1 Introduction

In the last articles I have intensely dealt with the topic ETL.

An introduction to this topic is worthwhile for the following reasons:

• Modularity - better coding
• Flexibility
• Easier for other data scientists to read the code
• Easier error avoidance
• Automation
• …

2 Roadmap for ETL

At the beginning of the series of lectures I showed basically how to call .py files, which are located in different directories, in a notebook.

You can find this post her:

• “ETL - Read .py from different sources”

Furthermore I have designed the following variants of pipelines. Each can be developed further as desired. Here is the corresponding legend in advance:

3 Conclusion

The object-oriented programming used in the creation of ETLs is extremely helpful in everyday coding. I therefore advise everyone to learn this kind of programming!

Here again clearly listed all links about ETL:

• “ETL - Read .py from different sources”
• “Simple Pipeline”
• “Pipeline with join”
• “Pipeline with join2”
• “Pipeline with intermediate storage”