Running Distributed Computing Jobs with data in your Data Warehouse

Data Warehouse

Data warehouses are popular because they allow organizations to store large amounts of data from disparate sources in one place, making it easier to analyze and make decisions based on that data. Data warehouse software allows you to process, transform, and utilize data for decision-making. Data warehouses can provide a stable, centralized repository for large amounts of historical data. They can improve business processes and decision-making with actionable insights, and can increase a business's data strategy return on investment (ROI) and improve data quality.

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 data warehouses for storage and distributed computing for compute workloads, it is unsurprising that many organizations are seeking to run distributed computing jobs with data in their data warehouse. Kaspian offers native connectors for the most popular data warehouses. Just register your data warehouse as a 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.