@@ -5,40 +5,77 @@ title: Introduction to TDengine Cloud Service
TDengine Cloud, is the fast, elastic, serverless and cost effective time-series data processing service based on the popular open source time-series database, TDengine. With TDengine Cloud you get the highly optimized and purpose-built for IoT time-series platform, for which TDengine is known.
This section introduces the major features, competitive advantages, typical use-cases and benchmarks to help you get a high level overview of TDengine.
This section introduces the major features, competitive advantages and typical use-cases to help you get a high level overview of TDengine cloud service.
## Major Features
The major features are listed below:
1. While TDengine supports [using SQL to insert](../data-in/insert-data), it also supports [Schemaless writing](/reference/schemaless/) just like NoSQL databases. TDengine 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.
2. TDengine supports seamless integration with third-party tools like [Telegraf](../data-in/telegraf)，[Prometheus](../data-in/prometheus)，they can write data into TDengine with simple configuration and without a single line of code.
3. Support for [time series specific queries](../taos-sql/distinguished), including aggregation, nested query, downsampling, interpolation and others.
4. Support for [user defined functions](../taos-sql/udf).
5. Support for [caching](../taos-sql/database). TDengine always saves the last data point in cache, so Redis is not needed in some scenarios.
6. Support for [stream processing](../taos-sql/stream).
7. Support for [data subscription](../taos-sql/tmq) with the capability to specify filter conditions.
8. High availability is supported by replication including multi-cloud replication.
9. Provides an interactive [command-line interface](../tools/cli) for management, maintenance and ad-hoc queries.
10. Provides many ways to [get data in](../data-in) and [get data out](../data-out) data.
11. Provides a Dashboard to monitor your running instances of TDengine.
12. Provides [connectors](../programming/connector/) for [Java](../programming/connector/java), [Python](../programming/connector/python), [Go](../programming/connector/go), [Rust](../programming/connector/rust), and [Node.js](../programming/connector/node).
13. Provides a [REST API](../programming/connect/rest-api/).
14. Supports seamless integration with [Grafana](../visual/grafana) for visualization.
15. Supports seamless integration with Google Data Studio.
For more details on features, please read through the entire documentation.
1. Data In
- Supports [using SQL to insert](../data-in/insert-data).
- Supports [Telegraf](../data-in/telegraf/).
- Supports [Prometheus](../data-in/prometheus/).
2. Data Out
- Supports standard [SQL](../data-out/query-data/), including nested query.
- Supports exporting data via tool [taosDump](../data-out/taosdump/).
- Supports writing data to [Prometheus](../data-out/prometheus/).
- Supports exporting data via [data subscription](../tmq/).
3. Data Explorer: browse through databases and even run SQL queryies once you login.
4. Visualization:
- Supports [Grafana](../visual/grafana/)
- Supports Google data studio (to be released soon)
- Supports Grafana cloud (to be released soon)
6.[Stream Processing](../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.
7.[Data Subscription](../tmq/): Application can subscribe a table or a set of tables. API is the same as Kafka, but you can specify filter conditions.
8. Enterprise
- Supports backuping data everyday.
- Supports replicating a database to another region or cloud.
- Supports VPC peering.
- Supports Allowed IP list for security.
9. Tools
- Provides an interactive [Command-line Interface (CLI)](../tools/cli/) for management and ad-hoc queries.
- Provides a tool [taosBenchmark](../tools/taosbenchmark/) for testing the performance of TDengine.
10. Programming
- Provides [connectors](../programming/connector/) for Java, Python, Go, Rust, Node.js and other programming languages.
- Provides a [REST API](../programming/connector/rest-api/).
For more details on features, please read through the entire documentation.
## Competitive Advantages
By making full use of [characteristics of time series data](https://tdengine.com/tsdb/characteristics-of-time-series-data/), TDengine Cloud differentiates itself from other time series platforms, with the following advantages.
By making full use of [characteristics of time series data](https://tdengine.com/tsdb/characteristics-of-time-series-data/) and its cloud native design, TDengine Cloud differentiates itself from other time series data cloud services, with the following advantages.
-**[High-Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine Cloud is a fast, elastic, serverless purpose built platform for IoT time-series data. It is the only time-series platform to solve the high cardinality issue to support billions of data collection points while outperforming other time-series platforms for data ingestion, querying and data compression.
-**Worry Free**: TDengine Cloud is a fast, elastic, serverless purpose built cloud platform for time-series data. It provides worry-free operations with a fully managed cloud service. You pay as you go.
-**[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. It is Enterprise ready with backup, multi-cloud replication, VPC peering and IP whitelisting.
-**[High-Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: It is the only time-series platform to solve the high cardinality issue to support billions of data collection points while outperforming other time-series platforms for data ingestion, querying and data compression.
-**[Ease of Use](https://tdengine.com/tdengine/easy-time-series-data-platform/)**: For administrators, TDengine Cloud provides worry-free operations with a fully managed cloud native solution. For developers, it provides a simple interface, simplified solution and seamless integration with third party tools. For data users, it provides SQL support with powerful time series extensions built for data analytics.
-**[Easy Data Analytics](https://tdengine.com/tdengine/time-series-data-analytics-made-easy/)**: Through super tables, storage and compute separation, data partitioning by time interval, pre-computation and other means, TDengine makes it easy to explore, format, and get access to data in a highly efficient way.
-**Enterprise Ready** It supports backup, multi-cloud/multi-region database replication, VPC peering and IP whitelisting.
With TDengine cloud, the **total cost of ownership of your time-series data platform can be greatly reduced**.
1. With its built-in caching, stream processing and data subscription, system complexity and operation cost are highly reduced.
2. With SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly.
3. With the elastic, serverless and fully managed service, 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:
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.
## Typical Use Cases
As a high-performance and cloud native time-series database, TDengine's typical use case include but are not limited to IoT, Industrial Internet, Connected Vehicles, IT operation and maintenance, energy, financial markets and other fields. TDengine is a purpose-built database optimized for the characteristics of time series data. As such, it cannot be used to process data from web crawlers, social media, e-commerce, ERP, CRM and so on. More generally TDengine is not a suitable storage engine for non-time-series data.
