refine introduction (#397)

Author: chenziliang

docs/index.mdx

@@ -3,20 +3,18 @@
 import Quickstart from '@site/src/components/Quickstart'
 import Grid, { GridItem } from '@site/src/components/Grid'
 
-Timeplus is a simple, powerful, and cost-efficient stream processing platform.
+**Timeplus** is a simple, powerful, and cost-efficient vectorized stream processing platform built in modern C++. Its core processing engine is a **single binary with no external dependencies**, making it easy to operate in production — a key differentiator compared to other solutions.  
 
-Timeplus provides powerful end-to-end capabilities to help data teams process streaming and historical data quickly and intuitively, accessible for organizations of all sizes and industries. It enables data engineers and platform engineers to unlock streaming data value using SQL.
+Timeplus provides end-to-end capabilities that help data teams process **both streaming and historical data** quickly and intuitively. It’s designed to be accessible for organizations of all sizes and industries, enabling data engineers and platform teams to unlock the value of streaming data using **streaming SQL**.  
 
-It's available in different ways:
+Timeplus is available in multiple editions:
 
-- [Timeplus Proton](/proton): the core engine open-sourced at [GitHub](https://github.com/timeplus-io/proton) under Apache 2.0 License. It is a fast and lightweight streaming SQL engine.
-- [Timeplus Enterprise](https://www.timeplus.com/product): the production-ready commercial product, able to run and scale everywhere, from edge to your data center. Customize according to your configuration needs.
+- **[Timeplus Proton](/proton)** – the open-source core engine, available on [GitHub](https://github.com/timeplus-io/proton) under the Apache 2.0 License. It is a **fast, lightweight streaming SQL engine** ideal for building streaming pipelines.  
+- **[Timeplus Enterprise](https://www.timeplus.com/product)** – the production-ready commercial product, designed to run and scale anywhere, from the edge to your data center, and customizable to your specific configuration needs.  
 
-![Timeplus High Level](/img/high_level_diagram.png)
+Timeplus makes it easy to connect to diverse data sources—including **Apache Kafka, Confluent Cloud, Redpanda, NATS, WebSocket/SSE, CSV uploads**, and more. Users can explore streaming patterns with SQL queries, send real-time insights and alerts to other systems or individuals, and build dashboards and visualizations to monitor and analyze data in real time.  
 
-Timeplus allows for easy connection to diverse data sources (such as Apache Kafka, Confluent Cloud, Redpanda, NATS, Web Socket/SSE, CSV file upload, and more), explore streaming patterns via SQL queries, send real-time insights and alerts to other systems or individuals, and create dashboards and visualizations.
-
-Still curious about [the benefits of using Timeplus](/why-timeplus)? Check out the [showcases](/showcases) to see how Timeplus customers use our unified streaming and historical processing platform.
+Still curious about [the benefits of using Timeplus](/why-timeplus)? Explore our [showcases](/showcases) to see how customers leverage our unified streaming and historical processing platform, and dive into the [architecture](/architecture) and  [cluster](/cluster) design to learn more about the technical internals and differentiators.
 
 ## Get started with Timeplus
 
@@ -42,7 +40,7 @@ Still curious about [the benefits of using Timeplus](/why-timeplus)? Check out t
   <GridItem href="/query-syntax">
     <h3>Write SQL queries &rarr;</h3>
     <p>
-    Create long-running queries using functions like transformations, joins, aggregation, windowed processing, substreams, and more.
+    Run streaming query by using functions like transformations, joins, aggregation, windowed processing, substreams, and more.
     </p>
 
   </GridItem>
@@ -60,21 +58,21 @@ Still curious about [the benefits of using Timeplus](/why-timeplus)? Check out t
   <GridItem href="/working-with-streams">
     <h3>Streams &rarr;</h3>
     <p>
-    Append-only streams, mutable streams, external streams to read or write data from other systems.
+    Append-only streams, mutable streams, external streams and external tables to read or write data from other systems.
     </p>
 
   </GridItem>
   <GridItem href="/view#m_view">
     <h3>Materialized Views &rarr;</h3>
     <p>
-    Long-running streaming SQL in background after creation. The results can be written to internal storage or external systems for ETL or alerting.
+    Data streaming processing pipeline via streaming SQL. The results can be written to native Timeplus stream or external systems.
     </p>
 
   </GridItem>
   <GridItem href="/functions">
     <h3>Functions &rarr;</h3>
     <p>
-    Thousand of built-in SQL functions are available in Timeplus. You can also define own functions (UDF) with JavaScript, Python,or any other languages.
+    Thousand of built-in SQL functions are available in Timeplus. You can also define own functions (UDF) with JavaScript, Python, SQL.
     </p>
 
   </GridItem>

docs/why-timeplus.md

@@ -1,14 +1,16 @@
-# What is Timeplus
+# Why Timeplus
 
-Timeplus is a unified real-time data processing platform built for developers who need to move, transform, and act on data fast. At the heart of Timeplus is an incremental processing engine that uses modern vectorization (SIMD), just-in-time (JIT) compilation, and advanced database internals to ingest, transform, store, and serve data with low latency and high throughput.
+## Overview
+
+**Timeplus** is a unified real-time data processing platform built for developers who need to move, transform, and act on data fast. At the heart of Timeplus is an incremental processing engine that uses modern vectorization (SIMD), just-in-time (JIT) compilation, and advanced database internals to ingest, transform, store, and serve data with low latency and high throughput.
 
 It plugs right into the tools you already use — stream sources like Kafka, Redpanda, and Pulsar, and sinks like ClickHouse, Apache Iceberg, S3, Splunk, Elasticsearch, and MongoDB. You can easily build pipelines that consume events, run streaming ETL, joins, aggregations, filtering, and other transformations, then push the results wherever they need to go — fast.
 
 Timeplus isn’t just for streaming. It also supports scheduled batch jobs, so you can mix real-time and periodic workloads in one place. Pair it with Timeplus Alert to trigger actions on live data and manage the full lifecycle of your data applications—without duct-taping multiple tools together.
 
-## Why Timeplus
+## Key Differentiations
 
-Timeplus simplifies stateful stream processing and analytics with a fast, single-binary engine. Using SQL as a domain-specific language and both row and column-based state stores, it enables developers to build real-time applications, data pipelines, and analytical dashboards at the edge or in the cloud, reducing the cost, time, and complexity of multi-component stacks.
+**Timeplus** simplifies stateful stream processing and analytics with a fast, single-binary engine. Using SQL as a domain-specific language and both row and column-based state stores, it enables developers to build real-time applications, data pipelines, and analytical dashboards at the edge or in the cloud, reducing the cost, time, and complexity of multi-component stacks.
 
 ### Architecture: The Best of Both Worlds {#architecture}
 
@@ -75,4 +77,4 @@ SQL-based rules can be used to trigger or resolve alerts in systems such as Page
 
 ### Scalability and Elasticity 
 
-Timeplus supports three deployment models: **MPP (shared-nothing)** for on-premises setups where ultra-low latency is critical, **storage/compute separation** for elastic cloud-native environments using S3 (or similar object storage) to store the NativeLog, Historical Store, and Query State Checkpoints with zero replication overhead, and **hybrid mode** that combines both approaches. In storage/compute separation deployments, clusters integrate seamlessly with Kubernetes HPA or AWS Auto Scaling Groups, enabling highly concurrent continuous queries while scaling automatically with demand. Please refer [Timeplus Architecture](/architecture) for more details. 
+Timeplus supports three deployment models: **MPP (shared-nothing)** for on-premises setups where ultra-low latency is critical, **storage/compute separation** for elastic cloud-native environments using S3 (or similar object storage) to store the NativeLog, Historical Store, and Query State Checkpoints with zero replication overhead, and **hybrid mode** that combines both approaches. In storage/compute separation deployments, clusters integrate seamlessly with Kubernetes HPA or AWS Auto Scaling Groups, enabling highly concurrent continuous queries while scaling automatically with demand. Please refer [Timeplus Architecture](/architecture) and [cluster](/cluster) design for more details. 

sidebars.js

@@ -39,7 +39,7 @@ const sidebars = {
         {
           type: "doc",
           id: "why-timeplus",
-          label: "What is Timeplus",
+          label: "Why Timeplus",
         },
         {
           type: "doc",