实时数据质量监控：Kafka 流契约与语法和语义测试

In today’s data-driven landscape, monitoring data quality has become a critical need for ensuring reliable and efficient data usage across domains. High-quality data is the backbone of AI innovation, driving efficiency and unlocking new opportunities. As decentralized data ownership grows, the ability to effectively monitor data quality is essential for maintaining reliability in data systems.

在当今数据驱动的环境中，监控数据质量已成为确保跨领域可靠和高效数据使用的关键需求。高质量的数据是AI创新的支柱，推动效率并开启新的机会。随着去中心化数据所有权的增长，有效监控数据质量的能力对于维护数据系统的可靠性至关重要。

Kafka streams, as a vital component of real-time data processing, play a significant role in this ecosystem. However, unreliable data within Kafka streams can lead to errors and inefficiencies for downstream users, and monitoring the quality of data within these streams has always been a challenge. This blog introduces a solution that empowers stream users to define a data contract, specifying the rules that Kafka stream data must adhere to. By leveraging this user-defined data contract, the solution performs automated real-time data quality checks, identifies problematic data as it occurs, and promptly notifies stream owners. This ensures timely action, enabling effective monitoring and management of Kafka stream data quality while supporting the broader goals of data mesh and AI-driven innovation.

Kafka流作为实时数据处理的重要组成部分，在这个生态系统中发挥着重要作用。然而，Kafka流中的不可靠数据可能导致下游用户出现错误和低效，监控这些流中的数据质量一直是一个挑战。本文介绍了一种解决方案，使流用户能够定义数据合同，指定Kafka流数据必须遵循的规则。通过利用这个用户定义的数据合同，该解决方案执行自动化的实时数据质量检查，识别发生的问题数据，并及时通知流所有者。这确保了及时采取行动，使Kafka流数据质量的有效监控和管理成为可能，同时支持数据网格和AI驱动创新的更广泛目标。

Problem statement

问题陈述

In the past, monitoring Kafka stream data processing lacked an effective solution for data quality validation. This limitation made it challenging to identify bad data, notify users in a timely manner, and prevent the cascading impact on downstream users from further escalating.

过去，监控Kafka流数据处理缺乏有效的数据质量验证解决方案。这一局限性使得识别坏数据、及时通知用户以及防止对下游用户的级联影响进一步升级变得具有挑战性。

Challenges in syntactic and semantic issue identification:

语法和语义问题识别中的挑战:

• Syntactic issues: Refers to...