Running Distributed Computing 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.

Distributed Computing

Distributed computing technology refers to a system where multiple computers work together to solve a problem. It allows for parallel processing of data across multiple machines, which can lead to faster processing times. Distributed computing technology has become increasingly popular due to the rise of big data. It allows for the processing of large amounts of data that would be too large for a single machine to handle. Some examples of distributed computing technologies include Apache Hadoop, Apache Spark, and Apache Flink.

With the growing popularity of both Elasticsearch for data storage and distributed computing for compute workloads, it is unsurprising that many organizations are seeking to run distributed computing jobs with Elasticsearch data. Kaspian offers a native connector for this operation. Just register your Elasticsearch datastore and link your Distributed Computing 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.