Update 01-index.md

docs-en/01-index.md

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 TDengine is a [high-performance](https://tdengine.com/fast), [scalable](https://tdengine.com/scalable) time series database with [SQL support](https://tdengine.com/sql-support). This document is the TDengine user manual. It introduces the basic concepts, installation, features, SQL, APIs, operation, maintenance, kernel design, etc. It’s written mainly for architects, developers and system administrators.
 
+To get a global view about TDengine, like feature list, benchmarks, and competitive advantages, please browse through section [Introduction](./intro).
+
 TDengine makes full use of the characteristics of time series data, proposes the concepts of "one table for one data collection point" and "super table", and designs an innovative storage engine, which greatly improves the efficiency of data ingestion, querying and storage. To understand the new concepts and use TDengine in the right way, please read [“Concepts”](./concept) thoroughly.
 
 If you are a developer, please read the [“Developer Guide”](./develop) carefully. This section introduces the database connection, data modeling, data ingestion, query, continuous query, cache, data subscription, user-defined function, etc. in detail. Sample code is provided for a variety of programming languages. In most cases, you can just copy and paste the sample code, make a few changes to accommodate your application, and it will work.
 
-We live in the era of big data, and scale-up is unable to meet the growing business needs. Any modern data system must have the ability to scale out, and clustering has become an indispensable feature of big data systems. The TDengine team has not only developed the cluster feature, they also decided to open source this important feature. To learn how to deploy, manage and maintain a TDengine cluster please refer to ["cluster"](./cluster).
+We live in the era of big data, and scale-up is unable to meet the growing business needs. Any modern data system must have the ability to scale out, and clustering has become an indispensable feature of big data systems. The TDengine team has not only developed the cluster feature, they also decided to open source this important feature. To learn how to deploy, manage and maintain a TDengine cluster please refer to ["Cluster"](./cluster).
 
 TDengine uses SQL as its query language, which greatly reduces learning costs and migration costs. In addition to the standard SQL, TDengine has extensions to support time series data scenarios better, such as roll up, interpolation, time weighted average, etc. The ["SQL Reference"](./taos-sql) chapter describes the SQL syntax in detail, and lists the various supported commands and functions.
 
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 If you want to know more about TDengine tools, REST API, and connectors for various programming languages, please see the ["Reference"](./reference) chapter.
 
-If you are very interested in the internal design of TDengine, please read the chapter ["TDengine Inside”](./tdinternal), which introduces the cluster design, data partitioning, sharding, writing, and reading processes in detail. If you want to study TDengine code or even contribute code, please read this chapter carefully.
+If you are very interested in the internal design of TDengine, please read the chapter ["Inside TDengine”](./tdinternal), which introduces the cluster design, data partitioning, sharding, writing, and reading processes in detail. If you want to study TDengine code or even contribute code, please read this chapter carefully.
 
 TDengine is an open source database, you are welcome to be a part of TDengine. If you find any errors in the documentation, or the description is not clear, please click "Edit this page" at the bottom of each page to edit it directly.