Improvement of Kafka Streaming Using Partition and Multi-Threading in Big Data Environment

Abstract

The large amount of programmable logic controller (PLC) sensing data has rapidly increased in the manufacturing environment. Therefore, a large data store is necessary for Big Data platforms. In this paper, we propose a Hadoop ecosystem for the support of many features in the manufacturing industry. In this ecosystem, Apache Hadoop and HBase are used as Big Data storage and handle large scale data. In addition, Apache Kafka is used as a data streaming pipeline which contains many configurations and properties that are used to make a better-designed environment and a reliable system, such as Kafka offset and partition, which is used for program scaling purposes. Moreover, Apache Spark closely works with Kafka consumers to create a real-time processing and analysis of the data. Meanwhile, data security is applied in the data transmission phase between the Kafka producers and consumers. Public-key cryptography is performed as a security method which contains public and private keys. Additionally, the public-key is located in the Kafka producer, and the private-key is stored in the Kafka consumer. The integration of these above technologies will enhance the performance and accuracy of data storing, processing, and securing in the manufacturing environment.