Apache spark is a powerful, in-memory data processing framework, praised for its speed, scalability and ease of use. It is used in a variety of industries for tasks like stream processing, real-time analytics, machine learning and graph data processing.
The core of apache spark is Resilient Distributed Datasets (RDD) – a fault-tolerant collection of elements parallelized across multiple nodes in the cluster and hidden from the end user. RDDs are the key to Spark’s remarkable processing speeds. Spark supports multiple workloads including SQL queries, real-time streaming, machine learning and graph processing – and developers can seamlessly combine these in the same application. It also supports various programming languages allowing flexibility.
Besides its speed, scalability and ease-of-use, apache spark offers a number of compelling advantages over other big data systems. These include a low cost of raw storage and flexible processing of different kinds of data. It also provides a simple, high-level API to work with data and offers support for emerging technologies like machine learning and graph processing.
However, there are some challenges associated with using apache spark. Some of these are: