150+ managed destinations
Cloud warehouses, data lakes, other databases, BI tools, and AI or vector stores - deliver MongoDB data wherever it needs to go, all maintained for you.
MongoDB
Operational Database
Move MongoDB data anywhere, in real time and in your control.
Dataddo is the turnkey data layer for MongoDB. Stream changes with change streams, replicate large collections in parallel, and deliver clean, governed data to 150+ destinations - with no pipelines to build or maintain. Run the data plane in the cloud, a sovereign region, or on-premises next to your database, so your data stays where it belongs. Fully managed and database-agnostic - no lock-in.
ARCHITECTURE
Where MongoDB fits as a source in your data stack
Sources
Business / DB / File / Streaming Connectors
450+ available, any direction
HubSpot
Google Ads
Google Sheets
Salesforce
SAP S/4HANA
Oracle
Microsoft Dynamics 365
AWS S3
Azure SQL
Google Cloud SQL
MySQL
PostgreSQL
IBM Db2
+ any other
Your existing stack
Orchestration
Monitoring
Governance & Lineage
IAM & SSO
Dataddo Platform
Speed
Security
Governance
Control Plane
UI
Visual workspace for teams to build, run and monitor pipelines
API
Programmatic interface to embed Dataddo in your own stack and workflows
MCP
Dedicated interface for AI & agents to access governed data in context
Data Plane
Isolated deployment
Hyperscalers
AWSAzureGoogle Cloud
Isolated deployment
EU Cloud Providers
HetznerOVHcloudSTACKIT
Isolated deployment
On-Prem
KubernetesOpenShift
Data + Metadata
Destinations
DWH / Data Lake / Lakehouse
Snowflake
Databricks
Microsoft Fabric
Google BigQuery
Amazon Redshift
Azure Synapse
Oracle
IBM Db2
Universal PostgreSQL
Universal MySQL
+ any other
Data + Metadata
Consumption
AI & Agents / Analytics
Claude
Gemini
OpenAI
Mistral
BottleCap AI
Power BI
Tableau
Looker Studio
Databox
+ any other
Sources
Business / DB / File / Streaming Connectors
450+ available, any direction
Orchestration
Monitoring
Governance & Lineage
IAM & SSO
Dataddo Platform
Speed
Security
Governance
Control Plane
Data Plane
Destinations
DWH / Data Lake / Lakehouse
Consumption
AI & Agents / Analytics
ETLELTReverse ETLCDCData Streaming
Any direction, any workload
YOUR LIVE DATA SOURCE
Turn MongoDB into a live source for every system
Sync MongoDB to your warehouse, lake, other databases, BI, and AI - continuously and without straining production. No pipelines to build, and no bad or broken data slipping through.
Log-based CDC
Stream inserts, updates, and deletes from MongoDB in near real time from its transaction log - no full reloads and minimal load on production.
High-performance replication
Parallel batch replication moves large MongoDB tables fast, with incremental syncs that transfer only new or changed rows.
Clean, safe, ready to serve
Blend and reshape MongoDB data, then let the Data Quality Firewall stop bad records and automatic PII detection mask sensitive fields before it lands downstream.
Schema-drift handling
When MongoDB changes its columns, Dataddo adapts the pipeline and alerts you instead of breaking the sync.
Proactive monitoring
Data-quality checks and delivery alerts catch gaps before they reach the systems MongoDB feeds.
WITH VS. WITHOUT
Who carries the load when things change
Keep MongoDB data flowing to every downstream system - and see who owns it when a schema, endpoint, or destination changes:
| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| API or auth change | You discover the breakage and scramble to fix it. | We update the connector and restore the pipeline - often before you notice. | Syncs from MongoDB keep flowing |
| Schema drift | Columns change and pipelines break or corrupt data silently. | Detected automatically and handled by configurable rules. | Only clean MongoDB data lands downstream |
| Endpoint deprecated | You re-engineer the integration. | We own the update - the data contract holds. | Your MongoDB pipelines keep working |
| Missing connector | You build and maintain a custom integration. | We build it and maintain it, under a ~4-week SLA. | MongoDB can reach any destination |
| Silent degradation | You find out when a report or model run fails. | Proactive monitoring catches anomalies and delays first. | Issues caught before downstream consumers break |
| Debugging | You dig through logs across disconnected tools. | Run histories, payload inspection, and end-to-end lineage in one place. | Faster root-cause, less downtime |
Data residency by design
Choose where MongoDB is read - cloud, sovereign, or on-prem
Reading from an operational database means touching your most sensitive production data. With Dataddo you decide where the data plane runs per workload - fully in the cloud, in a regional or sovereign cloud, or on-premises next to MongoDB. The control plane orchestrates every option the same way, through metadata only, so payload data never leaves your perimeter.
| Data Plane location | Typical data sensitivity | Why this setup |
|---|---|---|
|
Public cloud
AWS
Microsoft Azure
Google Cloud
|
Low to moderate - general business, marketing, and product data; sources that are already cloud-native. | Fastest to stand up and scales elastically. Best when the data has no residency restriction and often already lives in the same public cloud. |
|
Regional & EU sovereign clouds
EU sovereign clouds
Regional providers
Private cloud
|
Regulated / residency-bound - PII, financial, and health data governed by GDPR or local law. | Keeps processing inside a specific jurisdiction to meet data-residency and sovereignty rules, while still running as managed infrastructure. |
|
On-premises 
Kubernetes
Red Hat OpenShift
VMware Tanzu
|
Highly sensitive / restricted - data that contractually or legally cannot leave the corporate perimeter. | Data never leaves your network. Required for air-gapped, classified, or locked-down environments; the Control Plane still manages it via metadata only. |
DATA TRANSPORT
Every way to serve MongoDB data to other systems
The delivery pattern changes, the platform does not. Extract MongoDB by scheduled batch, replicate it in parallel, or stream changes in real time - all governed the same way.
| Transport type | What it does | Typical destinations | Typical business use cases |
|---|---|---|---|
| ETL & ELT | Classic extract-transform-load, or load-first with in-warehouse transformation. | Load MongoDB tables into your warehouse or lake on a schedule, Consolidate MongoDB with SaaS, ad, and CRM data for analytics | |
| Change Data Capture (CDC) | Real-time, low-latency replication that tracks row-level changes as they happen. | Stream MongoDB changes into your warehouse in near real time, Keep a continuously updated replica of MongoDB for analytics or AI without loading production | |
| Data Streaming | Continuous, event-driven pipelines for time-sensitive and AI-ready data workloads. | Feed events from MongoDB into downstream apps and services, Power real-time dashboards and alerts from MongoDB activity | |
| Reverse ETL | Activate your data: push curated, governed records from your AI agents or warehouse back into CRMs, operational systems, and the frontier apps where your teams act on it. | Activate MongoDB records in the CRMs, ad platforms, and tools your teams run on, Sync curated MongoDB tables into business apps without manual exports | |
| Batch File Delivery | Structured delivery of datasets via files to S3, SFTP, or any storage target. | Export MongoDB tables to S3, SFTP, or cloud storage as Parquet, CSV, or JSON, Deliver scheduled MongoDB extracts to partners and file-based systems |
Kafka
Google Cloud Storage
Azure Blob Storage
SFTP
SYNC METHODS
Pick how MongoDB changes are captured
Match the capture method to each table - from simple timestamp polling to log-based CDC - so you balance freshness, production load, and completeness.
| Method | Captures | Best for |
|---|---|---|
| Timestamp replication | New + updated rows | Frequently edited tables: orders, profiles, inventory |
| Row-sequence replication | New rows (cheapest) | Append-only logs, transactions, events |
| Log-based CDC | New + updated + deleted rows, real-time | High-volume, latency-sensitive tables |
| Custom SQL | You decide | Joins, filters, pre-aggregation before extraction |
Case Studies
See what data teams build with Dataddo
65,000 Social Media Accounts. One Platform. Zero Manual Authorizations.
Beauty & Consumer Goods
How Livesport Activates Data, Saves Engineering Resources with BigQuery and Dataddo
Entertainment
How ID&T Group Activates Data from 1M+ Festival Fans and Dozens of Social Accounts
Entertainment
How Sensire Accelerated Migration of a Proprietary On-Premise Data Infrastructure to the Cloud with Dataddo
Healthcare
How Ringside.ai Builds a Data Product Better and Faster Using Dataddo
Marketing
How Publicis Groupe Brasil Uses Dataddo's API to Scale a Data Product
Advertising
FAQ
MongoDB as a source + Dataddo, answered
Where can I send MongoDB data?
Any of Dataddo's 150+ destinations - cloud warehouses like Snowflake, BigQuery, and Redshift, data lakes, other operational databases, BI tools, and AI or vector stores - plus custom destinations on request.
Do I have to maintain the pipelines?
No. Dataddo is fully managed: we maintain the connectors, adapt to schema changes in MongoDB, and alert you if anything needs attention. Run it fully in the cloud with nothing to operate, or self-host the data plane as a lightweight agent - either way there is no pipeline code for you to maintain.
How does Dataddo read data from MongoDB?
Dataddo reads from your MongoDB collections using change streams or incremental batch queries on the schedule you choose. Change streams deliver document-level changes from the oplog, and incremental syncs move only new or changed documents - so production stays fast. If you run the data plane on-premises, the read stays inside your own network.
Can I capture changes in real time?
Yes, where MongoDB supports it. Log-based CDC reads the transaction log to stream inserts, updates, and deletes with near-instant freshness and minimal load on production. Where CDC is not available, timestamp or row-sequence based incremental syncs keep data current.
How is my data secured?
Dataddo is SOC 2 Type II and ISO 27001 certified. Data is encrypted in transit and at rest, PII can be masked or hashed, and if you run the data plane on-premises, payload data never leaves your network. EU or US data residency is available.
Will this add load to MongoDB?
Log-based CDC reads the transaction log instead of querying tables, and incremental batch syncs move only new or changed rows on a schedule you control - so production stays fast and load stays predictable.
Am I locked in?
No. Pipelines are database-agnostic and destination-agnostic - you can add or switch sources and destinations without rebuilding, and data lands in native formats you own.