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MongoDB · change streams

Replicate MongoDB to your cloud warehouse.

Describe the sync in plain English, approve it once, and rsync.ai streams every change from MongoDB into BigQuery, Databricks, or Postgres — each document kept intact as JSON next to its _id, queryable with plain SQL.

Real-time change streamsYou approve every syncNo per-row pricing
Connect MongoDB free →See how a sync runs
Reads MongoDB change streams You approve before anything moves
Destinations

Send MongoDB data where your team already works.

Pick where it lands. rsync.ai keeps each document intact as JSON beside its _id, and you approve the plan before anything moves.

Cloud warehouse

Land one row per document for BI and analytics, with the document as a JSON column — incremental after the first snapshot.

BigQueryDatabricksSnowflakeRedshift

Another database

Land MongoDB collections in a relational database as one row per document, with the document in a JSON column (JSONB on PostgreSQL) you can query with plain SQL.

PostgreSQLMySQL

Data lake / storage

Write Parquet, JSON, or CSV to object storage for your lake.

Amazon S3GCSAzure Blob
What syncs

Every MongoDB collection, documents kept intact.

Collections
Embedded documents
Change streams
Snapshot + incremental
Document → JSON column
_id → primary key
How it works

Four steps. Nothing runs until you approve.

01

Describe it

Name the collections to replicate in plain English — no aggregation pipelines, no connector config.

02

Review the plan

rsync.ai samples the documents, shows the destination layout (_id plus a JSON document column), and flags likely PII before a row moves.

03

Approve

Nothing runs until you say yes. New fields simply arrive inside the JSON document; only a dropped collection waits for you.

04

Stream

Change streams keep the destination up to date continuously; a failed run resumes where it stopped.

After it lands

Then work with the data without leaving rsync.ai

Synced tables become something you can query, schedule and share in the same workspace.

SQL Explorer

Run SQL against your connections, with personal columns masked in previews; writes are gated by role and every write is audited.

SQL Explorer docs→

Models

Turn a query into a table that rebuilds on a schedule or when its inputs refresh, with version history and data checks.

Models docs→

Charts and Dashboards

Draw a model or saved query as a chart, then put charts on one live dashboard.

Charts→ · Dashboards→

Workflows and Ask rsync.ai

Schedule reports and data alerts to Slack or email. Describe one in plain English and Ask rsync.ai drafts it.

Scheduled reports→ · Ask rsync.ai→

Compare

Why teams move MongoDB this way.

What you care aboutExport scriptsKafka + DebeziumPer-row ETLrsync.ai
Set up without an engineerNoNoRarelyPlain English
Real-time change streamsBatch dumpsYesVariesYes
Documents kept intact as JSONBy handYou build itSometimesAutomatic
You approve before it runsn/aNoNoYes
Cost as volume growsYour timeInfra + opsRises per rowPer GB, not per row

Straight about status: the MongoDB source and the BigQuery, Databricks, Postgres, MySQL, and storage destinations are live in production. Snowflake and Redshift are in preview. Self-hosting in your own VPC is available now — source-available under the rsync.ai Source-Available License — or run rsync.ai as a managed cloud.

FAQ

MongoDB replication — common questions

How fresh is the data?
You choose the schedule, from a few times a day down to near real time. rsync.ai reads MongoDB change streams, so it writes only the documents that changed — and a failed run resumes where it stopped instead of starting over. Change streams need a replica set or a sharded deployment; a standalone mongod has no oplog, so it cannot run CDC.
How do nested documents land?
Each change lands as one row: the document's _id as the key and the full document in a JSON column (JSONB on PostgreSQL), so nested objects and arrays arrive as stored. You can shape them into typed columns with SQL or a model in rsync.ai. You approve the destination layout before the first document moves.
Do I need to write aggregation pipelines or configure a connector?
No. Describe the sync in plain English. rsync.ai samples your collections, shows the destination layout, scans for likely personal data, and shows you the plan. Nothing runs until you approve it.
How are you priced?
On data volume (GB moved), not per row or Monthly Active Rows. Each plan includes a monthly GB allowance (10 GB on the free month), and pipelines pause at your cap instead of running up charges.
Who controls what moves?
You do. No pipeline is created or started until you confirm the plan, likely personal data is flagged before it lands, and changes in document shape arrive inside the JSON document (only a dropped collection waits for a person). Self-hosting entirely in your own VPC is available now for teams that need data to never leave their network.

Put your MongoDB data in the warehouse.

Connect your database free and send your first live sync to BigQuery or Databricks today.

Connect MongoDB free →See how it works
Live on rsync.ai Cloud today · self-hosted (rsync.ai Source-Available License)