Stream data from MongoDB to Google Bigquery
Move data from MongoDB to Google Bigquery in minutes using Estuary. Stream, batch, or continuously sync data with control over latency from sub-second to batch.
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- 200+Of connectors
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- <100msEnd-to-end latency
- 7+GB/secSingle dataflow
How to integrate MongoDB with Google Bigquery in 3 simple steps
Connect MongoDB as your data source
Set up a source connector for MongoDB in minutes. Estuary supports streaming (including CDC where available) and batch data capture through events, incremental syncs, or snapshots — without custom pipelines, agents, or manual configuration.
Configure Google Bigquery as your destination connector
Estuary supports intelligent schema handling, with schema inference and evolution tools that help align source and destination structures over time. It supports both batch and streaming data movement, reliably delivering data to Google Bigquery.
Deploy and Monitor Your End-to-End Data Pipeline
Launch your pipeline and monitor it from a single UI. Estuary guarantees exactly-once delivery, handles backfills and replays, and scales with your data — without engineering overhead.
MongoDB connector details
The Estuary MongoDB connector captures data from MongoDB collections into Estuary in real time using change streams. It continuously streams inserts, updates, and deletes as CDC events while performing an initial snapshot for completeness.
- Streams real-time CDC events via MongoDB change streams
- Supports batch modes (snapshot or incremental) for non-streaming collections
- Performs a live backfill while reading ongoing updates
- Compatible with MongoDB Atlas, self-hosted clusters, and DocumentDB / Cosmos DB variants
- Enables secure SSH tunneling for private network connectivity
See how Xometry uses MongoDB
Google Bigquery connector details
Estuary’s Google BigQuery materialization connector writes Estuary collections into tables within a BigQuery dataset for scalable analytics and near real-time reporting. The connector stages data through Google Cloud Storage, then applies standard merges or high-speed delta updates so BigQuery stays current as upstream data changes.
- Materialize Estuary collections into BigQuery tables within a selected BigQuery dataset.
- Use a Google Cloud Storage bucket as a temporary staging area for reliable delivery into BigQuery.
- Create the staging bucket in the same region as the destination BigQuery dataset.
- Support both standard merge mode and delta update mode for performance and update flexibility.
- Use a Google Cloud service account with access to the BigQuery dataset, BigQuery jobs, BigQuery read sessions, and the staging GCS bucket.
- Enable clustering by primary keys where appropriate to improve query performance on materialized tables.
See how Cosuno uses Google Bigquery
Estuary in action
See how to build end-to-end pipelines using no-code connectors in minutes. Estuary does the rest.
Success stories
Spend 2-5x less
Estuary customers not only do 4x more. They also spend 2-5x less on ETL and ELT. Estuary's unique ability to mix and match streaming and batch loading has also helped customers save as much as 40% on data warehouse compute costs.
MongoDB to Google Bigquery pricing estimate
Estimated monthly cost to move 800 GB from MongoDB to Google Bigquery is approximately $1,000.
Data moved
Choose how much data you want to move from MongoDB to Google Bigquery each month.
GB
Choose number of sources and destinations.
Why pay more?
Move the same data for a fraction of the cost.
What customers are saying
![Xometry avatar]()
![Xometry avatar]()
“Estuary has been a pleasure to work with and has significantly modernized our data infrastructure, delivering real-time and scalable processes that will significantly impact company-wide operations. Every data-driven organization should be looking at Estuary today.”
Read the Success Story![Recart avatar]()
![Recart avatar]()
Estuary became our real-time data backbone without the cost or complexity of traditional solutions. We replaced a fragile, high-maintenance pipeline with a managed system that just works and scales.
Read the Success Story![Cosuno avatar]()
![Cosuno avatar]()
Estuary just works. We’ve never had an incident, and it cut our data movement costs in half.
Read the Success Story
We're a big fan of Estuary's real-time, no code model. It's magic that we're getting real time data without much effort and we don't have to spend time thinking about broken pipelines. We've also experienced fantastic support by Estuary.
Getting started with Estuary
Free account
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Sign upDocs
Make sure you read through the documentation, especially the get started section.
Learn moreCommunity
Join the Slack community for the easiest way to get support while getting started.
Join Slack CommunityEstuary 101
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Frequently Asked Questions
Is this integration suitable for production workloads?
Yes. Estuary pipelines are designed for production use, with exactly-once delivery semantics, automated backfills, and continuous operation at scale.
Can I control where my data runs and is processed?
Yes. Estuary offers multiple deployment options, including fully managed SaaS, private deployments, and bring-your-own-cloud (BYOC). This allows teams to control where their data plane runs and meet security, compliance, and networking requirements. Learn more about Estuary's security and deployment options.
Can I build this MongoDB to Google Bigquery integration manually?
Yes, it's possible to build a manual pipeline using custom scripts, scheduled jobs, or open-source tools. However, manual approaches typically require ongoing maintenance, custom error handling, schema management, and operational overhead. Estuary simplifies this by providing a managed pipeline with built-in reliability, scaling, and monitoring.
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DataOps made simple
Add advanced capabilities like schema inference and evolution with a few clicks. Or automate your data pipeline and integrate into your existing DataOps using Estuary's rich CLI.
