


Volcano engine tool technology sharing: use AI to complete data mining and complete SQL writing with zero threshold
When using BI tools, the question often encountered is: "How can I produce and process data if I don't know SQL? Can I do mining analysis if I don't know the algorithm?"
When professional algorithm teams do data mining, data analysis and visualization will also appear relatively fragmented. Completing algorithm modeling and data analysis work in a streamlined manner is also a good way to improve efficiency.
At the same time, for professional data warehouse teams, data content with the same theme faces the problem of "repeated construction, relatively scattered use and management" - is there any way to produce it at the same time in one task, with the same theme? Datasets with different content? Can the produced data set be used as input to re-participate in data construction?
1. DataWind’s visual modeling capability is here
The BI platform DataWind intelligent data insight launched by the Volcano Engine has launched a new advanced feature-visual modeling.
Users can simplify the complex data processing and modeling process into a clear and easy-to-understand canvas process through visual dragging, pulling, and connecting operations. All types of users can complete data production and processing according to the idea of what they think is what they get. Thereby lowering the threshold for data production and acquisition.
Canvas supports the simultaneous construction of multiple groups of canvas processes, and one picture can realize the construction of multiple data modeling tasks, improving the efficiency of data construction and reducing task management costs; in addition, Canvas integrates and encapsulates more than 40 types of data cleaning , feature engineering operators, covering primary to high-level data production capabilities, without the need for coding to complete complex data capabilities.
2. Zero-threshold SQL tools
Data production and processing is the first step to obtain and analyze data.
For non-technical users, there is a certain threshold for using SQL syntax. At the same time, local files cannot be updated regularly, resulting in the dashboard needing to be redone manually every time. The technical manpower required to obtain data often needs to be scheduled, and the timeliness and satisfaction of data acquisition are greatly reduced. Therefore, it is particularly important to use zero-code data construction tools.
Listed below are two typical scenarios of how zero-threshold data processing is applied in work.
2.1 [Scenario 1] What you think is what you get, and the data processing process is completed visually
When product operation iterations are in urgent need of timely input feedback of different data, the data processing process can be abstracted and constructed through visualization The modular drag operator constructs the data processing process.
If you want to obtain the number of orders and order amounts based on date and city granularity, and obtain the city data of the top 10 daily consumption amount data, the operation is as follows:
General data processing process |
Visual modeling process |
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2.2 [Scenario 2] Quickly combine multiple tables to easily solve multi-data association calculations
In the data processing process, there are multiple data sources that need to be combined and used. Conventionally, it is difficult and time-consuming to master advanced Vlookup and other algorithms through Excel. At the same time, when the amount of data is large, the computer performance may not be able to complete the combined calculation of the data.
If there are two orders with relatively large amounts of data and a customer attribute information table, the profit amount needs to be calculated based on the bill amount and cost amount, and then the top 100 user order information is taken based on the profit contribution
General data processing process |
Visual modeling process |
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