Overview
What is Amazon RDS: Amazon RDS is a fully managed relational database service from AWS. It automates provisioning, configuration, backup, and patching for relational databases. Project teams use RDS as the system of record behind web applications and transactional workloads.
How to integrate Amazon RDS with Datagrid
The Amazon RDS Datagrid integration is a one-way write destination: Datagrid's AI agents pull records from upstream sources, apply enrichment and validation, and write the output into your RDS tables as native database columns that SQL-based applications can query. Datagrid writes to RDS but does not read from it, and automations fire on a schedule or in response to trigger events such as webhooks or upstream source updates. The Amazon RDS User Guide covers the supported engines: Amazon Aurora MySQL-Compatible, Amazon Aurora PostgreSQL-Compatible, MySQL, MariaDB, PostgreSQL, Oracle, SQL Server, and Db2. Follow this setup order: prepare RDS network access, create database credentials, configure write-back sync, verify authentication, then review sync details.
Prepare your RDS network access
Before you add the connection in Datagrid, confirm that the target RDS instance can accept the connection path you plan to use.
Make your RDS instance network-accessible from Datagrid. For public access, set the instance to Publicly Accessible. Configure VPC, DNS, routing, and security group inbound rules on the database port. For private access, configure VPC networking with security controls. AWS explains these connectivity paths in its Public/private access and RDS VPC guide documentation.
Turn on SSL/TLS for the connection. RDS for PostgreSQL enables it by default. Download the AWS global certificate bundle if your connection requires certificate verification.
Create database credentials
Create or confirm a database user with write privileges on the target schema and tables. The configured user must hold sufficient privileges to write to the records Datagrid agents will update or insert.
Configure write-back sync
Use a connection checklist like this before saving the Amazon RDS integration in Datagrid.
amazon_rds_destination:
engine: PostgreSQL
endpoint: <your-db-endpoint>
port: <database-port>
database: <database-name>
username: <datagrid-write-user>
ssl_tls: enabled
certificate_bundle: <aws-global-certificate-bundle-if-required>
target_schema: <target-schema>
target_tables:
- contracts
write_privileges_required: trueAfter the database endpoint and credentials are ready, add the Amazon RDS connection in Datagrid and enter the database endpoint, username, and password. Attach the connection to an agent workflow. Set a schedule or a trigger event, such as a webhook or an upstream source update.
Verify authentication
The integration authenticates with a database username and password. Test the configured user against the target schema before attaching production workflows.
Review data sync details
These sync settings define how Datagrid writes agent output into RDS.
The integration writes to RDS and does not read from it. It writes enriched records as native database columns for fields such as risk scores, compliance tags, normalized fields, and validated records. Scheduled runs or trigger events keep RDS tables current without manual exports.
Why use Amazon RDS with Datagrid
Operators running mission-critical programs need access to reviewed records within the systems where decisions already happen. Datagrid turns upstream business files and application records into structured database fields that SQL-based applications can use immediately.
Direct write-back of enriched data: Agents deliver reviewed outputs to the production database where operational teams already work.
Coverage across RDS engines: One integration handles managed relational engines used across operational, compliance, and transactional workflows.
Trigger-driven execution: Automations run on schedules, webhooks, or upstream source updates, so tables stay current without exports.
No downstream pipeline changes: SQL-based applications use the enriched fields immediately, with no additional infrastructure required.
Multi-integration workflows: Combine RDS write-back with reads from Amazon S3 or other sources, while keeping RDS as the workflow destination.
Agent-ready workflow output: The Contract Review Agent analyzes contract sources and routes structured results into an RDS table for legal, operations, and compliance review.
What you can build with Amazon RDS Datagrid integration
Use Amazon RDS for finished agent output that needs to live in a transactional database. These patterns fit teams that need standard SQL access to reviewed, normalized, and governed records.
Contract review results in production tables: The Contract Review Agent analyzes contracts from connected document sources and writes compliance tags into an RDS contracts table. Legal and operations teams query flagged clauses with standard SQL.
Automated record validation and normalization: An agent reads customer or project records from an upstream CRM, validates and normalizes fields, and writes corrected records back to RDS. The database stays the clean system of record.
Risk scoring for underwriting and claims: Agents compute risk scores from submitted documents and route them into the policy database. Underwriting applications read the scores inside the existing application workflow.
Cross-system reconciliation feeding analytics: For reconciliation, Datagrid reads from SaaS endpoints and RDS-adjacent sources, detects discrepancies, and writes corrected records into RDS. Pair the result with the Amazon Redshift integration for downstream BI.
Resources and documentation
Amazon RDS User Guide: Official AWS documentation for all RDS engines.
Public/private access: AWS connectivity options for public and private RDS access.
RDS VPC guide: VPC, DNS, and routing requirements for external connections.
Frequently asked questions
Which database engines does the Datagrid Amazon RDS integration support?
The integration supports eight engines: Amazon Aurora MySQL-Compatible, Amazon Aurora PostgreSQL-Compatible, MySQL, MariaDB, PostgreSQL, Oracle, SQL Server, and Db2. Use the engine-specific endpoint, port, database name, username, and password when configuring the connection.
How often does Datagrid write data to RDS?
You control the cadence. Use scheduled runs or trigger events for the workflow.
Does Datagrid read from Amazon RDS?
No. Datagrid connects to Amazon RDS as a one-way data destination. Datagrid writes to RDS and does not read from it.
What data can Datagrid write to RDS?
Datagrid writes enriched records for fields such as risk scores, compliance tags, normalized fields, and validated records.
Can Amazon RDS be used with other Datagrid integrations?
Yes. Datagrid can combine multiple integrations within a single workflow, such as reading from Amazon S3 or other upstream sources while Amazon RDS remains the destination.
Similar integrations
JDBC MySQL: Provides direct MySQL TCP connections for operational workflows and migrations that teams commonly pair with RDS-based write-back and enrichment.
Google Cloud SQL - PostgreSQL: GCP's managed PostgreSQL offering, used for cross-cloud migration and hybrid replication planning alongside RDS.
Azure PostgreSQL Database: Azure's managed PostgreSQL service, a common cross-cloud peer for migration, replication, and multi-cloud analytics alongside RDS.
Amazon Redshift: Cloud data warehouse for analytics workflows that sit downstream of RDS-backed applications and Datagrid-enriched tables.
Amazon AWS S3: Staging and snapshot export destination for database workflows that need Parquet exports and downstream analytics.
Snowflake: Cloud data warehouse alternative for analytics, often paired with CDC, snapshots, or ETL for cross-platform BI alongside RDS-backed systems.