In this article, we will discuss the utility of the Pandas library in processing data in ETL applications. Key areas of data analysis, data cleansing, and data frame transformations will be touched upon. I will present some of my preferred techniques to efficiently manage memory usage and process large data volumes using this library.
Pandas smoothly handles small datasets, making it easy to process data with a set of convenient commands. However, when it comes to larger data frames (1Gb and more), I typically employ Spark and distributed computing clusters, capable of managing terabytes and petabytes of data. However, the use of these powerful tools often incurs significant cost. This is why, for medium-sized datasets in environments with limited memory resources, Pandas can be a more feasible alternative.
In a previous article, I discussed using generators in Python to process data efficiently. Generators serve as a simple trick to optimize memory usage. Suppose, for instance, that you have a massive dataset stored externally, such as in a database or a large CSV file. For the sake of this scenario, assume that you need to process this 2-3 TB file and apply a transformation to each row of data. However, the service you have available only has 32 Gb of memory. This significantly limits data loading capabilities, preventing the entire file from being loaded into memory for line-by-line processing with Python’s split() operator. A viable solution, then, is to process it row by row, yielding it each time to free up memory for the next row. This creates a continuous stream of ETL data into the final stage of your data pipeline. This can take various forms, such as cloud storage, another database or data warehouse solution (DWH), a streaming topic, and so on.
In summary, advanced techniques make the task of processing and loading data more efficient, particularly in instances of limited resources. Through the use of Pandas and Python generators, it’s possible to manage and process large datasets more effectively, even on systems with limited memory. This presents a cost-effective solution for handling medium-sized datasets and provides more flexibility in your data pipeline design.
