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 operating manufacturing workloads on AWS, this complete technical information provides detailed steering on foundational ideas to superior optimizations to construct your transactional information lake with Apache Iceberg on AWS.
Apache Iceberg is an open supply desk format that simplifies information processing on massive datasets saved in information lakes. It does so by bringing the familiarity of SQL tables to huge information and capabilities resembling ACID transactions, row-level operations (merge, replace, delete), partition evolution, information versioning, incremental processing, and superior question scanning. Apache Iceberg seamlessly integrates with standard open supply huge information processing frameworks like Apache Spark, Apache Hive, Apache Flink, Presto, and Trino. It’s natively supported by AWS analytics providers resembling AWS Glue, Amazon EMR, Amazon Athena, and Amazon Redshift.
The next diagram illustrates a reference structure of a transactional information lake with Apache Iceberg on AWS.
AWS clients and information 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 information lakes and write-optimized options with Amazon EMR, AWS Glue, Athena, and Amazon Redshift on Amazon Easy Storage Service (Amazon S3).
We consider 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 providers, greatest practices on cost-optimization and efficiency, and efficient monitoring and upkeep insurance policies.
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Concerning the Authors
Carlos Rodrigues is a Huge Knowledge Specialist Options Architect at AWS. He helps clients worldwide construct transactional information lakes on AWS utilizing open desk codecs like Apache Iceberg and Apache Hudi. He will be reached through LinkedIn.
Imtiaz (Taz) Sayed is the WW Tech Chief for Analytics at AWS. He’s an skilled on information engineering and enjoys participating with the group on all issues information and analytics. He will be reached through LinkedIn.
Shana Schipers is an Analytics Specialist Options Architect at AWS, specializing in huge information. She helps clients worldwide in constructing transactional information lakes utilizing open desk codecs like Apache Hudi, Apache Iceberg, and Delta Lake on AWS.