MongoDB Atlas

Operational Database

Every source into MongoDB Atlas, in sync and in your control.

Dataddo is the turnkey data layer for MongoDB Atlas. Connect 400+ business sources, keep clean, governed collections in sync with no pipelines to build or maintain, and deploy the data plane in the cloud, a sovereign region, or on-premises so your data stays where it belongs. Fully managed and database-agnostic - no lock-in.

Start Free

Book a Demo Read the docs

ARCHITECTURE

Where MongoDB Atlas fits in your data architecture

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

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 DATA FOUNDATION

Every source into MongoDB Atlas, clean and current

Connect 400+ sources and keep MongoDB Atlas continuously fed with clean, governed data - no pipelines to build, and no bad or broken data slipping through.

400+ managed connectors

Marketing, sales, finance, product, and ad platforms - plus other databases and flat files - all maintained for you.

ETL, ELT, CDC, streaming

Load MongoDB Atlas however the workload demands - batch ETL or ELT, real-time CDC from your source databases, or continuous event streaming. One platform, every transport pattern.

Write modes that hit SLAs

Insert, upsert, truncate-insert, or delete - pick the write mode per table. Incremental loads and fast, reliable delivery keep MongoDB Atlas current and meet your data SLAs.

Clean, safe, ready to serve

Blend and reshape sources, then let the Data Quality Firewall stop bad records and automatic PII detection mask sensitive fields - so only trustworthy data lands in MongoDB Atlas.

Schema-drift handling

When a source changes its fields, Dataddo adapts the pipeline and alerts you instead of breaking the load into MongoDB Atlas.

Proactive monitoring

Data-quality checks and delivery alerts catch gaps before they reach MongoDB Atlas or the applications it powers.

WITH VS. WITHOUT

Who carries the load when things change upstream

Keep the same sources flowing into MongoDB Atlas - and see who owns it when an API, schema, or endpoint 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.	Pipelines to MongoDB Atlas keep flowing
Schema drift	Columns change and pipelines break or corrupt data silently.	Detected automatically and handled by configurable rules.	Only clean data lands in MongoDB Atlas
Endpoint deprecated	You re-engineer the integration.	We own the update - the data contract holds.	Your MongoDB Atlas loads keep working
Missing connector	You build and maintain a custom integration.	We build it and maintain it, under a ~4-week SLA.	Any source can reach MongoDB Atlas
Silent degradation	You find out when a report or model run fails.	Proactive monitoring catches anomalies and delays first.	Issues caught before MongoDB Atlas 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 Atlas is loaded - cloud, sovereign, or on-prem

Loading an operational database means moving your most sensitive 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 inside your own perimeter. The control plane orchestrates every option the same way, through metadata only, so MongoDB Atlas stays wherever compliance and latency require.

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 move data into and out of MongoDB Atlas

The delivery pattern changes, the platform does not. Load MongoDB Atlas by scheduled batch or in real time, replicate other databases into it, and activate its data back out - all governed the same way.

Transport type	What it does	Typical connectors	Typical business use cases
ETL & ELT	Classic extract-transform-load, or load-first with in-warehouse transformation.	Salesforce Google Analytics 4 HubSpot Snowflake BigQuery + 400 more	Consolidate SaaS, ad, and CRM data into MongoDB Atlas as an operational data store, Load raw data into MongoDB Atlas and transform it in place with SQL
Change Data Capture (CDC)	Real-time, low-latency replication that tracks row-level changes as they happen.	PostgreSQL MySQL SQL Server Oracle MongoDB + 400 more	Replicate production databases into MongoDB Atlas in near real time, Keep MongoDB Atlas in sync with other operational systems without full reloads
Data Streaming	Continuous, event-driven pipelines for time-sensitive and AI-ready data workloads.	Kafka Azure Event Hub + 400 more	Stream events and webhooks into MongoDB Atlas for always-current data, Feed applications and services from MongoDB Atlas with the latest 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.	Salesforce HubSpot Google Ads Marketo + 400 more	Write modeled metrics and segments from your warehouse back into MongoDB Atlas, Sync MongoDB Atlas with the CRMs, ad platforms, and apps your teams run on

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 Atlas + Dataddo, answered

What can I load into MongoDB Atlas?

Any of Dataddo's 400+ sources - marketing and ad platforms, CRMs, finance tools, other databases, and flat files - plus custom sources on request. Data is blended and cleansed before it lands in MongoDB Atlas.

Do I have to maintain the pipelines?

No. Dataddo is fully managed: we maintain the connectors, adapt to source schema changes, 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 connect to MongoDB Atlas?

With a connection string (DSN) in MongoDB's standard format and a database user, targeting a specific database. The instance must be reachable by Dataddo; an SSH tunnel is available and Dataddo IPs must be allowed behind a firewall.

Does Dataddo write to tables or collections in MongoDB Atlas?

Collections, not tables. The user needs rights to create collections and to insert, update and delete documents; collections are created on the first write following MongoDB's naming rules.

Can Dataddo update documents in MongoDB Atlas or only insert?

Seven write modes are available (insert, insert_ignore, truncate_insert, upsert, update, update_ignore, delete). All except insert and truncate_insert match documents on a composite key of up to four fields.

Can I sync data both into and out of MongoDB Atlas?

Yes. Dataddo loads MongoDB Atlas from 400+ sources and also does reverse ETL - pushing modeled data from your warehouse or other systems back into MongoDB Atlas, and from MongoDB Atlas into CRMs, ad platforms, and operational apps.

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 Atlas?

Incremental loads write only new or changed rows, and you control the schedule - so you move only the data you actually need and keep write load on MongoDB Atlas predictable.

Am I locked in?

No. Data lands in native MongoDB Atlas tables you own, and pipelines are database-agnostic - you can add or switch databases and destinations without rebuilding your sources.