619 feature generate dqx rules from odcs data contracts

docs/dqx/docs/demos.mdx

@@ -13,6 +13,7 @@ Import the following notebooks in the Databricks workspace to try DQX out:
 * [DQX Demo Notebook for Data Quality Summary Metrics](https://github.com/databrickslabs/dqx/blob/v0.10.0/demos/dqx_demo_summary_metrics.py) - demonstrates how to generate summary-level data quality metrics when validating data with DQX.
 * [DQX Demo Notebook for Profiling and Applying Checks at Scale on Multiple Tables](https://github.com/databrickslabs/dqx/blob/v0.10.0/demos/dqx_multi_table_demo.py) - demonstrates how to use DQX as a library at scale to apply checks on multiple tables.
 * [DQX Demo Notebook for AI-assisted checks generation](https://github.com/databrickslabs/dqx/blob/v0.10.0/demos/dqx_demo_ai_assisted_checks_generation.py) - demonstrates how to generate DQX rules/checks with LLM using natural language.
+* [DQX Demo Notebook for Data Contract Integration (ODCS)](https://github.com/databrickslabs/dqx/blob/v0.10.0/demos/dqx_demo_datacontract_odcs.py) - demonstrates how to generate DQX quality rules from ODCS (Open Data Contract Standard) data contracts, including predefined rules from schema constraints, explicit custom rules, and contract metadata tracking.
 * [DQX Demo Notebook for Spark Structured Streaming (Native End-to-End Approach)](https://github.com/databrickslabs/dqx/blob/v0.10.0/demos/dqx_streaming_demo_native.py) - demonstrates how to use DQX as a library with Spark Structured Streaming, using the built-in end-to-end method to handle both reading and writing.
 * [DQX Demo Notebook for Spark Structured Streaming (DIY Approach)](https://github.com/databrickslabs/dqx/blob/v0.10.0/demos/dqx_streaming_demo_diy.py) - demonstrates how to use DQX as a library with Spark Structured Streaming, while handling reading and writing on your own outside DQX using Spark API.
 * [DQX Demo Notebook for Lakeflow Pipelines (formerly DLT)](https://github.com/databrickslabs/dqx/blob/v0.10.0/demos/dqx_dlt_demo.py) - demonstrates how to use DQX as a library with Lakeflow Pipelines.