事务数据的两级数据暂存ETL
Two-level Data Staging ETL for Transaction Data
摘要：
在数据仓库中，抽取-转换-加载（Extract-Transform-Load，ETL）技术定期地将数据从数据源中抽取到一个中央数据仓库中，以支持业务决策。事务处理系统的数据具有插入、更新、删除等频繁变化的特点。ETL将更改传播到数据仓库并维护更改历史是一个挑战。此外，当处理具有依赖关系的数据时，ETL作业通常按顺序运行，这在处理早期到达的数据时不是最佳的\eg。在本文中，我们提出了一个两级的数据分级ETL处理事务数据。该方法检测来自事务处理系统的数据的变化，识别对应于变化的操作码，并使用两个暂存数据库来促进ETL过程中的数据处理。提出的ETL为快变、慢变和早到数据的处理提供了“一站式”方法。
英文摘要：
In data warehousing, Extract-Transform-Load (ETL) extracts the data from data sources into a central data warehouse regularly for the support of business decision-makings. The data from transaction processing systems are featured with the high frequent changes of insertion, update, and deletion. It is challenging for ETL to propagate the changes to the data warehouse, and maintain the change history. Moreover, ETL jobs typically run in a sequential order when processing the data with dependencies, which is not optimal, \eg, when processing early-arriving data. In this paper, we propose a two-level data staging ETL for handling transaction data. The proposed method detects the changes of the data from transactional processing systems, identifies the corresponding operation codes for the changes, and uses two staging databases to facilitate the data processing in an ETL process. The proposed ETL provides the "one-stop" method for fast-changing, slowly-changing and early-arriving data processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
