TalkVenue A

Low-code technology practices in observability platforms

10/1710:15 - 11:00

Laiqiang Ding

Laiqiang DingHead of Alibaba Cloud SLS Shanghai team

Head of Alibaba Cloud SLS Shanghai. He has been in business for more than 10 years. He used to be a senior technical architect of Splunk China Lab, and he is good at AIOps/SecOps big data analysis platform construction and scenario implementation.

Willing to share, past PyCon/Yunqi/CSDN conferences or live lecturers, sharing more than 20 different topic speeches or live broadcasts, nearly a hundred technical product articles, covering open source AIOps middle station construction, observable alarm operation and maintenance platform practice, big data Analysis and visualization, workflow scheduling, functional, design patterns and other aspects have been well received.

As business innovation accelerates in the era of digital intelligence and automation, increasingly complex architectures and huge data volumes have placed higher demands on centralized big data platforms. On the other hand, how to quickly build applications, processes, and business solutions continue to accumulate, which has led to the explosion of Low-Code products in various fields in the past two years, and their related technologies are also applicable under the observability platform. This session introduces how to use low-code technologies (Python as an example) in building observability platforms to innovate the experience of using products and improve the flexibility of scenarios and engineering efficiency of solutions. The presentation will cover the key aspects of flexible data collection and processing, intelligent analysis, monitoring and alerting, and response automation, involving cloud-native, stream batch computing, OLAP, DSL, orchestration and templating technologies. Presentation outline. 1:

  1. background overview of observability and low-code technologies
  2. technical challenges, architecture design and technical difficulties in each aspect of the observability platform
  3. best practices and use of Python technologies
  4. future expectations