Running Pandas Jobs with Redshift Data

Redshift

Amazon Redshift is a popular data warehousing solution that can handle data on an exabytes scale. It is useful for processing real-time analytics, combining multiple data sources, log analysis, or more. Redshift uses parallel-processing and compression to decrease command execution time. This allows Redshift to perform operations on billions of rows at once. This also makes Redshift useful for storing and analyzing large quantities of data from logs or live feeds through a source such as Amazon Kinesis Data Firehose.

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

Get started today

No credit card needed