Business Problem
Data teams maintain brittle ETL scripts that break silently, require manual intervention, and lack visibility into pipeline health. Data freshness and quality suffer.
Solution Overview
An agentic orchestrator that manages end-to-end data pipelines: extracting from source databases, transforming data according to business rules, loading into target systems, and monitoring pipeline health with automatic error recovery.
Implementation Steps
Define Pipeline Configuration
Create a declarative configuration describing data sources, transformations, and destinations.
Connect Source Databases
Use the PostgreSQL MCP server to extract data from source databases.
Apply Transformations
Use Claude to handle complex transformation logic including data cleaning and normalization.
Load to Destination
Write transformed data to target systems using appropriate MCP servers.
Monitor and Alert
Set up monitoring for pipeline health, data quality, and automatic alerting on failures.
Code Examples
async function runPipeline(config) {
const { sources, transforms, destinations } = config;
// Extract
const rawData = await Promise.all(
sources.map(src => postgres.tool("query", { sql: src.query }))
);
// Transform
const transformed = await claude.analyze(rawData, {
prompt: config.transformPrompt
});
// Load
for (const dest of destinations) {
await dest.server.tool("write", { data: transformed });
}
await slack.tool("send_message", {
channel: "#data-ops",
text: `Pipeline complete: ${transformed.length} records processed`
});
}