CSV as a Database in RPA

 

CSV as a Database: A RPA Developer's Perspective

Introduction

When it comes to data handling in RPA, CSV files often take center stage due to their simplicity and accessibility. While not a traditional database, they can serve as a viable data source for certain automation scenarios. However, understanding the nuances and limitations of treating CSV as a database is essential for effective RPA development.

A Different Approach

Unlike traditional databases, CSV files require a unique connection string. Here’s a typical example:

Driver={Microsoft Text Driver (*.txt; *.csv)}; Dbq=$sFolderPath$; Extensions=csv;

Noticeably absent is the specific file path. Instead, the connection points to a folder. This means you can access multiple CSV files within the same directory using a single connection.

To specify which CSV file to interact with, you use SQL-like syntax:

SELECT * FROM [TCT.csv]

The file name becomes the table, simplifying the process compared to Excel where you'd deal with sheets.

Limitations of CSV as a Database

While CSV files offer convenience, they also come with limitations:

• Updating Challenges: I've encountered difficulties in updating CSV files directly through OLEDB. It seems to be a stubborn challenge that requires further exploration.
• Performance: For large datasets, CSV files might not be the most efficient option due to their flat structure and lack of indexing.
• Data Integrity: CSV files are susceptible to data corruption if not handled carefully.

Conclusion

Using CSV as a database in RPA can be a practical solution for certain scenarios, but it's essential to be aware of its limitations. For complex data manipulation and large datasets, a dedicated database is often a more suitable choice.