Update README.md

Author: BohuTANG

README.md

@@ -1,18 +1,16 @@
 <h1 align="center">Databend</h1>
-<h2 align="center">ANY DATA. ANY SCALE. ONE DATABASE.</h2>
-<h3 align="center">Blazing analytics, fast search, geo insights, vector AI — supercharged in a new-era Snowflake-compatible warehouse</h3>
+<h3 align="center">Unified Multimodal Database for Any Data at Any Scale.</h3>
+<p align="center">A <strong>next-generation</strong> cloud-native warehouse built in <strong>Rust</strong>. Open-source, Snowflake-compatible, and unifying BI, AI, Search, Geo, and Stream.</p>
 
 <div align="center">
 
 <a href="https://databend.com/">☁️ Try Cloud</a> •
-<a href="#quick-start">🚀 Quick Start</a> •
-<a href="https://docs.databend.com/">📖 Documentation</a>
+<a href="#-quick-start">🚀 Quick Start</a> •
+<a href="https://docs.databend.com/">📖 Documentation</a> •
+<a href="https://link.databend.com/join-slack">💬 Slack</a>
 
 <br><br>
 
-<a href="https://link.databend.com/join-slack">
-<img src="https://img.shields.io/badge/slack-databend-0abd59?logo=slack" alt="slack" />
-</a>
 <a href="https://github.com/databendlabs/databend/actions/workflows/release.yml">
 <img src="https://img.shields.io/github/actions/workflow/status/datafuselabs/databend/release.yml?branch=main" alt="CI Status" />
 </a>
@@ -22,96 +20,67 @@
 
 <br>
 
-<img src="https://github.com/user-attachments/assets/4c288d5c-9365-44f7-8cde-b2c7ebe15622" alt="databend" />
-
-## Why Databend?
-
-Databend has expanded from analytics into a unified multimodal database: **one Snowflake-compatible SQL surface for BI, AI, search, and geospatial workloads.**
-
-**Unified Engine**: Analytics, vector, full-text, and geospatial share the same optimizer and elastic runtime.
-
-**Unified Data**: Structured, semi-structured, vector, and unstructured live directly on object stores with indexes, caching, transactions, MVCC branching.
-
-**Analytics Native**: ANSI SQL, windowing, incremental aggregates, and streaming ingestion deliver BI without moving data.
-
-**Vector Native**: Built-in embeddings, vector indexes, and semantic retrieval exposed through SQL and SDKs.
-
-**Search Native**: JSON full-text indexing, structured filters, and ranking to power hybrid search experiences.
-
-**Unified Deployment**: Cloud, self-hosted, or `pip install databend` all run the same engine on shared object storage.
-
-**Rust Performance**: Vectorized Rust execution with separated storage keeps performance high and compute spend lean.
+<img src="https://github.com/user-attachments/assets/4c288d5c-9365-44f7-8cde-b2c7ebe15622" alt="databend" width="100%" />
 
-**Enterprise Scale**: Fine-grained governance, masking, auditing, and production deployments exceeding 800+ PB and 100M+ daily queries.
+## 💡 Why Databend?
 
-## Benchmarks
+Databend is an open-source **unified multimodal database** built in Rust. It empowers **Analytics**, **AI**, **Search**, and **Geo** workloads on a single platform directly from object storage.
 
-**Performance**: [TPC-H vs Snowflake](https://docs.databend.com/guides/benchmark/tpch) | [ClickBench Results](https://www.databend.com/blog/category-product/clickbench-databend-top)
-**Cost**: [90% Cost Reduction](https://docs.databend.com/guides/benchmark/data-ingest)
+- **Unified Engine**: One optimizer and runtime for all data types (Structured, Semi-structured, Vector).
+- **Native Pipelines**: Built-in **Stream** and **Task** for automated data cleaning and transformation.
+- **Cloud Native**: Stateless compute nodes over object storage (S3, GCS, Azure) with full ACID support.
+- **High Performance**: Vectorized execution and Zero-Copy processing.
 
+## ⚡ Quick Start
 
-![Databend Architecture](https://github.com/user-attachments/assets/288dea8d-0243-4c45-8d18-d4d402b08075)
+### 1. Cloud (Recommended)
+[Start for free on Databend Cloud](https://docs.databend.com/guides/cloud/) - Production-ready in 60 seconds.
 
+### 2. Local (Python)
+Ideal for development and testing:
 
-## Use Cases
-
-- **SQL Analytics**: ANSI joins, window functions, incremental aggregates, and streaming ingestion for BI workloads.
-- **AI Vector**: Persist embeddings alongside facts, index vectors, and run semantic retrieval for RAG and agent pipelines.
-- **JSON Search**: Mix full-text search over JSON, metadata filters, and semantic similarity inside one query plan.
-- **Geo Analytics**: Run distance, containment, and hex-grid analytics to power maps and mobility scenarios.
-- **Lakehouse ETL**: Query Parquet, CSV, and NDJSON in object storage, transform streaming updates, and load them into managed tables.
-
-## Quick Start
-
-### Option 1: Databend Cloud Warehouse (Recommended)
-[Start with Databend Cloud](https://docs.databend.com/guides/cloud/) - Serverless warehouse clusters, production-ready in 60 seconds
-
-### Option 2: Local Development with Python
 ```bash
 pip install databend
 ```
 
 ```python
 import databend
-
 ctx = databend.SessionContext()
-
-# Local table for quick testing
-ctx.sql("CREATE TABLE products (id INT, name STRING, price FLOAT)").collect()
-ctx.sql("INSERT INTO products VALUES (1, 'Laptop', 1299.99), (2, 'Phone', 899.50)").collect()
-ctx.sql("SELECT * FROM products").show()
-
-# S3 remote table (same as cloud warehouse)
-ctx.create_s3_connection("s3", "your_key", "your_secret")
-ctx.sql("CREATE TABLE sales (id INT, revenue FLOAT) 's3://bucket/sales/' CONNECTION=(connection_name='s3')").collect()
-ctx.sql("SELECT COUNT(*) FROM sales").show()
+ctx.sql("SELECT 'Hello, Databend!'").show()
 ```
 
-### Option 3: Docker (Self-Host Experience)
+### 3. Docker
+Run the full warehouse locally:
+
 ```bash
 docker run -p 8000:8000 datafuselabs/databend
 ```
-Experience the full warehouse capabilities locally - same features as cloud clusters.
 
-## Community
+## 🚀 Use Cases
+
+- **BI & Analytics**: High-speed SQL on massive datasets. See [Query Processing](https://docs.databend.com/guides/query/query-processing).
+- **AI & Vectors**: Built-in vector search and embedding management. See [Vector Database](https://docs.databend.com/guides/query/vector-db).
+- **Full-Text Search**: Fast indexing and retrieval on text and semi-structured data (JSON). See [Full-Text Index](https://docs.databend.com/guides/query/full-text).
+- **Geospatial**: Advanced geo-analytics and mapping. See [Geospatial Analysis](https://docs.databend.com/guides/query/geospatial).
+- **Stream & Task**: Continuous data ingestion and transformation. See [Lakehouse ETL](https://docs.databend.com/guides/query/lakehouse-etl).
+
+## 🤝 Community & Support
 
-- [📖 Documentation](https://docs.databend.com/) - Complete guides and references
-- [💬 Slack](https://link.databend.com/join-slack) - Live community discussion
-- [🐛 GitHub Issues](https://github.com/databendlabs/databend/issues) - Bug reports and feature requests
-- [🎯 Good First Issues](https://link.databend.com/i-m-feeling-lucky) - Start contributing today
+- [📖 Documentation](https://docs.databend.com/)
+- [💬 Join Slack](https://link.databend.com/join-slack)
+- [🐛 Issue Tracker](https://github.com/databendlabs/databend/issues)
+- [🗺️ Roadmap](https://github.com/databendlabs/databend/issues/14167)
 
-**Contributors get immortalized in `system.contributors` table! 🏆**
+**Contributors are immortalized in the `system.contributors` table! 🏆**
 
 ## 📄 License
 
-[Apache License 2.0](licenses/Apache-2.0.txt) + [Elastic License 2.0](licenses/Elastic.txt)
-[Licensing FAQs](https://docs.databend.com/guides/products/dee/license)
+[Apache 2.0](licenses/Apache-2.0.txt) + [Elastic 2.0](licenses/Elastic.txt) | [Licensing FAQ](https://docs.databend.com/guides/products/dee/license)
 
 ---
 
 <div align="center">
 <strong>Built by engineers who redefine what's possible with data</strong><br>
 <a href="https://databend.com">🌐 Website</a> •
-<a href="https://x.com/DatabendLabs">🐦 Twitter</a> •
-<a href="https://github.com/databendlabs/databend/issues/14167">🗺️ Roadmap</a>
+<a href="https://x.com/DatabendLabs">🐦 Twitter</a>
 </div>