What is Apache Spark?

Apache spark is a powerful, open-source processing engine for big data analytics that offers speed, processing power and developer-friendly APIs. It can be used with a range of different tools to address a broad spectrum of business use cases for batch processing, streaming and machine learning.

At its core, Apache Spark uses Resilient Distributed Datasets (RDD) to execute an application across a cluster of machines. RDDs are fault-tolerant collections of elements that are divided into logical partitions and worked on in parallel by worker nodes, hiding the process and location from an end user. Spark works with a cluster manager — either its own standalone cluster mode or more comprehensive solutions like Hadoop YARN, Kubernetes and Mesos — to distribute jobs and manage execution.

In addition to its in-memory processing capabilities, Apache spark also supports a variety of data repositories including the Hadoop Distributed File System (HDFS), NoSQL databases and relational data stores such as Apache Hive. It can also be used for conventional disk-based processing if the data sets are too large to fit into memory.

One of the biggest reasons for the popularity of apache spark is its ability to support multiple workloads within a single application. This includes SQL queries, streaming data, and machine learning with MLlib and graph data processing with GraphX. It can run in a variety of programming languages, such as Java, Python and Scala, giving developers the flexibility to choose a language best suited for their work.

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