DataHub is a data access and processing platform in Tencent Cloud, which provides data access, processing, and distribution features at one stop. Data is vital in internet businesses, and data access and reporting serve as the bridge between data generation, computing, storage, and analysis throughout the entire linkage. Therefore, simple yet efficient data access is critical. A business usually has data to be reported to the backend for storage, analysis, computing, and search, such as business metrics, process information, and monitoring data. The general processing linkage is as shown below:
You can set up a classic data reporting architecture generally in the following steps:
In the above four steps, the development and deployment workload for the server is the highest, as you need to consider code logic development as well as the scalability and stability of the server and downstream storage. In addition, when the data volume gets high, problems on the server will become more obvious, and the server needs to be maintained with a lot of manpower and resources. As tasks involved in this regard are generally universal, DataHub aims to meet the requirements in such scenario by offering a stable, elastic, high-reliability, and high-throughput data access service.
DataHub provides SDKs for various programming languages, including Java, Python, Go, PHP, Node.js, C++, and .NET, to help the client better report data. Data can be reported to a storage engine such as CKafka in three simple steps (more Tencent Cloud message queue services like TDMQ for RocketMQ, Pulsar, RabbitMQ, and CMQ will be supported in the future).
After you complete data access easily and quickly through DataHub, how to make the data generate value becomes the most important thing. To address this, DataHub provides two core features:
DataHub offers a simple data ETL engine, which can cleanse most types of data in order to simply format and process data for subsequent use.
After data processing is completed, DataHub can also meet the data distribution needs in various scenarios:
After the data has gone through the four stages of reporting, access, processing, and distribution, the general data reporting and analysis needs can be easily and quickly satisfied, creating data value at ultra low costs.
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