Applicability
When to Use
✓When consolidating data from many operational databases
✓When building a single source of truth for analytics
✓When data needs to be stored in raw and processed forms
Overview
How It Works
The Data Lake Ingestion pattern uses MCP servers to extract data from multiple operational systems and load it into a centralized data lake (S3 or similar). Each source has an extraction agent that pulls data on a schedule, validates the schema, and writes partitioned files to the lake.
The key insight is that each MCP server connection represents a data source. The ingestion agent orchestrates extractions in parallel, applies schema validation, and writes to the lake with consistent partitioning (typically by date). A metadata catalog tracks what was ingested and when.
Implementation
Code Example
typescript
async function ingestFromAllSources() {
const sources = [
{ name: "users", extract: () => postgres.query("SELECT * FROM users WHERE updated_at > $1", [lastIngest]) },
{ name: "orders", extract: () => postgres.query("SELECT * FROM orders WHERE updated_at > $1", [lastIngest]) },
{ name: "events", extract: () => elasticsearch.search({ index: "events-*", query: { range: { timestamp: { gte: lastIngest } } } }) }
];
for (const source of sources) {
const data = await source.extract();
const validated = validateSchema(data, schemas[source.name]);
const partitionKey = `${source.name}/year=${now.getFullYear()}/month=${now.getMonth()+1}/day=${now.getDate()}`;
await s3.putObject({ Bucket: "data-lake", Key: `${partitionKey}/data.parquet`, Body: toParquet(validated) });
}
}Quick Info
Categorydata-pipeline
ComplexityMedium