As a powerful processing engine built for speed and ease of use, Spark lets companies build powerful analytics applications. It is the most active big data project in the Apache Software Foundation and just last year IBM announced that they were putting 3,500 of their engineers to work on advancing the project.
One of the most popular Apache Spark use cases is integrating with MongoDB, the leading NoSQL database. Each technology is powerful on its own but together they push analytics capabilities even further by enabling sophisticated real-time analytics and machine learning applications.
Here are just a few Apache Spark use cases that incorporate MongoDB.
Content recommendations. A web analytics platform built on MongoDB provided insight to websites on how their content performed by geography and audience. The platform used Spark’s machine learning algorithm to incorporate that performance data into serving up targeted content recommendations websites users.
Predictive modeling. A global manufacturing company wanted to better predict future warranty returns on their products by analyzing material samples for their production lines. They turned that data into a predictive failure model using Spark Machine Learning and MongoDB.
Targeted ads. A video sharing website uses Spark with MongoDB to place relevant advertisements in front of users as they browse, view, and share videos.
Customer Service. A multinational banking group implemented a unified real-time monitoring application running Apache Spark and MongoDB. The bank wanted to ensure a high quality of service across its online channels, and so needed to continuously monitor client activity to check service response times and identify potential issues.
These Apache Spark use cases represent just a handful of possibles that come from the power turning analytics into real-time action. Learn more about how employing Spark and MongoDB can deliver powerful results for your enterprise. Downloading our white paper today.