Running Spark Jobs with PostgreSQL Data

PostgreSQL

PostgreSQL (a.k.a. Postgres) is a free and open-source relational database management system that is widely used by developers. It is highly customizable and has many useful features for developers. PostgreSQL is one of the most widely-used free and open-source relational database management systems that focus primarily on SQL compliance and extensibility. PostgreSQL offers true community-driven development, extensibility, strong SQL compliance, and useful features such as table inheritance and function overloading.

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