MLOps: Python or SQL?

Data scientists often prefer using Python and pandas for their data analysis and machine learning tasks. On the other hand, MLOps engineers find the scalability of SQL appealing. The question arises: who should decide which tool to use, or should both be utilized together?

Data scientists gravitate towards Python for several reasons. Firstly, Python offers familiarity since it is the same language they use for building ML models. Secondly, Python provides simplicity, abstraction of complexity, and enhanced readability, making it easier to work with. Additionally, Python’s agility makes it suitable for experimentation, enabling data scientists to iterate quickly. Moreover, Python boasts powerful libraries like pandas, which excel in data transformation tasks. However, pandas is not scalable due to its high RAM resource requirements and slow speed.

SQL has its own merits that attract MLOps engineers. First and foremost, SQL offers efficient resource usage, making optimal use of available resources. It also excels in processing speed, enabling faster data manipulation and analysis. SQL’s scalability is another advantage, as it can handle processing across multiple servers without the need to move data. However, the complexity of feature engineering often results in spaghetti SQL code that is difficult to maintain.

Why not both? Some organizations employ specialist team members who manually translate Python into SQL, but this approach has drawbacks. Manual recoding introduces the risk of errors, especially when different individuals with varying understandings of requirements are involved. Moreover, this manual process slows down experimentation since data scientists have to wait for data engineers to write SQL code. Furthermore, this double-processing approach incurs the expenses of an additional headcount.

You can have the best of both worlds with Python and SQL. By utilizing the open-source FeatureByte SDK, data scientists can declare feature definitions in Python using a syntax inspired by pandas. Behind the scenes, the FeatureByte transforms library automatically generates optimized SQL code to materialize the feature values, bridging the gap between Python and SQL seamlessly.

Automated SQL generation has further benefits, such as protecting your organization from potentially devastating security breaches. Click here to learn more about feature engineering security vulnerabilities.