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Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

Enterprise data is brought into data lakes and data warehouses to carry out analytical, reporting, and data science use cases using AWS analytical services like Amazon Athena , Amazon Redshift , Amazon EMR , and so on. These data processing and analytical services support Structured Query Language (SQL) to interact with the data.

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Empower financial analytics by creating structured knowledge bases using Amazon Bedrock and Amazon Redshift

AWS Big Data

For instructions, refer to Creating a general purpose bucket. It reads metadata from your structured data store to generate SQL queries. For more information, refer to the Set up query engine for your structured data store in Amazon Bedrock Knowledge Bases. To learn more, refer to Amazon Bedrock pricing. Choose Next.

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

AWS Big Data

This expands data access to broader options of analytics engines. Under the hood, UniForm generates Iceberg metadata files (including metadata and manifest files) that are required for Iceberg clients to access the underlying data files in Delta Lake tables. With UniForm, you can read Delta Lake tables as Apache Iceberg tables.

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

AWS Big Data

Initially, data warehouses were the go-to solution for structured data and analytical workloads but were limited by proprietary storage formats and their inability to handle unstructured data. In practice, OTFs are used in a broad range of analytical workloads, from business intelligence to machine learning.

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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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Write queries faster with Amazon Q generative SQL for Amazon Redshift

AWS Big Data

Amazon Redshift is a fully managed, AI-powered cloud data warehouse that delivers the best price-performance for your analytics workloads at any scale. It enables you to get insights faster without extensive knowledge of your organization’s complex database schema and metadata. Within this feature, user data is secure and private.

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The next generation of Amazon SageMaker: The center for all your data, analytics, and AI

AWS Big Data

This week on the keynote stages at AWS re:Invent 2024, you heard from Matt Garman, CEO, AWS, and Swami Sivasubramanian, VP of AI and Data, AWS, speak about the next generation of Amazon SageMaker , the center for all of your data, analytics, and AI. The relationship between analytics and AI is rapidly evolving.