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aws-data-analytics Claude Code Plugin

Author
Amazon Web Services
Category
Development
Topics
Data Engineering & Analytics · Cloud, Deployment & CI/CD · Databases & Storage
First cataloged
2026-07-09 (UTC)
Explanation last updated
2026-08-14 (UTC)
Source (GitHub) last updated
2026-08-14 (UTC) (13 days ago)

The explanation below is AI-generated. Please verify it against the sources.

This plugin adds AWS data-engineering skills to a coding assistant, covering data lake creation, ingestion, querying, cataloging, vector storage, external connectivity, and OpenSearch operations. According to the README, it packages eight skills built around AWS services such as S3 Tables, AWS Glue, Amazon Athena, S3 Vectors, and Amazon OpenSearch Service, plus one MCP server (aws-mcp) that provides AWS API access and documentation lookup. Users interact with these skills through natural-language requests like creating an Iceberg table, importing data from a database, or auditing catalog contents. Operation requires configured AWS credentials, a target region, and IAM permissions scoped to the specific AWS service namespaces used (athena, glue, s3, s3tables, s3vectors). The skills are described as customizable, so an organization can fork and adjust them to its own conventions.

About the service

The plugin operates against AWS-managed data and analytics services — including Amazon S3 Tables, AWS Glue Data Catalog, Amazon Athena, Amazon S3 Vectors, Amazon OpenSearch Service, and Amazon Redshift — and can also connect to external data sources such as Oracle, SQL Server, PostgreSQL, MySQL, Snowflake, and BigQuery through AWS Glue connections, authenticated with the user's own AWS credentials.

What you can do with aws-data-analytics

  • Ingest data into the data lake from S3, JDBC databases, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables
  • Resolve and locate data lake assets by name, keyword, column, or S3 path
  • Inventory and audit the AWS Glue Data Catalog across S3 Tables, Redshift-federated, and remote Iceberg catalogs
  • Store and query vector embeddings with Amazon S3 Vectors for semantic search or RAG
  • Provision and operate Amazon OpenSearch Service or Serverless for search, log, and trace analytics

Sources

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