Running Pandas Jobs with data in your Data Lake

Data Lake

Data lakes are popular because they provide a consolidated, centralized storage area for raw, unstructured, semi-structured, and structured data taken from multiple sources and lacking a predefined schema. They specialize in ingesting structured, semi-structured and unstructured data and provide mechanisms to easily ingest streaming data in addition to batch loads. Data lakes are open format so users avoid lock-in to a proprietary system like a data warehouse. They are also highly durable and low cost because of their ability to scale and leverage object storage.

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