Running Spark Jobs with MongoDB Data

MongoDB

MongoDB is a source-available cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with optional schemas. MongoDB has become one of the most wanted databases in the world because it makes it easy for developers to store, manage, and retrieve data when creating applications with most programming languages. MongoDB is popular because it's easy to learn and get started. It's highly scalable (auto-sharding) and cost-effective, and it has a flexible data model.

Spark

Apache Spark is an open-source data processing engine that is designed to improve data-intensive applications' performance. It provides a more efficient way to process data, which can be used to speed up the execution of data-intensive tasks. It lets you process big data sets faster by splitting the work up into chunks and assigning those chunks across computational resources. Spark has been called a "general-purpose distributed data processing engine" and "a lightning-fast unified analytics engine for big data and machine learning". It provides development APIs in Java, Scala, Python, and R, and supports code reuse across multiple workloads, batch processing, interactive SQL queries, streaming analytics, machine learning, and graph processing. Spark is especially used to access and analyze social media profiles, call recordings, emails, etc. This helps companies make correct business decisions for target advertising, customer retention, fraud detection, etc.
With the growing popularity of both MongoDB for data storage and Spark for compute workloads, it is unsurprising that many organizations are seeking to run Spark jobs with MongoDB data. Kaspian offers a native connector for this operation. Just register your MongoDB datastore and link your Spark 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.

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