DatriseAI-first ETL

Azure Table Storage to MongoDB Integration

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

How Datrise loads Azure Table Storage into MongoDB

Datrise syncs Azure Table Storage's tables, entities, partition keys, row keys, properties, and timestamps 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

Azure Table Storage: Key-value NoSQL store on Azure.

MongoDB: Document database for flexible schemas.

How Azure Table Storage entities map to MongoDB

Azure Table Storage entityMongoDB objectNotes
tablesazure_table_storage_tablesid PK · custom fields → native nested documents
entitiesazure_table_storage_entitiesid PK · linked to azure_table_storage_tables
partition keysazure_table_storage_partition_keysid PK · linked to azure_table_storage_tables
row keysazure_table_storage_row_keysid PK · linked to azure_table_storage_tables

FAQ

How does Datrise handle Azure Table Storage'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 Azure Table Storage 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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