In the previous chapters on creating data requirements and automating ETL, we step-by-step broke down the process of data preparation, transformation, validation, and visualization. These activities were implemented as separate code blocks (Fig. 7.2-18 – Fig. 7.2-20) each performing a specific task.

Now we have the next goal – to combine these elements into a single, coherent and automated pipeline of data processing – a pipeline, ETL -Pipeline – in which all stages (loading, validation, visualization, export) are executed sequentially in a single auto-executable script.

In the following example, the full cycle of data processing will be realized: from loading the source CSV file →to checking the structure and values using regular expressions →calculating the results→ generating a visual report in PDF format.

• The following text query to the LLM. can be used to retrieve the appropriate code:

  Please write a code sample that loads data from CSV, validates DataFrame with regular expressions, checks identifiers in ‘W-NEW’ or ‘W-OLD’ format, energy efficiency with letters ‘A’ to ‘G’, warranty period and replacement cycle with numerical values in years and at the end creates a report with a count of passed and failed values, generates a PDF with a histogram of results and adds a text description. ⏎

The automated code (Fig.‎ 7.3-6) inside the LLM chat room or in DIE, after copying the code, will validate the data from the CSV -file using the specified regular expressions, create a report on the number of passed and failed records, and then save the validation results as a PDF-file.

This structure of ETL -conveyor, where each step – from data loading to report generation – is implemented as a separate module, ensures transparency, scalability and reproducibility. Presenting the validation logic as easy-to-read Python code makes the process transparent and understandable not only for developers, but also for specialists in data management, quality, and analytics.

The Pipeline -approach to automating data processing allows you to standardize processes, increase their repeatability and simplify adaptation to new projects. This creates a unified methodology for analyzing data, regardless of the source or type of task – whether it’s compliance testing, generating reports, or transferring data to external systems.

Such automation reduces the impact of human error, reduces reliance on proprietary solutions, and increases the accuracy and reliability of results, making them suitable for both operational analytics at the project level and strategic analytics at the company level.

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