---
schema_version: "1"
id: 76af0ddb70b200f336c473d54c0adb9f56c73cd6bca110bd186b30be57803adf
title: Build and Share Functionality
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/release-notes/262-0-0/rn_data_build.md
canonical_url: https://help.salesforce.com/s/articleView?id=release-notes.rn_data_build.htm&release=262.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 7e90b08a324e505886df84f7f5dd91e1aeaed65cc8d9a5ff8ecdbb7fe114cb71
description: Extend the use of Data 360 by building a sharing functionality using data kits and packages.
products:
  - Cross Cloud
release: "262"
---
# Build and Share Functionality

Extend the use of Data 360 by building a sharing functionality using data kits and packages.

- **[Make Predictive Model Calls from Code Extension Batch Transform Scripts](https://help.salesforce.com/s/articleView?id=release-notes.rn_cdp_2026_summer_batch_transform_predictive_code_extension.htm&release=262.0.0&type=5)**  
    Score, classify, or rank records during a batch data transform. Pass structured feature columns from a data lake object (DLO) or data model object (DMO) to a configured predictive model in AI Models (formerly Einstein Studio) and write predictions to a target DLO or DMO. Code extension scripts already support generative AI model calls.
- **[Access Code Extension from Data Cloud Setup](https://help.salesforce.com/s/articleView?id=release-notes.rn_cdp_2026_summer_code_extension_data_cloud_setup.htm&release=262.0.0&type=5)**  
    Code Extension is now part of Data Cloud Setup, under Developer Tools. Previously, it was a separate tab in Data 360.
- **[Extend Data 360 by Using Custom Code with Code Extension](https://help.salesforce.com/s/articleView?id=release-notes.rn_cdp_2026_summer_code_extension.htm&release=262.0.0&type=5)**  
    With code extension, you can bring your custom Python code into Data 360 to extend Data 360's native capabilities. If native Data 360 features don't meet your business requirements, deploy your custom logic across supported Data 360 features such as batch data transforms. For example, if you need advanced data transformations for your business use case, you can deploy custom business logic to Data 360 to implement your requirements. Salesforce CLI with the Code Extension plugin and the Data Custom Code Python SDK work together as your local toolchain for Code Extension.
- **[Enhance Search Index Chunking in Data 360 by Using Code Extension](https://help.salesforce.com/s/articleView?id=release-notes.rn_cdp_2026_summer_search_index_chunking_code_extension.htm&release=262.0.0&type=5)**  
    Improve how Data 360 search indexes handle unstructured data by using a custom Python chunking function. Write, test, package, and deploy your function by using code extension.
- **[Create and Deploy Code Extensions Using Natural Language with Agentforce Vibes](https://help.salesforce.com/s/articleView?id=release-notes.rn_cdp_2026_summer_vibes_code_extension.htm&release=262.0.0&type=5)**  
    Author, debug, and deploy Data 360 code extensions by using Agentforce Vibes. Use natural-language prompts in either Agentforce Vibes IDE or local IDEs, such as VS Code and Cursor, where the Agentforce Vibes extension is enabled.
- **[Make Generative AI Calls from Code Extension Batch Transform Scripts](https://help.salesforce.com/s/articleView?id=release-notes.rn_cdp_2026_summer_batch_transform_genai_code_extension.htm&release=262.0.0&type=5)**  
    Generate text during a batch data transform to summarize, classify, or enhance records at scale. Send prompts and data from a data lake object (DLO) or data model object (DMO) to a configured generative AI model, then write the generated content to a target DLO or DMO. Code extension functions already support generative AI calls.
- **[Accelerate Development by Cloning Data 360 Sandboxes](https://help.salesforce.com/s/articleView?id=release-notes.rn_cdp_2026_summer_accelerate_development_data360_sandbox.htm&release=262.0.0&type=5)**  
    Replicate your existing Data 360 configurations, mappings, and metadata into a new environment without manual rework. Instead of refreshing from production, clone a sandbox where you provisioned Data 360. Use the cloned sandbox for testing or training so that your teams can work with consistent data models while avoiding repetitive setup tasks.