Apache Spark- What, Why & How

WHAT - Apache Spark is an open-source, distributed computing system that can process large amounts of data quickly. It is a popular choice for big data processing, and can be used in a variety of applications.

There are several reasons WHY Apache Spark is needed in big data processing:

1. Speed: Spark is designed to process large amounts of data quickly, making it much faster than traditional Hadoop MapReduce. It can process data in-memory, which can be up to 100 times faster than disk-based processing.
2. Flexibility: provides a wide range of libraries and API’s, which make it easy to perform a variety of big data processing tasks, such as data processing, data analysis, machine learning and graph processing.
3. Scalability: It is designed to work in a distributed computing environment, which allows it to scale to process large amounts of data on a cluster of machines.
4. Ease of Use: Spark’s API is designed to be easy to use, which makes it accessible to a wide range of developers, including those with little or no experience with big data processing.
5. Integration: It can be integrated with a variety of other big data tools and technologies, such as Hadoop, Kafka, and cloud computing platforms, which makes it easy to use in a variety of big data processing environments.