Running Pandas Jobs with data in your NoSQL Database

NoSQL Database

NoSQL databases are popular because they are highly scalable and flexible, making them ideal for handling large amounts of unstructured data. They can also be used to store structured data. NoSQL databases are designed to handle big data and can be used to store data in a variety of formats including JSON, XML, and BSON. They are also highly available and fault-tolerant. Additionally, they are designed to scale horizontally, allowing companies to add more computing power and storage as needed.

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 NoSQL databases for storage and Pandas for compute workloads, it is unsurprising that many organizations are seeking to run Pandas jobs with data in their NoSQL database. Kaspian offers native connectors for the most popular NoSQL databases. Just register your NoSQL database as a 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.