Journalist Summary:
The Apache Iceberg on AWS Technical Guide has been launched to assist individuals new to Apache Iceberg on AWS or those currently using it for production workloads. Apache Iceberg is an open-source table format that simplifies data processing in data lakes by bringing SQL table familiarity to big data. It offers features such as ACID transactions, row-level operations, partition evolution, data versioning, incremental processing, and advanced query scanning. The integration of Apache Iceberg with popular big data processing frameworks like Apache Spark, Apache Hive, Apache Flink, Presto, and Trino, makes it a versatile tool for data engineers. AWS analytics services like AWS Glue, Amazon EMR, Amazon Athena, and Amazon Redshift natively support Apache Iceberg.
The technical guide provides detailed guidance on building a transactional data lake using Apache Iceberg on AWS with the help of a reference architecture diagram. AWS customers and data engineers use Apache Iceberg for its benefits, high performance, and scalability in creating transactional data lakes and write-optimized solutions using Amazon EMR, AWS Glue, Athena, and Amazon Redshift on Amazon S3.
The authors of the technical guide, Carlos Rodriguez, Yomtiaz (Taz) Sayed, and Shain Schippers, are AWS solutions architects specializing in big data and analytics. They offer guidance on working with Apache Iceberg on supported AWS services, cost and performance optimization, and effective monitoring and maintenance policies.
Overall, the adoption of Apache Iceberg on AWS is expected to grow rapidly, and individuals can benefit from the technical guide’s productive guidance to leverage this tool effectively for their data processing and analytics needs.
Article Source
https://aws.amazon.com/blogs/big-data/understanding-apache-iceberg-on-aws-with-the-new-technical-guide/