Apache Iceberg on AWS

Apache Iceberg is a high-performance table format for large analytic datasets. It is designed to work with cloud storage, making it a perfect fit for AWS data engineering tasks.

• **Table Format**: Iceberg provides a way to manage large analytical datasets with schema evolution and partitioning.
• **ACID Transactions**: Supports reliable updates and deletes without needing to lock tables.
• **Cloud-Native**: Optimized for cloud storage solutions like AWS S3.

To set up Apache Iceberg on AWS, follow these steps:

1. Provision an Amazon S3 bucket for Iceberg data storage.
2. Set up an EMR cluster with Apache Spark and the Iceberg library.
3. Configure your Spark session to work with Iceberg.

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("IcebergExample") \
    .config("spark.sql.catalog.my_catalog", "org.apache.iceberg.spark.SparkCatalog") \
    .config("spark.sql.catalog.my_catalog.type", "hive") \
    .config("spark.sql.catalog.my_catalog.warehouse", "s3://your-bucket/path/to/warehouse") \
    .getOrCreate()

Once set up, you can create and manage Iceberg tables using SQL:

CREATE TABLE my_catalog.db.my_table (
    id INT,
    data STRING
) USING iceberg;

• Use partitioning wisely to optimize query performance.
• Regularly vacuum and optimize tables to manage file sizes.
• Monitor performance and adjust configurations as needed.