Source connector

MongoDB connector

MongoDB integration for databases, warehouses and BI tools

Document database often used as an operational source for analytics. Datrise captures MongoDB entities such as collections, documents, change streams, and schema snapshots, then lands normalized and typed models for warehouse reporting, attribution, and AI workflows.

The MongoDB Wire Protocol drivers + Change Streams (pre/post-images, MongoDB 6.0+) authenticates with username / password, mTLS, service account, incremental runs key on resumeToken (Change Streams).

Of the 4 objects, 2 sync as upserts keyed on the primary key and 2 as append-only streams, so each incremental run moves only what changed since the last watermark. Every object lands in its own table (for example `mongodb_collections`) with a schema derived from the MongoDB API rather than a flattened CSV, in any of the 43 supported destinations.

Datrise is in early access: pick a destination below to open the MongoDB pipeline page, and join the waitlist to get the MongoDB connector (Database) with guided onboarding.

Incremental syncSchema-aware mapping43 destinations available

What MongoDB data Datrise extracts

Each object lands in its own typed table. Sync mode is derived from the object: history-style objects append, everything else upserts.

Object	Landing table	Sync mode
collections	mongodb_collections	Upsert by primary key, incremental on updated-at
documents	mongodb_documents	Upsert by primary key, incremental on updated-at
change streams	mongodb_change_streams	Append-only, incremental by timestamp
schema snapshots	mongodb_schema_snapshots	Append-only, incremental by timestamp

MongoDB API: setup facts for the connector

Verified against the vendor documentation on 2026-09-02. Unknown values are left blank rather than guessed.

API	MongoDB Wire Protocol drivers + Change Streams (pre/post-images, MongoDB 6.0+)
Authentication	username / password, mTLS, service account — SCRAM (username/password) is the default; x.509 certificate auth is common for service-to-service TLS, and Atlas additionally supports AWS-IAM and OIDC as service-account-style mechanisms (Kerberos/LDAP are Enterprise-only and LDAP is deprecated as of MongoDB 8.0).
Pagination	cursor — Reads are cursor-based (`find()`/`getMore` batches); Change Streams separately expose a resumable position via an opaque `resumeToken` returned on each event and in `postBatchResumeToken`.
Rate limit	Not published
Incremental cursor	resumeToken (Change Streams) — A Change Stream's `resumeToken` (passed back via `resumeAfter`/`startAfter`) is the native CDC cursor; it only resolves as far back as the oplog's retention window, so a connector that falls behind that window must fall back to a full resync.
Webhooks	No
Bulk export	Yes — mongodump / mongoexport (BSON or JSON/CSV dumps, optionally with --oplog for a consistent point-in-time snapshot)
Deletes detectable	Yes — Change Stream events with `operationType: "delete"` report the removed document's `_id` via `documentKey`; recovering its prior field values requires pre-images, which must be enabled per collection ahead of time via `changeStreamPreAndPostImages`.

Official API reference

What bites when replicating MongoDB

`resumeAfter`/`startAfter` can only replay as far back as the oplog still retains, and self-managed deployments have no forced minimum oplog size, so a connector that pauses too long loses its resume position and must fall back to a full collection re-sync.
Pre-images (needed to see a deleted or updated document's prior state) must be turned on per collection with `changeStreamPreAndPostImages: { enabled: true }` *before* the change happens and are retained only for `expireAfterSeconds` (minimum 100 seconds) — there's no way to enable them retroactively.
A change stream cannot `resumeAfter` an `invalidate` event (e.g. a dropped database or renamed collection); only `startAfter` continues past one, and even then only within the still-live oplog window.
`mongodump --oplog`, the option that makes a dump consistent as of a single point in time, does not work at all on sharded clusters and aborts mid-dump if a `renameCollection`, an aggregation with `$out`, or a role/privilege change occurs while it runs.

Compare this against every other documented API

Popular destinations for MongoDB

Choose a destination below to open the ready pipeline page.

A
Airtable
Relational spreadsheet destination for ops and go-to-market teams.
View pipeline
A
Amazon Athena
Serverless SQL over S3 data lake tables.
View pipeline
A
Amazon DynamoDB
Serverless key-value and document store on AWS.
View pipeline
A
Amazon QuickSight
AWS serverless BI with SPICE and embedded analytics.
View pipeline
A
Amazon Redshift
AWS petabyte-scale warehouse with Spectrum.
View pipeline
A
Amazon S3 Data Lake
Object storage landing zone for parquet and snapshots.
View pipeline
A
Apache Superset
Open-source BI for SQL exploration, charts, and dashboard publishing.
View pipeline
A
Azure Data Lake Storage
ADLS Gen2 object storage for analytics workloads.
View pipeline
A
Azure Synapse
Microsoft analytics workspace with SQL pools.
View pipeline

MongoDB connector FAQ

What MongoDB data does the connector extract?: 4 objects: collections, documents, change streams and schema snapshots. Each lands in its own table with types preserved; history-style objects (2: change streams and schema snapshots) load as append-only streams.
Is the MongoDB integration incremental?: Yes. After the initial full load, every run reads only records changed since the last watermark (updated-at or event timestamp) and applies an upsert or an append depending on the object.
Where can I load MongoDB data?: Into 43 destinations: relational databases, cloud warehouses, data lakes, spreadsheets and BI tools. Each MongoDB → destination pair has its own page with the object-to-table mapping.

Early access

Launch your MongoDB pipeline with guided onboarding

Join the waitlist to get priority access for MongoDB integrations with guided setup, schema-aware mapping, and production-grade incremental sync.