Author: Zen and the Art of Computer Programming
Due to the vigorous development of the Internet, massive amounts of data are more and more easily generated. These data may come from various sources, in the form of structured, semi-structured, unstructured or even multimedia. In the application, it is necessary to integrate data from different sources, such as rule-based matching, business knowledge-based fusion, graph network-based analysis, etc. However, there are often challenges in the process of data integration. In order to deal with these challenges, many excellent technologies have emerged in the field of data integration, such as ETL (extract-transform-load) tools, machine learning methods, graph databases, etc. However, how to effectively integrate massive data and apply it to the actual production environment is still a big problem. This article will discuss the application scenarios of big data technology in data integration and related technical solutions, and explain the relevant principles and methodologies from the aspects of data integration efficiency, cost, robustness, reliability, etc., hoping to inspire readers.
2. Explanation of basic concepts and terms
2.1 Big data
Definition: Refers to data collections with ultra-high dimensionality, diversity, and rapid growth characteristics. The three characteristics of "ultra-high dimensionality", "diversity" and "rapid growth" indicate the complexity of the dataset and the scale of growth over the life cycle of the data itself. Big data generally includes unstructured, semi-structured and structured data. Unstructured data includes text, audio, video, images, maps, models, application logs, etc. Semi-structured data refers to data stored in various formats or encoding methods, such as JSON, XML, CSV, HTML, RDF, etc. Structured data refers to data with fixed schema and column names, such as tables in relational databases, documents in NoSQL databases, spreadsheets, log files, etc.
2.2 Data Integration
Definition: Integrate data from multiple sources according to specified rules to generate information and indicators that meet the needs. Data integration can be broken down into three types: ETL, data warehouse, and data lake. ETL mainly focuses on extracting, transforming, and loading (Extract Transform Load) data to the target system; data warehouse is a subject-oriented central data set for storing collated, cleaned and prepared data; data lake is a