Author: Zen and the Art of Computer Programming
1 Introduction
Data Integration and Data Analysis are playing an increasingly important role in current Internet companies. With the popularization of cloud computing, big data, artificial intelligence, blockchain and other technologies, the speed and scale of data generation, storage and processing are undergoing revolutionary changes. For the construction of traditional data warehouses, it has been unable to withstand the impact of such massive data. In this context, data integration tools are particularly critical. Traditional data warehouse construction relies on rule-based ETL (Extract-Transform-Load), which is not suitable for the rapid development of new technologies. Data integration mainly includes three types: data synchronization between log, relational and non-relational databases; data normalization and cleaning between different data sources; and message passing between different types of application systems. This article will elaborate on the theoretical basis, design methods and usage skills of MarkLogic, and share with you the best practices in data integration. Hope to bring some reference value to readers.
2. Explanation of basic concepts and terms
2.1 Data Integration
Data integration refers to the process of fusing, integrating and transforming data from multiple sources and forms to obtain unified and effective results. Data integration can be divided into three stages: Extraction, Transformation, and Loading. The extraction phase includes acquiring data from various sources such as various data sources, files, reports, databases, APIs, etc. The conversion stage includes operations such as modifying data, adding, deleting, checking, and modifying. The loading stage includes storing the processed data into the target system, such as database, file, Hadoop cluster, message queue, etc. Integration of data is usually achieved through external tools or programming languages.