Running Pandas Jobs with PostgreSQL Data

PostgreSQL

PostgreSQL (a.k.a. Postgres) is a free and open-source relational database management system that is widely used by developers. It is highly customizable and has many useful features for developers. PostgreSQL is one of the most widely-used free and open-source relational database management systems that focus primarily on SQL compliance and extensibility. PostgreSQL offers true community-driven development, extensibility, strong SQL compliance, and useful features such as table inheritance and function overloading.

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 PostgreSQL for data storage and Pandas for compute workloads, it is unsurprising that many organizations are seeking to run Pandas jobs with PostgreSQL data. Kaspian offers a native connector for this operation. Just register your PostgreSQL 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.

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