Apache Spark vs. Apache Flink: A Comprehensive Comparison
Apache Spark and Apache Flink are two popular open-source data processing frameworks that have gained widespread adoption in recent years. While both frameworks share some similarities, they have distinct differences in their architecture, programming model, and use cases. In this article, we will provide an overview of Apache Spark and Apache Flink, and compare their features to help readers understand the differences between these two powerful tools.
What is Apache Spark?
Apache Spark is a unified analytics engine that provides a common framework for batch processing, stream processing, machine learning, and graph processing. It was originally developed at the University of California, Berkeley, and donated to the Apache Software Foundation in 2013. Spark has a wide range of use cases, including data warehousing, ETL, machine learning, and stream processing.
What is Apache Flink?
Apache Flink is a distributed processing engine that focuses on real-time data processing and stateful stream processing. It was originally developed at the University of California, Berkeley, and donated to the Apache Software Foundation in 2014. Flink has a scalable architecture that can handle large-scale data processing tasks with ease. It provides low-latency…
