What is the difference between Apache Spark and Hadoop?

Apache Spark and Hadoop are both open-source distributed computing frameworks. The main difference between the two is that Apache Spark is a fast and general-purpose engine for large-scale data processing, while Hadoop is a batch-oriented distributed computing system designed for large-scale data storage and processing.

For example, Apache Spark can be used to quickly process large datasets in parallel, while Hadoop is better suited for storing and managing large amounts of data. Apache Spark also supports data streaming and machine learning algorithms, while Hadoop does not.

What is Apache Spark?

Apache Spark is an open-source cluster-computing framework. It is a fast and general-purpose engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs.

For example, Spark can be used to process large amounts of data from a Hadoop cluster. It can also be used to analyze streaming data from Kafka, or to process data from a NoSQL database such as Cassandra. Spark can also be used to build machine learning models, and to run SQL queries against data.