Athena Iceberg/Hudi Tables - Data Engineering on AWS

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Iceberg and Hudi are open-source table formats that provide features like ACID transactions, schema evolution, and data versioning. This lesson will cover how to use these table formats with Athena.

2.1 Amazon Athena

A serverless query service that allows users to run SQL queries on data stored in S3.

2.2 Apache Iceberg

A high-performance table format that supports ACID transactions, time travel, and schema evolution, designed for big data.

2.3 Apache Hudi

A data management framework that provides features like incremental data processing, data versioning, and efficient storage.

3.1 Prerequisites

• An AWS account with permissions to access Athena and S3.
• The AWS CLI installed and configured.
• Data stored in Amazon S3 in either Iceberg or Hudi format.

3.2 Creating an Iceberg Table

To create an Iceberg table in Athena, use the following SQL statement:

CREATE TABLE mydb.my_iceberg_table (
    id INT,
    name STRING,
    created_at TIMESTAMP
) 
WITH (
    format = 'ICEBERG',
    location = 's3://your-bucket/path/to/table/'
);

3.3 Creating a Hudi Table

To create a Hudi table in Athena, use the following SQL statement:

CREATE TABLE mydb.my_hudi_table (
    id INT,
    name STRING,
    created_at TIMESTAMP
) 
WITH (
    format = 'HUDI',
    location = 's3://your-bucket/path/to/table/'
);

3.4 Querying Data

After creating the tables, you can query them using standard SQL:

SELECT * FROM mydb.my_iceberg_table WHERE created_at > '2023-01-01';

• Use partitioning to optimize query performance.
• Regularly vacuum and optimize your Iceberg and Hudi tables.
• Monitor query performance and adjust configurations as needed.
• Ensure data consistency by using ACID transactions.