Author: Zen and the Art of Computer Programming
1 Introduction
1. Background introduction
The rapid development of mobile Internet applications has become a trend of social and economic globalization. As more and more people use mobile terminal devices such as mobile phones and tablets, data generation and collection have become particularly important. At the same time, the data volume of mobile Internet is also showing explosive growth. According to market research statistics, up to now, the amount of data generated by mobile Internet applications has reached 1.2 billion, and the average daily data volume is close to 7.75GB, accounting for the vast majority of user traffic, and the value of this data has gradually increased to an incalculable level. degree of estimation. The construction of a mobile data platform is obviously an important and arduous task. For the construction of mobile data platforms, the first thing to solve is the core issues of data collection, storage, management and analysis. Secondly, data development specifications must be formulated to ensure data security, accuracy and effectiveness. Finally, a complete business operation system needs to be established to help enterprises better understand user needs and improve product quality and service levels through the strategic use of real-time tracking, prediction, and monitoring of data. Therefore, building a powerful and robust data center platform has become the key to building a successful mobile data platform.
2. Explanation of basic concepts and terms
data collection
Data collection refers to the process of collecting, cleaning, standardizing, transmitting and applying data from various data sources to mobile application clients. Generally, the data collection workflow includes the following steps:
- Data source selection - Mobile applications will obtain various data from different channels, such as various hardware information, network data, logs, etc.
- Data cleaning - Since the data types, structures or formats provided by different data sources are inconsistent, the data from different data sources must be cleaned to ensure data consistency and correctness.
- Data standardization - In order to facilitate the exchange, analysis and processing of data, the data collection process often needs to be standardized, such as unified data format, timestamp format, etc.
- Data transfer - data between different data sources needs to be transferred to the target server