Author: Zen and the Art of Computer Programming
1 Introduction
As an important infrastructure for business data, the data platform provides data services and analytical capabilities that have become the key for various departments of the company to work together, communicate efficiently, and improve work efficiency. However, due to the influence of various factors such as the differences in needs of different industries and different data scales and complexities, creating a high-quality, low-latency, easy-to-expand, reliable, secure, and easy-to-use data platform also faces huge challenges. challenge. In recent years, with the popularization of cloud computing, container technology and microservice architecture, the need to build a data platform based on open source solutions has attracted more and more widespread attention from society. The cost of building a data platform is getting lower and lower, and the market competition is becoming increasingly fierce. This article will lead readers from 0 to 1 to experience some key points in the construction process of an open source data platform, including the selection of various components of the open source data platform, data collection, storage, processing, analysis, visualization, monitoring, security, management, etc. By sharing the pitfalls and lessons learned by the author in practice, I hope to help more people get started quickly and master the skills of building an open source data platform.
2. Open source data platform framework
First, let's sort out the main components involved in the construction of an open source data platform. The following are the main components of the data platform:
(1) Data collection module: responsible for collecting original data, such as database logs, network traffic, server logs, third-party interfaces, etc.; (2)
Data transmission module: responsible for transferring the collected data through Transmitted to subsequent modules in various ways;
(3) Data storage module: Responsible for persistent storage of data for subsequent analysis and query; (
4) Data cleaning and conversion module: Responsible for cleaning and converting original data to make it consistent with subsequent modules Requirements;
(5) Data calculation module: responsible for calculating the above data, including data aggregation, statistics, sorting, etc.; (
6) Data report display module: