Sync OneLake Data to Mode in Minutes
Load data into Microsoft OneLake to power your Fabric analytics, data science projects, and real-time business intelligence. Unify all your enterprise data with OneLake as the single data foundation.
Mode is an analytics platform that helps data science teams and domain specialists collaborate to solve problems and make more strategic decisions.
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Connect to PostgreSQL databases for real-time data replication.
Move files securely to and from SFTP servers.
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Connect to custom REST API endpoints with flexible source and destination support.
Load and extract files from Amazon S3 buckets.
Replicate MongoDB collections with real-time change data capture.
Connect Oracle databases to your warehouse, lakehouse, and operational stack.
Integrate Microsoft Dynamics 365 CRM and ERP data.
Move IBM Db2 database data into the systems your teams rely on.
Read from and write to Google Sheets as a source or destination.
Load and extract files from Azure Blob Storage containers.
Connect OneLake to Mode and 200+ other platforms in minutes.
Talk to an expertFAQ
Clear answers to the questions teams ask when evaluating Integrate.io.
Still have questions?
Talk to an expert →Yes. Integrate.io helps teams build managed pipelines that move OneLake data into Mode for analytics, operations, and reporting workflows.
The available OneLake data depends on the connector, authentication, API permissions, and objects selected. Integrate.io helps map that data into Mode fields and tables.
Yes. Integrate.io supports mapping, filtering, joins, enrichment, scheduling, monitoring, and error handling before OneLake data reaches Mode.
Refresh timing depends on source limits, destination capacity, data volume, and business requirements. Teams can configure schedules that keep Mode updated from OneLake.
Most OneLake to Mode pipelines can be configured visually in Integrate.io. Teams can add advanced logic when the integration requires API-specific handling or custom transformations.
Start with a scoped OneLake sync, confirm field mapping and row counts in Mode, review pipeline logs, then schedule the production workflow once the data matches expectations.
