning or managing servers. It uses the Presto query language, which is similar to SQL.

What is EMR?

EMR is a managed Hadoop framework that simplifies the process of running big data applications in the cloud. It allows you to easily provision clusters for data processing and analysis.

Why use Athena with EMR?

Athena is a great choice for analyzing data stored in EMR because:

Serverless: No need to manage infrastructure, reducing complexity and cost.
Fast Queries: Athena utilizes Presto, a highly optimized query engine, for fast results.
Scalability: Athena automatically scales to handle your data size and query workloads.
Cost-effective: Pay only for the queries you run, making it ideal for ad-hoc analysis.

Essential Athena Commands

1. Creating a Database:

CREATE DATABASE my_emr_database;

2. Creating a Table:

CREATE EXTERNAL TABLE my_emr_table (
  column1 STRING,
  column2 INT,
  column3 TIMESTAMP
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
LOCATION 's3://my-bucket/my-data/';

3. Querying Data:

SELECT * FROM my_emr_database.my_emr_table;

4. Filtering Data:

SELECT * FROM my_emr_database.my_emr_table WHERE column1 = 'value';

5. Grouping Data:

SELECT column1, COUNT(*) AS count FROM my_emr_database.my_emr_table GROUP BY column1;

Key Considerations

Data Format: Make sure your data is in a format that Athena can understand (e.g., CSV, ORC, Parquet).
Data Location: Specify the S3 location where your data is stored.
Permissions: Ensure you have read access to the S3 bucket containing your data.
Optimization: Optimize your queries for performance by using efficient data formats, partitioning, and indexing.

Useful Tips & Tricks

1. Using Partitions:

Partitioning your data can significantly improve query performance. For example:

CREATE EXTERNAL TABLE my_partitioned_table (
  column1 STRING,
  column2 INT
)
PARTITIONED BY (year INT, month INT, day INT)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
LOCATION 's3://my-bucket/my-data/';

2. Understanding the date_parse function:

This function helps you extract date information from text columns. For example:

SELECT date_parse(date_column, '%Y-%m-%d') AS parsed_date FROM my_emr_table;

3. Using the with clause:

This clause allows you to create temporary named queries for readability and reusability. For example:

WITH my_filtered_data AS (
  SELECT * FROM my_emr_database.my_emr_table WHERE column1 = 'value'
)
SELECT * FROM my_filtered_data;

4. Using Explain:

The EXPLAIN statement lets you analyze the execution plan for your queries, identifying potential bottlenecks and areas for optimization. For example:

EXPLAIN SELECT * FROM my_emr_database.my_emr_table;

Additional Resources

Athena Documentation: Comprehensive guide to all things Athena.
Presto Documentation: Documentation for the query language used by Athena.
EMR Documentation: Learn how to integrate Athena with EMR.
GitHub Community: Find solutions and connect with the Athena community.

Remember: This cheat sheet is just a starting point. Experiment with different commands, explore optimization techniques, and use the available resources to become an Athena expert. Happy querying!

athena emr cheat sheet