Understanding Apache Iceberg on AWS with the brand new technical information

We’re excited to announce the launch of the Apache Iceberg on AWS technical information. Whether or not you’re new to Apache Iceberg on AWS or already working manufacturing workloads on AWS, this complete technical information provides detailed steering on foundational ideas to superior optimizations to construct your transactional knowledge lake with Apache Iceberg on AWS.

Apache Iceberg is an open supply desk format that simplifies knowledge processing on giant datasets saved in knowledge lakes. It does so by bringing the familiarity of SQL tables to huge knowledge and capabilities resembling ACID transactions, row-level operations (merge, replace, delete), partition evolution, knowledge versioning, incremental processing, and superior question scanning. Apache Iceberg seamlessly integrates with standard open supply huge knowledge processing frameworks like Apache Spark, Apache Hive, Apache Flink, Presto, and Trino. It’s natively supported by AWS analytics companies resembling AWS Glue, Amazon EMR, Amazon Athena, and Amazon Redshift.

The next diagram illustrates a reference structure of a transactional knowledge lake with Apache Iceberg on AWS.

AWS prospects and knowledge engineers use the Apache Iceberg desk format for its many advantages, in addition to for its excessive efficiency and reliability at scale to construct transactional knowledge lakes and write-optimized options with Amazon EMR, AWS Glue, Athena, and Amazon Redshift on Amazon Easy Storage Service (Amazon S3).

We imagine Apache Iceberg adoption on AWS will proceed to develop quickly, and you’ll profit from this technical information that delivers productive steering on working with Apache Iceberg on supported AWS companies, finest practices on cost-optimization and efficiency, and efficient monitoring and upkeep insurance policies.

Associated assets

Concerning the Authors

Carlos Rodrigues is a Huge Knowledge Specialist Options Architect at AWS. He helps prospects worldwide construct transactional knowledge lakes on AWS utilizing open desk codecs like Apache Iceberg and Apache Hudi. He may be reached by way of LinkedIn.

Imtiaz (Taz) Sayed is the WW Tech Chief for Analytics at AWS. He’s an professional on knowledge engineering and enjoys participating with the neighborhood on all issues knowledge and analytics. He may be reached by way of LinkedIn.

Shana Schipers is an Analytics Specialist Options Architect at AWS, specializing in huge knowledge. She helps prospects worldwide in constructing transactional knowledge lakes utilizing open desk codecs like Apache Hudi, Apache Iceberg, and Delta Lake on AWS.