Apache Spark is a general-purpose data processing framework built on top of Hadoop MapReduce. It is swift and includes a Machine Learning component. First, this tutorial will introduce you to the framework. Then, you’ll learn about the many uses of Spark. Then, hopefully, you’ll be ready to start using it for your projects.
Apache Spark is a general-purpose data processing framework.
Apache Spark is a general-purpose data-processing framework that simplifies complicated data processing tasks. Designed for distributed, scale-out data processing, Spark runs on millions of servers, on-premise or in the cloud. In addition, its architecture is intended to be developer-friendly, with bindings for popular programming languages such as Java, Python, R, and Scala.
Spark also has a robust interactive shell that allows users to test code without executing the whole job. This makes it easy to try out ad-hoc data analysis without wasting resources. Additionally, Spark is built with a set of powerful higher-level libraries, including SparkSQL, Spark Streaming, MLlib, and GraphX.
A Spark application comprises two parts: a driver and an executor. The driver translates user-written code into a series of tasks and distributes them to the worker nodes. The executors execute the tasks assigned to them. The driver and the executors communicate with each other via a cluster manager.
It is built on top of Hadoop MapReduce.
Hadoop is an open-source data processing framework that consists of two main components: the MapReduce algorithm and the distributed file system (HDFS). The former combines several critical aspects of data management into a single system and runs on clusters of computers. The latter uses an efficient algorithm to process data in parallel across nodes. The algorithms are designed to handle large data sets and can be used for statistical analysis.
Apache Spark, built on top of Hadoop MapReduce and Hadoop Common, allows users to transform extensive data into many smaller ones. Its MLlib module provides tools for pipeline development and model evaluation. The Spark framework is the preferred back-end platform for Apache Mahout, another open-source data processing framework.
It is lightning-fast
Apache Spark is a fast and efficient way to work with unstructured data. It allows businesses to process data at scale. It is lightweight and can run independently or on an existing Hadoop cluster. It also contains a component known as GraphX that makes graph analytics tasks easier to perform. It is a cost-effective alternative to Hadoop as it does not require a large data centre or storage.
GraphX is another component of Apache Spark, which is used for manipulating graphs and performing graph-parallel operations. It provides a uniform tool for ETL, exploratory analysis, and iterative computations. It also provides a library of standard graph algorithms such as PageRank.
It has a Machine Learning component.
Apache Spark has a Machine Learning component, which makes it easy to perform machine learning operations on large data sets. This feature is the key to Apache Spark’s incredible processing speed. It works by splitting data into tasks and executing them in a parallel batch process. It supports various data sources, including text files, SQL databases, and NoSQL stores. In addition, the Apache Spark Core API supports both map and reduce operations and has built-in support for filtering, sampling, and joining data sets.
Apache Spark supports machine learning by including MLlib, a machine learning library that supports many standard machine learning algorithms. This library provides a stable API and mature documentation. It was introduced at the Hadoop Summit 2014 in San Jose, CA, which showcased numerous innovations in the Hadoop ecosystem. Apache Spark 1.0.1 was released on July 11, 2014.
It integrates with Hadoop.
The Hadoop software is a distributed data-processing framework, and Apache Spark is a functional replacement for MapReduce. The two programs share a similar query language, so if you’re familiar with SQL, you’ll know how to use Spark to query your Hadoop data. However, there are a few key differences between SQL and Hadoop. For instance, SQL infers schema as the data is written, while Hadoop assumes it when the metadata is read.
Hadoop and Spark both have scheduling and resource management mechanisms. The former performs resource negotiation through YARN, a popular resource management framework that looks at computing resources holistically. The latter utilizes Mesos, an OS-level scheduler, to manage data and resources.
