Automated ETL Pipeline with Data Quality Checks

Medium~14h estimatedTechnologyFinanceE-commerce

PostgreSQL MCP ServerAWS S3 MCP ServerSlack MCP Server

The Challenge

Business Problem

Data pipelines break silently. Bad data flows downstream for hours before anyone notices, causing incorrect reports, failed ML models, and eroded trust in data.

The Approach

Solution Overview

Connect PostgreSQL, AWS S3, and Slack MCP Servers to build ETL pipelines with built-in data quality checks that alert on anomalies and auto-pause on critical failures.

Step-by-Step

Implementation Steps

Extract from Sources

Configure PostgreSQL MCP Server to extract data from operational databases on a schedule.

Transform and Validate

Apply business rules, dedup, and run data quality checks (null rates, schema drift, value ranges).

Load to Data Warehouse

Store transformed data in S3/data warehouse with partitioning and versioning.

async function runETL() {
  const raw = await postgres.query('SELECT * FROM orders WHERE updated_at > $1', [lastRun]);
  const validated = validateData(raw.rows);
  if (validated.errorRate > 0.05) {
    await slack.sendMessage({ channel: '#data-alerts', text: `ETL paused: ${validated.errorRate*100}% error rate` });
    return;
  }
  await s3.putObject({ bucket: 'data-lake', key: `orders/${today}/data.parquet`, body: transform(validated.rows) });
}

Monitor Pipeline Health

Track pipeline runs, data freshness, and quality metrics with automated alerting.

Code

Code Examples

typescript

Data Quality Check

function validateData(rows) {
  const errors = [];
  for (const row of rows) {
    if (!row.customer_id) errors.push({ row: row.id, field: 'customer_id', error: 'null' });
    if (row.amount < 0) errors.push({ row: row.id, field: 'amount', error: 'negative' });
  }
  return { rows, errors, errorRate: errors.length / rows.length };
}

Overview

ComplexityMedium

Estimated Time~14 hours

Tools Used

PostgreSQL MCP ServerAWS S3 MCP ServerSlack MCP Server

Industry

TechnologyFinanceE-commerce

ROI Metrics

Time Saved10 hours/week on manual checks

Cost Reduction95% reduction in bad data incidents

Efficiency GainReal-time data quality monitoring