MongoDB is great for operational workloads, but running analytical queries directly on your production database is costly and slow. Aggregation pipelines can't match SQL for complex analytics, and connecting BI tools to MongoDB requires additional tooling. Your data team needs the data in BigQuery — but building and maintaining an ETL pipeline takes weeks.
NoSQLSync provides a real-time CDC pipeline from MongoDB to BigQuery. Once connected, every insert, update, and delete in MongoDB streams to BigQuery in seconds. Your data team queries fresh operational data in BigQuery using standard SQL, Looker Studio, Tableau, dbt, and the entire Google Cloud analytics ecosystem — without affecting MongoDB performance.
Key Benefits
Real-time dashboards in Looker Studio
Connect Looker Studio directly to BigQuery tables that are updated in real time from MongoDB. No more stale data or overnight batch jobs.
SQL-based analytics for your data team
Your analysts query MongoDB data using standard SQL in BigQuery — JOINs, window functions, CTEs, and all the features they already know and love.
Zero load on your production MongoDB
CDC reads from the oplog, not the collections. Your production MongoDB experiences zero additional query load from analytics.
Combine MongoDB data with other sources
BigQuery excels at joining data from multiple sources. Combine your MongoDB operational data with Google Analytics, CRM data, or log data for holistic analysis.
ML-ready data
BigQuery ML lets you build and run machine learning models directly on your MongoDB data using SQL. No need to export data to a separate ML platform.
Cost-effective at scale
BigQuery's storage is cheap and query pricing is per-query, not per-instance. Keep years of MongoDB data for trend analysis without breaking the bank.
How to set it up
Frequently asked questions
Data reaches BigQuery within seconds of being written to MongoDB. Our CDC pipeline uses BigQuery's Storage Write API for low-latency streaming inserts.
Nested documents become BigQuery STRUCT columns or JSON columns, depending on your preference. You can also flatten specific nested fields into top-level columns.
Yes. During schema mapping, you can designate timestamp columns for partitioning and frequently-filtered columns for clustering — giving you optimal query performance and cost.
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