Developer productivity
Directly query Apache Iceberg and Parquet data in your data lake – NEW!
Aurora PostgreSQL enables developers and AI agents to directly query operational data together with data stored in open table formats on
Amazon S3 and S3 Tables through the same PostgreSQL applications, tools, and endpoints you
already use. You can also access data from external catalogs through the AWS Glue Data
Catalog. Your historical data stays in your data lake, accessible and queryable alongside your
live operational data with no data movement or duplication. By eliminating reverse ETL
pipelines, you reduce operational complexity, lower infrastructure costs, and simplify
application development.
MCP servers
Aurora MCP
server provides the flexibility and ease to build and deploy from the tools and agents
of your choice. A critical component in agentic AI architectures, Aurora MCP server provide the
standardized interface needed for agents to interact with developer tools and your Aurora
databases. Aurora MCP server is available on Github for PostgreSQL and MySQL.
Aurora integrates directly with the developer tools you already use, saving you hours or
weeks of time to build, test, and deploy. AI-enabled IDEs (e.g., Kiro) and agentic development platforms (e.g., Vercel) help you build in your
preferred environment and significantly shorten the path from idea to working application.
Aurora is integrated with agentic frameworks (e.g., Amazon Bedrock AgentCore, Letta,
LangGraph) to persist agent memories using a fully managed database.
Agent skills
Kiro power for Aurora PostgreSQL provides specialized skills to AI agents that contain
specific Aurora knowledge, MCP tools, and best practices to instantly understand how to work
with Aurora PostgreSQL. With one-click installation, you can start building database-backed apps
in Kiro using natural language – no Aurora expertise required.
Zero-ETL integration with Amazon Redshift
Aurora zero-ETL integration with
Amazon Redshift enables near real-time analytics and ML using Amazon Redshift on
petabytes of transactional data from Aurora by removing the need for you to build and maintain
complex data pipelines that perform extract, transform, and load (ETL) operations. Transactional
data is automatically and continuously replicated within seconds of being written in Aurora and
is seamlessly made available in Amazon Redshift.
Once data is available in Amazon Redshift, you can start analyzing it immediately and apply
advanced features like data sharing, materialized views, and Amazon Redshift ML to get holistic
and predictive insights. You can consolidate multiple tables from various Aurora database
clusters and replicate your data into one Amazon Redshift data warehouse to run unified
analytics across multiple applications and data sources. When using both Aurora serverless and Amazon Redshift Serverless,
you can generate near real-time analytics on transactional data without having to manage any
infrastructure for data pipelines. Additional information is available in Aurora
zero-ETL integrations with Amazon Redshift documentation.
Zero-ETL integration with Amazon SageMaker
Aurora zero-ETL integration with Amazon SageMaker enables near real-time access of your data in the lakehouse
architecture of SageMaker to run a broad range of analytics. With zero-ETL integration, data
from Aurora is automatically extracted and loaded into the lakehouse in SageMaker enabling you
to derive near real-time insights from your operational data. The data synced into the lakehouse
is compatible with Apache Iceberg open standards, enabling you to use your preferred analytics
tools and query engines such as SQL, Apache Spark, BI, and AI/ML tools. Additional information
is available in Aurora
zero-ETL integration with Amazon SageMaker documentation.
RDS Proxy
Aurora works in conjunction with RDS
Proxy, a fully managed, highly available database proxy that makes applications more
scalable, more resilient to database failures, and more secure. RDS Proxy allows applications to
pool and share connections established with the database, improving database efficiency and
application scalability. It reduces failover times by automatically connecting to a new database
instance while preserving application connections. It enhances security through integrations
with AWS IAM and AWS Secrets Manager.
Data API
Data API is an easy-to-use, secure HTTPS API for executing SQL queries against Aurora
databases. It eliminates the need for database drivers, client-side connection pools, and VPC
networking configuration to securely connect to an Aurora database, which makes accessing Aurora
as simple as making an API call. Data API also improves application scalability by automatically
pooling and sharing database connections and is integrated with AWS IAM and AWS Secrets Manager. You can call Data
API via applications built with an AWS SDK or through AWS AppSync GraphQL APIs.
