包阅导读总结
1. 关键词：Headless 数据架构、数据管理、数据格式、处理查询、监管合规
2. 总结：本文介绍了头less数据架构，它实现了数据存储等与服务的分离，能集中管理数据，简化监管合规。该架构类似无头服务器，可涵盖多种数据格式，如数据流和表，为不同用例提供灵活性。
3. 主要内容：
– 头less数据架构是数据存储等与写入、处理和查询服务的分离
– 可在单一逻辑位置管理数据，包括权限、模式演变和表优化
– 使监管合规更简单，数据集中而非多处复制
– 类似无头服务器，需自带处理或查询“头”接入数据，如 Trino 等
– 能涵盖多种数据格式，数据流提供低延迟增量数据访问，表提供高效批量查询能力
– 二者结合为不同用例提供选择灵活性
思维导图：
文章地址：https://www.infoworld.com/article/3479722/a-developers-guide-to-the-headless-data-architecture.html
文章来源：infoworld.com
作者：InfoWorld
发布时间：2024/8/5 8:30
语言：英文
总字数：1583字
预计阅读时间：7分钟
评分：90分
标签：无状态数据架构,数据管理,数据存储,数据处理,Apache Kafka
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The headless data architecture is an organic emergence of the separation of data storage, management, optimization, and access from the services that write, process, and query it. With this architecture, you can manage your data from a single logical location, including permissions, schema evolution, and table optimizations. And, to top it off, it makes regulatory compliance a lot simpler, because your data resides in one place, instead of being copied around to every processing engine that needs it.
We call it a “headless” data architecture because of its similarity to a “headless server,” where you have to use your own monitor and keyboard to log in. If you want to process or query your data in a headless data architecture, you will have to bring your own processing or querying “head” and plug it into the data — for example, Trino, Presto, Apache Flink, or Apache Spark.
A headless data architecture can encompass multiple data formats, with data streams and tables as the two most common. Streams provide low-latency access to incremental data, while tables provide efficient bulk-query capabilities. Together, they give you the flexibility to choose the format that is most suitable for your use cases, whether it’s operational, analytical, or somewhere in between.
First, let’s take a look at streaming in the headless data architecture.
