Running Pandas Jobs with AWS S3 Data

AWS S3

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics. Amazon S3 is used as the primary storage platform for a data lake built on AWS because of its virtually unlimited scalability and high durability. AWS-powered data lakes, supported by the unmatched availability of Amazon S3, can handle the scale, agility, and flexibility required to combine different data and analytics approaches.

Pandas

Pandas is an open-source Python package that is most widely used for data science/data analysis and machine learning tasks. It provides support for multi-dimensional arrays and data manipulation. Pandas strengthens Python by giving the popular programming language the capability to work with spreadsheet-like data enabling fast loading, aligning, manipulating, and merging, in addition to other key functions. It is prized for providing highly optimized performance when backend source code is written in C or Python. Pandas has become popular because it provides a powerful set of commands and features that are used to easily analyze data. It can be used to perform various tasks like filtering data according to certain conditions, or segmenting and segregating data according to preference. It can efficiently handle large datasets and provides spreadsheet functionality.

With the growing popularity of both AWS S3 for data storage and Pandas for compute workloads, it is unsurprising that many organizations are seeking to run Pandas jobs with AWS S3 data. Kaspian offers a native connector for this operation. Just register your AWS S3 datastore and link your Pandas job; Kaspian's autoscaling compute layer makes it easy to crunch through any data in your cloud with minimal setup or management.

Learn more about Kaspian and see how our flexible compute layer for the modern data cloud is already reshaping the way companies in industries like retail, manufacturing and logistics are thinking about data engineering and analytics.