# Integrating Data from Cloud Platforms with AI Laptop Workflows

**URL:** <https://community.cdata.com/t/integrating-data-from-cloud-platforms-with-ai-laptop-workflows/982>\
**Category:** CData Connect Cloud\
**Tags:** discussion, cdata-connect-cloud\
**Created:** [December 30, 2024, 6:27am UTC](https://community.cdata.com/t/integrating-data-from-cloud-platforms-with-ai-laptop-workflows/982 "2024-12-30T06:27:26Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Olivia\_Barn](https://avatars.discourse-cdn.com/v4/letter/o/d78d45/32.png) [@Olivia\_Barn](https://community.cdata.com/u/Olivia_Barn)\
**Post date:** [December 30, 2024, 6:27am UTC](https://community.cdata.com/t/integrating-data-from-cloud-platforms-with-ai-laptop-workflows/982/1 "2024-12-30T06:27:28Z")

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Hi everyone,

I am currently working on a project to integrate data from various cloud platforms into workflows on an **[AI laptop](https://www.lenovo.com/us/en/lenovoauraedition/)**. My goal is to create seamless data pipelines using CData’s ODBC and JDBC drivers, but I’m encountering some challenges with performance, particularly in managing large data sets efficiently.

Has anyone here worked with CData connectors in AI laptop environments? Any configuration tips or best practices you can recommend?

Looking forward to your suggestions!