As a labor-intensive industry, logistics involves collection, warehousing, customs, trunk transportation, terminal distribution and other links. The practical operation link is long, and the amount of data related to people, goods and fields collected by IOT equipment is large. How to meet the requirements of timeliness and accuracy of data at a lower technical cost is a huge technical challenge. This article introduces a method that is different from traditional computing engines such as Hadoop, Flink and Spark. It uses MQ lightweight computing to support logistics data-based operation and decision making with low technical cost.
Xin Wang: Novice network, Senior technical Specialist, Alibaba Group Cainiao network senior technical expert, responsible person for export logistics data intelligence. Apache Storm PMC Member, Apache RocketMQ, IoTDB Committer He has rich experience in r&d and application in the field of big data.