Just like sewage treatment if the upstream source is not properly controlled spending a lot of money on downstream treatment will result in half the result with half the effort. How to control the quality of source data? It is necessary to check the data when it is synchronized. Some obvious data quality problems such as null values illogical data values chaotic data structures etc. are easy to find. Once this type of data is discovered it should be rejected and the problems should be reported to the production and research department of the business system. Team clearly request rectification and make up for it after the rectification is completed.
Dialectical analysis of the necessity of collecting SDK For exa Job Seekers Phone Numbers List mple the DataLeap suite produced by ByteDances Volcano Engine provides the function of collecting SDK that is users can configure the SDK by themselves to complete personalized data connection and collection into the lake. Whether it is necessary to collect SDK or not requires detailed analysis of the specific situation and conclusions need to be drawn after dialectical analysis based on your own business needs and scenarios data resource distribution available technical resources etc.
Data development For data development it is actually necessary to provide a set of tools and specifications to carry the entire data storage calculation and development process and to make this process standardized efficient and controllable as much as possible. For example at the tool level many major manufacturers have completed commercial packaging and provided a complete set of data development kits. Interested students can search for product white papers and technologies such as DataLeap Huoshan DataWork Alibaba and TBDS Tencent. For another example in terms of tool integration development processes and specifications Meituan and Bilibili have excellent practices called integrated modeling.
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