Author: Zen and the Art of Computer Programming
Apache Zeppelin (hereinafter referred to as Zeppelin) is an open source project originally developed by researchers at UC Berkeley University and officially released on October 1, 2013. The project is implemented in Scala language, which is an open source tool for data science, interactive data analysis and visualization. Its unique features include a powerful SQL query interface, support for multiple programming languages (such as Java, Python, R, etc.), and the ability to flexibly present results to users. It also supports advanced analysis of datasets and provides a rich library of built-in functions, statistical charting libraries, and machine learning algorithms.
Zeppelin has a wide range of applications in China. In the data analysis of today's enterprises, Zeppelin is an important tool because it can improve data analysis efficiency, improve data quality, and meet business needs. In addition, Zeppelin has also attracted the attention of many developers because it has a simple and easy-to-use interface that can be easily used by non-technical personnel, especially in those environments that need to quickly generate, share, analyze and explore data. Therefore, Zeppelin occupies a pivotal position in the field of data analysis.
2. Explanation of basic concepts and terms
2.1 Data Warehouse
Data warehouse (Data Warehouse, DW) is a system used to store, organize and analyze massive data. It is mainly used for subject-oriented complex and diverse reporting and decision-making, and is used to support various application scenarios such as marketing, sales, human resource management, and decision support.
The data warehouse is divided into three layers: the source data layer, the data mart layer, and the data lake area (or called the dimensional modeling layer). Among them, the source data layer usually contains raw data, including various sources such as log files, transaction records, database tables, emails, web page click streams, mobile application data, etc.; the data mart layer usually uses fact tables and dimension tables The purpose of organizing data is to retrieve and analyze data more efficiently; the data lake area is used to support complex analysis through