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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights.

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. and later supports the Apache Iceberg framework for data lakes. The snapshot points to the manifest list. AWS Glue 3.0

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Use AWS Glue ETL to perform merge, partition evolution, and schema evolution on Apache Iceberg

AWS Big Data

As enterprises collect increasing amounts of data from various sources, the structure and organization of that data often need to change over time to meet evolving analytical needs. Schema evolution enables adding, deleting, renaming, or modifying columns without needing to rewrite existing data.

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Use Amazon Athena with Spark SQL for your open-source transactional table formats

AWS Big Data

AWS-powered data lakes, supported by the unmatched availability of Amazon Simple Storage Service (Amazon S3), can handle the scale, agility, and flexibility required to combine different data and analytics approaches.

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Analyze Elastic IP usage history using Amazon Athena and AWS CloudTrail

AWS Big Data

By extracting detailed information from CloudTrail and querying it using Athena, this solution streamlines the process of data collection, analysis, and reporting of EIP usage within an AWS account. Additionally, you can analyze activity logs with AWS CloudTrail Lake and Amazon Athena.

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Enforce fine-grained access control on Open Table Formats via Amazon EMR integrated with AWS Lake Formation

AWS Big Data

With Amazon EMR 6.15, we launched AWS Lake Formation based fine-grained access controls (FGAC) on Open Table Formats (OTFs), including Apache Hudi, Apache Iceberg, and Delta lake. Many large enterprise companies seek to use their transactional data lake to gain insights and improve decision-making.