How to Stream MongoDB Data to ClickHouse in Real Time

MongoDB is trusted for its flexible, document-oriented design. It works well for storing application data, powering APIs, and scaling fast for operational workloads. But when teams try to use MongoDB for analytics—especially across large datasets—they often run into performance limits.

ClickHouse offers a better fit for this kind of workload. It's a high-speed, columnar database built specifically for real-time analytics. With support for large-scale aggregations and complex queries, ClickHouse makes it easy to run fast, insightful analysis, even on billions of rows.

In this guide, you’ll learn how to stream and sync data from MongoDB to ClickHouse using Estuary Flow, a no-code platform for building real-time data pipelines. You'll avoid the complexity of batch ETL and Kafka infrastructure while setting up a reliable, low-latency connection between your operational database and your analytics engine.

Whether you're offloading reporting from MongoDB, building dashboards, or scaling customer-facing analytics, this is your easiest path to get there.

Why Teams Sync MongoDB to ClickHouse for Analytics

MongoDB is a popular choice for building applications quickly. Its flexible schema, fast writes, and document-based structure make it ideal for operational use cases like content management, user profiles, and event tracking. But when teams try to extract insights from that data—especially at scale—they often hit performance and complexity roadblocks.

MongoDB wasn’t designed to be an analytics engine. It can run simple aggregations, but as data volume grows, queries become slower and more resource-intensive. Complex reporting logic, deep filtering, and joins across large collections can lead to timeouts or require expensive cluster scaling. When teams try to power dashboards or run multi-dimensional analysis, MongoDB begins to show its limits.

ClickHouse, on the other hand, was built specifically for that kind of workload. It’s a columnar OLAP database optimized for speed, compression, and high-throughput queries. ClickHouse can scan billions of rows in milliseconds, run advanced aggregations, and serve real-time metrics with minimal overhead. It thrives in scenarios where MongoDB slows down.

By syncing MongoDB data into ClickHouse, teams separate operational and analytical concerns. MongoDB remains the system of record, focused on real-time writes and application logic. ClickHouse becomes the analytics layer, optimized for performance and scale.

This combination enables use cases like:

• Real-time dashboards powered by ClickHouse without overloading MongoDB
• Scalable product or behavioral analytics built on top of operational data
• Offloading costly queries from MongoDB into a purpose-built engine
• Customer-facing analytics that require both speed and reliability

With Estuary Flow, this sync happens continuously and automatically. You can capture changes from MongoDB in real time and deliver them into ClickHouse with low latency—no ETL code or Kafka setup required.

How Estuary Flow Makes the MongoDB to ClickHouse Sync Simple

Setting up a reliable pipeline between MongoDB and ClickHouse isn’t easy if you go the traditional route. You’d need to manage change streams, write your own connector or Kafka integration, build retry logic, and handle schema mismatches manually. For most teams, this means added complexity, infrastructure, and technical debt.

Estuary Flow removes those barriers.

Flow connects directly to your MongoDB cluster and captures data in real time using change streams—MongoDB’s native mechanism for watching inserts, updates, and deletes. It then delivers those changes to ClickHouse as structured, schema-enforced records, ready for analytics.

Here’s what makes Flow ideal for this sync:

• Change Data Capture with no extra tooling - Flow taps directly into MongoDB change streams, capturing live updates as they happen. If your deployment doesn’t support change streams (like time-series collections), Flow can use batch modes with customizable polling.
  
• Automatic backfill and live sync - When you first set up the pipeline, Flow performs a historical load to ensure ClickHouse has a complete dataset. After that, it streams changes continuously, so your analytics stay up to date.
  
• Schema-aware transformation - MongoDB documents are flexible, but analytics tools like ClickHouse require consistent structure. Estuary Flow infers schemas from your data, flattens nested fields where needed, and keeps track of changes to ensure reliability over time.
  
• Kafka-style delivery without Kafka - Flow uses its internal Dekaf system to emit changes as Kafka-compatible topics. These topics can be consumed by ClickHouse using ClickPipes, with no need to deploy or manage Kafka infrastructure.
  
• Resilience and observability - Built-in retries, state tracking, and logs make the pipeline production-ready. Flow automatically resumes from where it left off if interrupted, and helps you monitor pipeline health through the UI or API.

In short, Estuary Flow acts as the glue between your flexible MongoDB collections and your performance-optimized ClickHouse tables. It handles all the moving parts behind the scenes so you can focus on what matters—getting insights from your data, not maintaining the plumbing.

Step-by-Step: Syncing MongoDB to ClickHouse with Estuary Flow

You can build a real-time data sync between MongoDB and ClickHouse in just a few steps using Estuary Flow. No Kafka, no batch scripts, and no complex transformation logic required.

Let’s walk through the process from start to finish.

Step 1: Prepare Your MongoDB Cluster

Before connecting Flow, make sure your MongoDB deployment is ready:

• Enable change streams. If you’re using MongoDB Atlas or a self-managed replica set, ensure change streams are enabled. This allows Flow to capture real-time inserts, updates, and deletes.
• Check user roles. The user account you use to connect should have at least read access to the target database and collections.
• Allow network access. If you’re using a managed cluster, allowlist Estuary’s IP addresses or set up an SSH tunnel so Flow can securely reach your database.
• Optional: Enable changeStreamPreAndPostImages if you want to capture before/after images of document updates or deletes.

Step 2: Connect MongoDB to Flow as a Source

Now that your MongoDB cluster is prepared, you can connect it to Estuary Flow to begin capturing data into real-time collections.

• Visit dashboard.estuary.dev and sign in with your account. This is where you'll set up and manage your pipelines.
  
• From the left-hand sidebar, click on Sources. This is where all your inbound data connections live, including databases, APIs, and streaming sources.
  
• Click + New Source to launch the connector catalog. Search for MongoDB, select it from the list, and click Capture to start configuring your source.
  
• Enter MongoDB connection details:
  • Name: Give your source a meaningful name like mongo_prod_orders or user_events_mongo.
  • Address: Provide the host and port of your MongoDB instance. For Atlas, use the full mongodb+srv://... URI. If your credentials authenticate via the admin DB, be sure to append ?authSource=admin.
  • Username & Password: Enter the database user's credentials with read access to the collections you want to capture.
  • Database: You can optionally specify which databases to include.
• Configure network access (if needed) - If your MongoDB deployment is not accessible over the public internet, scroll down to the Network Tunnel section. Here, you can configure SSH tunneling by entering your SSH host, user, and private key to allow Flow to connect securely.
• After completing the fields, click Next. Flow will test the connection to ensure credentials are valid and that it can access the cluster. If successful, you’ll move on to selecting collections for capture.

Step 3: Choose Collections and Configure Capture Mode

Now you’ll select what data to sync from MongoDB.

• Pick the collections you want to stream. You can choose collections across multiple databases.
• For each collection, select a capture mode:
  • Change Stream Incremental (recommended for real-time sync)
  • Batch Incremental or Batch Snapshot (for collections like views or time-series data)
• If using a batch mode, specify a cursor field (typically _id or a timestamp) that Flow can use to track changes efficiently.

Click Publish to deploy your source. Flow will begin backfilling the selected collections and automatically start streaming new changes as they occur.

Step 4: Set Up ClickHouse as the Destination

1. In the Flow dashboard, go to Destinations and click + New Destination
2. Choose ClickHouse from the connector catalog
3. Click Materialize

Fill in the configuration details:

• Name the materialization (e.g., clickhouse_metrics)
• Choose the same Data Plane region used for your MongoDB source
• Set an Auth Token that ClickHouse will use to authenticate with Flow

Under Source Collections, select the MongoDB-backed collections you captured in Step 3.

Review the field mappings and click Publish.

Step 5: Connect ClickHouse to Flow Using ClickPipes

Now it’s time to consume the data stream inside ClickHouse.

1. In the ClickHouse Cloud dashboard, go to Integrations → ClickPipes
2. Add a new Kafka Pipe with these connection settings:
   • Broker: dekaf.estuary-data.com:9092
   • Protocol: SASL_SSL
   • SASL Mechanism: PLAIN
   • Username: Your full Flow materialization name (e.g., your-org/clickhouse_metrics)
   • Password: The Auth Token from Step 4
   • Schema Registry URL: https://dekaf.estuary-data.com
3. Select the topics that match your Flow collections
4. Map each topic to your ClickHouse schema and save the pipe

ClickHouse will now start ingesting your MongoDB data in real time.

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Conclusion

MongoDB is excellent for fast writes and flexible document storage, but it’s not built to handle large-scale analytics or reporting workloads efficiently. ClickHouse fills that gap with a columnar engine designed for speed, scale, and complex queries across massive datasets.

By syncing MongoDB to ClickHouse using Estuary Flow, you get the best of both systems. Your operational workloads remain fast and responsive in MongoDB, while ClickHouse takes care of aggregations, dashboards, and real-time insights, without the need to manage Kafka, write custom pipelines, or deal with brittle batch jobs.

Flow makes it easy to connect the two with a fully managed, low-latency pipeline that captures changes as they happen. It’s a scalable, production-ready solution that frees your team from data plumbing and unlocks deeper, faster analytics.

If your team relies on MongoDB but is struggling with slow queries or limited visibility, this integration is your path forward.

Next Steps: Start Streaming from MongoDB to ClickHouse

If you're ready to unlock real-time analytics on your MongoDB data, Estuary Flow gives you the easiest and most reliable way to get there.

• Create your Estuary Flow account
  Set up your first MongoDB to ClickHouse pipeline in just a few clicks—no Kafka, no custom scripts. Get started with Flow
• Explore step-by-step tutorials
  Learn how Flow works, how to handle nested fields, and how to customize your data streams. View documentation
• Join the Estuary Slack community
  Connect with other engineers, ask questions, and get support from the Estuary team. Join our Slack
• Talk to us about your architecture
  Need help with secure deployments, private networking, or scaling? Our team is here to help. Contact Estuary