Use Cases
Common Use Cases
- Daily data exports
- Historical data archival
- Analytics data preparation
- Cross-system data sharing
Before You Begin
Prerequisites
- AWS S3 bucket
- PostgreSQL database with data
- AWS credentials with S3 write access
Walkthrough
Step-by-Step Guide
1
Set Up Export Query
Define the SQL queries that extract data for the data lake.
2
Transform Data
Clean and transform the extracted data into an analytics-friendly format.
3
Upload to S3
Write the transformed data to S3 with proper partitioning.
async function exportToDataLake(table, date) {
const data = await postgres.query(`SELECT * FROM ${table} WHERE updated_at >= $1`, [date]);
const csv = convertToCSV(data.rows);
await s3.putObject({
Bucket: "my-data-lake",
Key: `${table}/year=${date.getFullYear()}/month=${date.getMonth()+1}/day=${date.getDate()}/data.csv`,
Body: csv,
ContentType: "text/csv"
});
}4
Set Up Scheduling
Automate the export to run daily with incremental extraction.
5
Add Data Validation
Verify row counts and data integrity after each export.
Examples
Code Examples
typescript
Incremental Export
async function incrementalExport(table) {
const lastExport = await getLastExportTimestamp(table);
const newData = await postgres.query(`SELECT * FROM ${table} WHERE updated_at > $1`, [lastExport]);
if (newData.rows.length > 0) {
await uploadToS3(table, newData.rows);
await saveExportTimestamp(table, new Date());
}
return { exported: newData.rows.length };
}Help
Troubleshooting
What file format should I use?+
How do I handle schema changes?+
Quick Info
DifficultyIntermediate
Time Estimate1.5 hours
Tools
AWS S3 MCP ServerPostgreSQL MCP Server