Capabilities
AI-powered ETL and data preparation platform with a visual pipeline builder, 250+ transformation functions, Ask Zia for natural language pipeline creation, 70+ source connectors, ML-powered sentiment analysis, keyword extraction, data quality monitoring, automated retries, and a Python Code Studio (2026 release). Designed for analysts, business users, data engineers, and scientists. HIPAA and GDPR compliant. ([Zoho DataPrep](https://www.zoho.com/dataprep/))
Key integrations
Zoho Analytics, Zoho CRM, Zoho Books, AWS S3, Google Cloud Storage, Snowflake, Databricks, MySQL, REST APIs
Platform availability
The table below records native platform support as of April 2026, per the master reference. A denotes an officially shipped native app or supported surface; a denotes that the platform is either unsupported or that status is not disclosed.
| Platform | Native app |
|---|---|
| Web | |
| iOS | |
| Android | |
| iPadOS | |
| Android Tablet | |
| macOS | |
| Windows |
Source note
[Zoho DataPrep](https://www.zoho.com/dataprep/); cloud-based tool.
Where Zoho DataPrep shows up in the rest of the Encyclopedia
The encyclopedia indexes references to Zoho DataPrep across the 112 chapters and 681-entry feature atlas. Jump to the full passage for context.
- Data warehouse export patternsDeveloper, extension, and integration platformcrm
Data warehouse export patterns are architectures for moving CRM data into analytics warehouses or lakes through scheduled exports, APIs, ETL, Zoho DataPrep, or BI connectors while preserving keys and history. The reason it matters in Developer, extension, and integration platform is that it shapes API contracts, custom UI, extension lifecycle, sandbox testing, and the boundary between supported configuration and custom engineering. A mature implementation defines when the feature is used, who owns it, and what evidence proves it is working. For adoption, document Data warehouse export patterns in the words users use, not only the words administrators use. Train the relevant roles on what changes for them, then require a backup, sample run, and rollback path before bulk or destructive use; additionally, add an acceptance test and a monitoring or review mechanism.