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Apache Spark Structured Streaming Helps Smart Manufacturing - Xiaochang Wu

1,532 views · Published 26 October 2017 · 32:45 · Indexed 28 October 2026

Channel: Databricks · 2017 · Science & Technology

This presentation introduces how we design and implement a real-time processing platform using latest Spark Structured Streaming framework to intelligently transform the production lines in the manufacturing industry. In the traditional production line there are a variety of isolated structured, semi-structured and unstructured data, such as sensor data, machine screen output, log output, database records etc. There are two main data scenarios: 1) Picture and video data with low frequency but a large amount; 2) Continuous data with high frequency. They are not a large amount of data per unit. However the total amount of them is very large, such as vibration data used to detect the quality of the equipment. These data have the characteristics of streaming data: real-time, volatile, burst, disorder and infinity. Making effective real-time decisions to retrieve values from these data is critical to smart manufacturing. The latest Spark Structured Streaming framework greatly lowers the bar for building highly scalable and fault-tolerant streaming applications. Thanks to the Spark we are able to build a low-latency, high-throughput and reliable operation system involving data acquisition, transmission, analysis and storage. The actual user case proved that the system meets the needs of real-time decision-making. The system greatly enhance the production process of predictive fault repair and production line material tracking efficiency, and can reduce about half of the labor force for the production lines.Session hashtag: #EUstr1

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Apache Spark Structured Streaming Helps Smart Manufacturing - Xiaochang Wu

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