Iceberg is an open table format for very large analytic datasets. We also explain how to upsert and merge in an S3 data lake using an Iceberg framework and apply Lake Formation access control using Athena. In this post, we show you how to configure Lake Formation using Iceberg table formats. You get the flexibility to choose the table and file format best suited for your use case and get the benefit of centralized data governance to secure data access when using Athena. In 2022, we announced that you can enforce fine-grained access control policies using AWS Lake Formation and query data stored in any supported file format using table formats such as Apache Iceberg, Apache Hudi, and more using Amazon Athena queries. Furthermore, making the data available in the data lake in near-real time often leads to the data being fragmented over many small files, resulting in poor query performance and compaction maintenance. Performing an operation like inserting, updating, and deleting individual records from a dataset requires the processing engine to read all the objects (files), make the changes, and rewrite entire datasets as new files. ![]() However, many use cases, like performing change data capture (CDC) from an upstream relational database to an Amazon S3-based data lake, require handling data at a record level. It allows you to access diverse data sources, build business intelligence dashboards, build AI and machine learning (ML) models to provide customized customer experiences, and accelerate the curation of new datasets for consumption by adopting a modern data architecture or data mesh architecture. Building a data lake on Amazon Simple Storage Service (Amazon S3) provides numerous benefits for an organization.
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