Avoiding Entity Key Drift in a Data Lake: Step 1, Normalization
中文摘要
探讨构建高频流式管道的首步：通过数据标准化避免数据湖中的实体键漂移，基于openSenseMap实时API分析数据质量问题。
English Summary
Part one of a series on building streaming pipelines, focusing on normalization to avoid entity key drift using the openSenseMap IoT API.
This is the opening piece of a four-part deep dive series, on building a high-frequency streaming pipeline against a live public API. The data source is openSenseMap, a citizen-science IoT network used for climate research, mostly in Germany. A live public API is what makes it useful: it produces data-quality problems and edge cases that clean sample datasets never show. This article focuses on step-1: Normalization, later pieces cover matching algorithms, adaptive polling and noise filtering, and a vendor-agnostic Apache Iceberg pipeline with Terraform that runs locally in Docker and moves to AWS or GCP with minimal change. The post Avoiding Entity Key Drift in a Data Lake: Step 1, Normalization appeared first on Towards Data Science.