Mapping Contract Agent: Architecture, Integrations, & Security

Modern enterprise data pipelines require coordination, broad platform support, and strict security designs. The Mapping Contract Agent operates within a collaborative multi-agent architecture, integrates with major cloud datawarehouses, and runs inside a zero-storage security model. It incorporates core capabilities including automated docs, business logic execution (SQL/Python), source-to-target lineages, DQ validation rules, approval workflows, and version control.

Multi-Agent Architecture & Cooperation

In the Datapunkt ecosystem, specialized agents work together under the Root Orchestrator to automate pipelines. The Mapping Contract Agent serves as the bridge between source schemas and target models.

The Cooperation Triad

1. Source Catalog Agent: Connects to raw data sources, profiles columns, and publishes the source schema metadata.
2. Data Modeling Agent: Designs and generates target schemas based on business warehouse designs or canonical standards.
3. Mapping Contract Agent (This Agent): Consumes the source schema from the Source Catalog Agent, compares it with the target schema from the Data Modeling Agent, and establishes the Source to Target relationship, custom Business Logic, and DQ Validation rules.

Downstream Integrations

• Transformation Agent: Uses the generated mapping contract and SQL/Python files to execute physical ETL/ELT pipelines.
• Data Quality Agent: References the contract's DQ Validation rules to set expectations and track operational data quality.
• Data Lineage Agent: Reads the contract's Source to Target mapping definitions to visualize column-level lineage graphs.

Integrations

The Mapping Contract Agent supports a wide variety of database systems, file systems, and version control applications.

Supported Inbound Source Formats

• Databases: PostgreSQL, MySQL, and Oracle.
• Files: CSV, JSON, Parquet, and Avro.
• Direct Import: Direct metadata import is available for advanced enterprise configurations.

Supported Outbound Destinations

• Data Warehouses: Google BigQuery, Snowflake, and Amazon Redshift.
• Data Lakes: Google Cloud Storage (GCS), Amazon S3, and Azure Data Lake Storage (ADLS).
• Databases: PostgreSQL and MySQL.

Version Control & Deployment Integrations

• Git Repositories: Commit version-controlled YAML/JSON contracts directly to GitHub, GitLab, or Bitbucket.
• CI/CD Orchestrators: Integrates with Airflow, Prefect, Dagster, and DBT to execute generated SQL/Python transformations and run validation gates.

Chat Integration

• Collaboration Tools: Connect the agent to Slack or Microsoft Teams to edit mapping schemas within shared channels. Contact support on the Datapunkt site to request setup tokens.

Security, Compliance, and Data Residency

Datapunkt prioritizes security and enterprise compliance. The Mapping Contract Agent is designed to secure your metadata assets.

Zero-Data Storage Policy

A major security benefit of the Datapunkt architecture is that we do not store your operational database records:

• The agent only processes schema definitions, column names, and metadata structures.
• Actual transactional data, customer information, and table rows never touch or reside on our servers.
• All parsing is executed in-memory and discarded when the session ends, keeping your data within your own network borders.

Enterprise Compliance Support

• GDPR & CCPA: The agent simplifies compliance by identifying PII mapping paths and tracking where sensitive data is routed.
• SOC 2 & ISO 27001: Operates using credential isolation, TLS 1.3 transit encryption, and audit logs that record all system activities.
• Approval Workflow & Version Control: Meets compliance requirements by logging who approved each mapping contract, along with full revision histories.

Multi-Region Residency Controls

You can specify the region where the agent instance and session metadata reside:

• United States (US)
• Europe (EU)
• Asia-Pacific (APAC)
• Customer-Specified Location (for custom deployments)

Troubleshooting & Support

Follow these practices to resolve common operational challenges:

Handling Type Mismatches

If the agent reports a type mismatch between source and target fields, write a prompt in the chat asking the agent to apply explicit casting rules in the generated SQL/Python transformation.

Resolving Validation Failures

If the DQ Validation gates reject schema mapping changes, check the validation conditions in the contract editor. You can update the validation boundaries in the chat window.

Customer Support

For custom API configurations, corporate licensing, or subscription issues, contact our support team directly via the Datapunkt site.

Benefits: What Makes It Good?

• Zero-Storage Security Architecture: Keeps your sensitive business data safe by only processing metadata.
• Continuous Multi-Agent Sync: Integrates with the Source Catalog and Data Modeling agents, ensuring that mappings update automatically when schemas change.
• Streamlined Audits: The combination of Version Control history and the Approval Workflow signature logs provides instant compliance reporting.
• Highly Portable Transform Pipelines: The generation of both SQL and Python scripts makes it easy to deploy transformations to any execution engine.