Author: Zen and the Art of Computer Programming
1 Introduction
With the increase in the amount of Internet data, the generation of massive data and the release of value, and the wide application of emerging technologies such as cloud computing, big data, and artificial intelligence, the data lake architecture has become one of the hot topics in the field of enterprise data analysis. . This article will introduce the basic concepts and terminology of the data lake from three aspects: the definition, characteristics and structure of the data lake architecture. Then, it will show readers how to use open source tools to build a data lake through multiple specific cases. Finally, we will discuss the future direction and outlook.
2. What is a data lake?
Data Lake, the English name Data Lake, is a cloud-based data warehouse. In January 2014, Amazon Bezos announced that it was officially listing its data asset "Amazon Web Services Glacier" trademark. The trademark refers to Amazon's "sacred place" for storing, retrieving and analyzing large data sets in the cloud. Today, data lakes have become an important tool for enterprises to conduct data analysis and decision-making. Acquiring, processing, and analyzing data is often costly, and a data lake can significantly reduce this cost while providing better value discovery capabilities. The data lake architecture is a solution to effectively store and manage large data sets during the construction of big data infrastructure. According to statistics, the world generates more than 10 billion pieces of data every day, and the development of data lakes has greatly promoted the release of data value. The characteristics of a data lake mainly include the following aspects:
- Diversity of data sources: The data sources in the data lake architecture include not only data sources from different categories such as databases, file systems, message queues, and log systems, but also other data sources, such as social networks, emails, and IoT devices , mobile applications, etc.;
- Large data scale: the data lake architecture can help users effectively manage massive data, especially after various sources are brought together;
- The value of data analysis and decision-making: the data lake architecture can help users quickly and efficiently conduct data analysis and decision-making, and can also provide a large number of visualization, machine learning and other services;
- Data Sharing and Collaboration: Data