diff --git a/docs/en/02-intro/index.md b/docs/en/02-intro/index.md
index 03ecf253a487a4a263143287727e5bbc81c9aace..d385845d7c57203d6e1cc8ddb8d53307f2655914 100644
--- a/docs/en/02-intro/index.md
+++ b/docs/en/02-intro/index.md
@@ -12,32 +12,32 @@ This section introduces the major features, competitive advantages, typical use-
The major features are listed below:
1. Insert data
- - supports [using SQL to insert](../develop/insert-data/sql-writing).
- - supports [schemaless writing](../reference/schemaless/) just like NoSQL databases. It also supports standard protocols like [InfluxDB LINE](../develop/insert-data/influxdb-line)，[OpenTSDB Telnet](../develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](../develop/insert-data/opentsdb-json) among others.
- - supports seamless integration with third-party tools like [Telegraf](../third-party/telegraf/), [Prometheus](../third-party/prometheus/), [collectd](../third-party/collectd/), [StatsD](../third-party/statsd/), [TCollector](../third-party/tcollector/) and [icinga2/](../third-party/icinga2/), they can write data into TDengine with simple configuration and without a single line of code.
+ - Supports [using SQL to insert](../develop/insert-data/sql-writing).
+ - Supports [schemaless writing](../reference/schemaless/) just like NoSQL databases. It also supports standard protocols like [InfluxDB Line](../develop/insert-data/influxdb-line), [OpenTSDB Telnet](../develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](../develop/insert-data/opentsdb-json) among others.
+ - Supports seamless integration with third-party tools like [Telegraf](../third-party/telegraf/), [Prometheus](../third-party/prometheus/), [collectd](../third-party/collectd/), [StatsD](../third-party/statsd/), [TCollector](../third-party/tcollector/), [EMQX](../third-party/emq-broker), [HiveMQ](../third-party/hive-mq-broker), and [Icinga2](../third-party/icinga2/), they can write data into TDengine with simple configuration and without a single line of code.
2. Query data
- - supports standard [SQL](../taos-sql/), including nested query.
- - supports [time series specific functions](../taos-sql/function/#time-series-extensions) and [time series specific queries](../taos-sql/distinguished), like downsampling, interpolation, cumulated sum, time weighted average, state window, session window and many others.
- - supports [user defined functions](../taos-sql/udf).
+ - Supports standard [SQL](../taos-sql/), including nested query.
+ - Supports [time series specific functions](../taos-sql/function/#time-series-extensions) and [time series specific queries](../taos-sql/distinguished), like downsampling, interpolation, cumulated sum, time weighted average, state window, session window and many others.
+ - Supports [User Defined Functions (UDF)](../taos-sql/udf).
3. [Caching](../develop/cache/): TDengine always saves the last data point in cache, so Redis is not needed for time-series data processing.
-4. [Stream Processing](../develop/stream/): not only is the continuous query is supported, but TDengine also supports even driven stream processing, so Flink or spark is not needed for time-series data processing.
-5. [Data Subscription](../develop/tmq/): application can subscribe a table or a set of tables. API is the same as Kafka, but you can specify filter conditions.
+4. [Stream Processing](../develop/stream/): Not only is the continuous query is supported, but TDengine also supports event driven stream processing, so Flink or Spark is not needed for time-series data processing.
+5. [Data Subscription](../develop/tmq/): Application can subscribe a table or a set of tables. API is the same as Kafka, but you can specify filter conditions.
6. Visualization
- - supports seamless integration with [Grafana](../third-party/grafana/) for visualization.
- - supports seamless integration with Google Data Studio.
+ - Supports seamless integration with [Grafana](../third-party/grafana/) for visualization.
+ - Supports seamless integration with Google Data Studio.
7. Cluster
- - supports [cluster](../deployment/) with the capability of increasing processing power by adding more nodes.
- - supports [deployment on Kubernetes](../deployment/k8s/)
- - supports high availability via data replication.
+ - Supports [cluster](../deployment/) with the capability of increasing processing power by adding more nodes.
+ - Supports [deployment on Kubernetes](../deployment/k8s/).
+ - Supports high availability via data replication.
8. Administration
- - provides [monitoring](../operation/monitor) on running instances of TDengine.
- - provides many ways to [import](../operation/import) and [export](../operation/export) data.
+ - Provides [monitoring](../operation/monitor) on running instances of TDengine.
+ - Provides many ways to [import](../operation/import) and [export](../operation/export) data.
9. Tools
- - provides an interactive [command-line interface](../reference/taos-shell) for management, maintenance and ad-hoc queries.
- - provides a tool [taosBenchmark](../reference/taosbenchmark/) for testing the performance of TDengine.
+ - Provides an interactive [Command-line Interface (CLI)](../reference/taos-shell) for management, maintenance and ad-hoc queries.
+ - Provides a tool [taosBenchmark](../reference/taosbenchmark/) for testing the performance of TDengine.
10. Programming
- - provides [connectors](../reference/connector/) for [C/C++](../reference/connector/cpp), [Java](../reference/connector/java), [Python](../reference/connector/python), [Go](../reference/connector/go), [Rust](../reference/connector/rust), [Node.js](../reference/connector/node) and other programming languages.
- - provides a [REST API](../reference/rest-api/).
+ - Provides [connectors](../reference/connector/) for [C/C++](../reference/connector/cpp), [Java](../reference/connector/java), [Python](../reference/connector/python), [Go](../reference/connector/go), [Rust](../reference/connector/rust), [Node.js](../reference/connector/node) and other programming languages.
+ - Provides a [REST API](../reference/rest-api/).
For more details on features, please read through the entire documentation.
@@ -49,7 +49,7 @@ By making full use of [characteristics of time series data](https://tdengine.com
- **[Simplified Solution](https://tdengine.com/tdengine/simplified-time-series-data-solution/)**: Through built-in caching, stream processing and data subscription features, TDengine provides a simplified solution for time-series data processing. It reduces system design complexity and operation costs significantly.
-- **[Cloud Native](https://tdengine.com/tdengine/cloud-native-time-series-database/)**: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for kubernetes deployment and full observability, TDengine is a cloud native Time-Series Database and can be deployed on public, private or hybrid clouds.
+- **[Cloud Native](https://tdengine.com/tdengine/cloud-native-time-series-database/)**: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for Kubernetes deployment and full observability, TDengine is a cloud native Time-series Database and can be deployed on public, private or hybrid clouds.
- **[Ease of Use](https://tdengine.com/tdengine/easy-time-series-data-platform/)**: For administrators, TDengine significantly reduces the effort to[
](https://tdengine.com/tdengine/easy-time-series-data-platform/) deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access.
@@ -58,15 +58,22 @@ By making full use of [characteristics of time series data](https://tdengine.com
- **[Open Source](https://tdengine.com/tdengine/open-source-time-series-database/)**: TDengine’s core modules, including cluster feature, are all available under open source licenses. It has gathered over 19k stars on GitHub. There is an active developer community, and over 140k running instances worldwide.
-With TDengine, the total cost of ownership of your time-series data platform can be greatly reduced. 1: With its superior performance, the computing and storage resources are reduced significantly；2: With SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly；3: With its simplified solution and nearly zero management, the operation and maintenance costs are reduced significantly.
+With TDengine, the total cost of ownership of your time-series data platform can be greatly reduced.
+
+1. With its superior performance, the computing and storage resources are reduced significantly.
+2. With SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly.
+3. With its simplified solution and nearly zero management, the operation and maintenance costs are reduced significantly.
## Technical Ecosystem
This is how TDengine would be situated, in a typical time-series data processing platform:
+
+
-
Figure 1. TDengine Technical Ecosystem
+
Figure 1. TDengine Technical Ecosystem
+
On the left-hand side, there are data collection agents like OPC-UA, MQTT, Telegraf and Kafka. On the right-hand side, visualization/BI tools, HMI, Python/R, and IoT Apps can be connected. TDengine itself provides an interactive command-line interface and a web interface for management and maintenance.
diff --git a/docs/zh/02-intro.md b/docs/zh/02-intro.md
index c78eb176404ebae68638f63acc516b4557c2ddc2..012c49d2c3c82d5865eb2d8e76f37bb0f0f69e8b 100644
--- a/docs/zh/02-intro.md
+++ b/docs/zh/02-intro.md
@@ -85,8 +85,8 @@ TDengine 的主要功能如下：
+