Remove Big Data Remove Metadata Remove Reference
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Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

Amazon Athena provides interactive analytics service for analyzing the data in Amazon Simple Storage Service (Amazon S3). Amazon Redshift is used to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes. Table metadata is fetched from AWS Glue.

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Manage concurrent write conflicts in Apache Iceberg on the AWS Glue Data Catalog

AWS Big Data

However, commits can still fail if the latest metadata is updated after the base metadata version is established. Iceberg uses a layered architecture to manage table state and data: Catalog layer Maintains a pointer to the current table metadata file, serving as the single source of truth for table state.

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Expand data access through Apache Iceberg using Delta Lake UniForm on AWS

AWS Big Data

The landscape of big data management has been transformed by the rising popularity of open table formats such as Apache Iceberg, Apache Hudi, and Linux Foundation Delta Lake. These formats, designed to address the limitations of traditional data storage systems, have become essential in modern data architectures.

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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

Eventually, transactional data lakes emerged to add transactional consistency and performance of a data warehouse to the data lake. Central to a transactional data lake are open table formats (OTFs) such as Apache Hudi , Apache Iceberg , and Delta Lake , which act as a metadata layer over columnar formats.

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Use open table format libraries on AWS Glue 5.0 for Apache Spark

AWS Big Data

Open table formats are emerging in the rapidly evolving domain of big data management, fundamentally altering the landscape of data storage and analysis. For more details, refer to Iceberg Release 1.6.1. These are useful for flexible data lifecycle management. For more details, refer to Delta Lake Release 3.2.1.

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Build a high-performance quant research platform with Apache Iceberg

AWS Big Data

Iceberg offers distinct advantages through its metadata layer over Parquet, such as improved data management, performance optimization, and integration with various query engines. Icebergs table format separates data files from metadata files, enabling efficient data modifications without full dataset rewrites.

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Write queries faster with Amazon Q generative SQL for Amazon Redshift

AWS Big Data

Amazon Q generative SQL for Amazon Redshift uses generative AI to analyze user intent, query patterns, and schema metadata to identify common SQL query patterns directly within Amazon Redshift, accelerating the query authoring process for users and reducing the time required to derive actionable data insights.