Running Pandas Jobs with Elasticsearch Data

Elasticsearch

Elasticsearch is a distributed, RESTful search and analytics engine that can be used to search all kinds of documents. It provides scalable search, has near real-time search, and supports multitenancy. Elasticsearch is popular because it's easy to use and has a lot of features. It's also open-source and has a large community of users. Elasticsearch is a NoSQL database because it doesn't use the traditional SQL relational database model. Instead, it uses a document-oriented model that allows for more flexibility and scalability.

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