What is Apache spark?
Apache Spark is a unified analytics engine for large-scale data processing. Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools. These two qualities are key to the worlds of big data and machine learning
At a fundamental level, an Apache Spark application consists of two main components: a driver, which converts the user’s code into multiple tasks that can be distributed across worker nodes, and executors, which run on those nodes and execute the tasks assigned to them. Some form of cluster manager is necessary to mediate between the two.
Spark can run in a standalone cluster mode that simply requires the Apache Spark framework and a JVM on each machine in your cluster. However, it’s more likely you’ll want to take advantage of a more robust resource or cluster management system to take care of allocating workers on demand for you.
Speed: Run workloads 100x faster.
Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. It relies on Resilient Distributed Dataset (RDD) that allows Spark to transparently store data on memory and read/write it to disc only if needed. This helps to reduce most of the disc read and write time during data processing.
Ease of Use: Write applications quickly in Java, Scala, Python, R, and SQL.
Spark allows you to write scalable applications in Java, Scala, Python, and R. So, developers get the scope to create and run Spark applications in their preferred programming languages. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells.
Generality: Combine SQL, streaming, and complex analytics.
Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. The best feature is that you can combine these libraries seamlessly in the same application.
Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. This aspect of Spark makes it an ideal tool for migrating pure Hadoop applications, provided the apps use case is Spark friendly.
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