Update README.md

README.md

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+# 🕶️ anonyspark
+
+`anonyspark` is a lightweight Python package for schema-driven **data masking and anonymization** in **PySpark DataFrames**. Designed for ML engineers, data analysts, and compliance teams working with sensitive data in big data environments, it helps enforce **data privacy**, **PII redaction**, and **regulatory compliance** (e.g., HIPAA, GDPR).
+
+---
+
+## Motivation
+
+In enterprise data pipelines, personally identifiable information (PII) and sensitive fields are often left exposed in logs, training data, or staging zones. `anonyspark` solves this by enabling **deterministic and schema-aware masking** of such fields **directly in Spark**, without leaving the distributed environment.
+
+---
+
+## Key Features
+
+-  **Schema-driven masking** based on column types or names  
+-  Supports **regex**, **nulling**, **hashing**, or **custom UDF-based** masking  
+-  Designed for **PySpark DataFrames**, not pandas  
+-  Lightweight, dependency-free, and easy to integrate  
+-  CLI-ready for pipeline integration (coming soon)
+
+---
+
+## Use Cases
+
+-  Mask PII fields in ETL pipelines before storage or ML training  
+-  Anonymize user data before model sharing or analytics  
+-  Simulate production-like data in dev/test environments  
+-  Help comply with HIPAA, GDPR, and internal audit policies  
+
+---
+
+##  Installation
+
+```bash
+pip install anonyspark
+
+PyPi link: https://pypi.org/project/anonyspark/
+
+License: MIT License
+
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference