DatriseAI-first ETL

Teradata to MongoDB Integration

AI-first ETL from Teradata into MongoDB. Governed entities, incremental sync, typed landing tables.

How Datrise loads Teradata into MongoDB

Datrise syncs Teradata's tables, rows, schemas, indexes, primary indexes, and query history into MongoDB as a collection per source entity. Flexible or custom fields land in native nested documents, and timestamps such as created, updated, and status changes are typed as BSON Date.

Sync is incremental: Datrise uses upserts by stable id with updateOne(upsert) on the source primary key, so re-runs update only what changed. Optional sharding on the entity id for large collections. Mongo has no fixed schema, so Datrise keeps field types consistent across documents to avoid mixed-type query surprises.

Ideal for document-oriented apps that want CRM data in their existing Mongo store.

Endpoints

Teradata: Teradata analytical database for enterprise workloads.

MongoDB: Document database for flexible schemas.

How Teradata entities map to MongoDB

Teradata entityMongoDB objectNotes
tablesteradata_d_tablesid PK · custom fields → native nested documents
rowsteradata_d_rowsid PK · linked to teradata_d_tables
schemasteradata_d_schemasid PK · linked to teradata_d_tables
indexesteradata_d_indexesid PK · linked to teradata_d_tables

FAQ

How does Datrise handle Teradata's custom fields in MongoDB?

Flexible values are stored as native nested documents, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native MongoDB types.

How does the Teradata to MongoDB sync stay up to date?

It runs incrementally — Datrise uses upserts by stable id with updateOne(upsert) on the source primary key.

Related pipelines

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