Spark: Data Analysis Explained

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Spark: Data Analysis Explained

Apache Spark is a powerful, open-source processing engine for big data sets, built around speed, ease of use, and sophisticated analytics. It was developed in 2009 in UC Berkeley’s AMPLab, and its core purpose is to provide an easy and speedy interface for programming clusters, which is also highly optimized.

Spark has the ability to support a wide range of tasks that businesses often need to perform on large datasets, such as SQL queries, streaming data, machine learning and graph processing. It’s designed to perform both batch processing (similar to Hadoop) and new workloads like streaming, interactive queries, and machine learning.

Spark Architecture

Spark’s architecture is based on the concept of distributed computing. It means that the processing of data is done parallelly and distributed across an array of nodes. This parallel processing enables Spark to run tasks quickly by leveraging the power of thousands of nodes.

At the heart of Spark’s architecture is the idea of a Resilient Distributed Dataset (RDD). An RDD is a fault-tolerant collection of elements that can be processed in parallel. RDDs are immutable, partitioned collections of records that can be worked on in parallel. They can be created by loading an external dataset or by transforming an existing RDD.

Spark Core

Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities. Spark Core is the base engine for large-scale parallel and distributed data processing. It is responsible for memory management and fault recovery, scheduling, distributing and monitoring jobs on a cluster & interacting with storage systems.

Spark Core also contains the API that defines resilient distributed datasets (RDDs), which are Spark’s main programming abstraction. RDDs represent a collection of items distributed across many compute nodes that can be manipulated in parallel. Spark Core provides many APIs for building and manipulating these collections.

Spark SQL

Spark SQL is a Spark module for structured data processing. It provides a programming interface for data manipulation using structured and semi-structured data. It also provides a programming abstraction called DataFrames and can also act as a distributed SQL query engine.

Spark SQL integrates relational processing with Spark’s functional programming API. It supports querying data either via SQL or via the Hive Query Language. For those of you familiar with RDBMS, Spark SQL will be an easy transition from your earlier tools where you can extend the boundaries of traditional relational data processing.

Spark Streaming

Spark Streaming is a Spark component that enables processing of live streams of data. Examples of data streams include log files generated by production web servers, or queues of live event data. Spark Streaming provides an API for manipulating data streams that matches the RDD-based functional programming API.

Spark Streaming supports real time processing of data and produces results in batches. The data collected is not processed immediately, but it is divided into batches of pre-defined intervals and then processed. It can handle high velocity and high volume data with low-latency processing that can scale to hundreds of nodes.

Spark MLlib

MLlib stands for Machine Learning Library. Spark MLlib is used to perform machine learning in Apache Spark. It makes machine learning scalable and easy with common learning algorithms and use cases like clustering, regression filtering, dimensional reduction, and alike.

MLlib allows data scientists to focus on their data problems and models instead of solving the complexities surrounding distributed data (such as infrastructure, configurations, and so on). It contains high-quality algorithms that leverage iteration, and can yield better results than the one-pass approximations sometimes used on MapReduce.

Spark GraphX

GraphX is a component in Spark for graphs and graph-parallel computation. At a high level, GraphX extends the Spark RDD by introducing a new Graph abstraction: a directed multigraph with properties attached to each vertex and edge. To support graph computation, GraphX exposes a set of fundamental operators (e.g., subgraph, joinVertices, and aggregateMessages) as well as an optimized variant of the Pregel API.

Furthermore, GraphX includes a growing collection of graph algorithms and builders to simplify graph analytics tasks. GraphX enables the users to view the same data as both graphs and collections, transforming and joining graphs with RDDs efficiently and expressing iterative graph computation within a single computation stage.

Spark Cluster Managers

In a Spark application, the driver program runs the main() function and creates a SparkContext. This SparkContext can connect to several types of cluster managers (either Spark’s own standalone cluster manager, Mesos or YARN), which allocate resources across applications.

Once connected, Spark acquires executors on nodes in the cluster, which are processes that run computations and store data for your application. Next, it sends your application code (defined by JAR or Python files passed to SparkContext) to the executors. Finally, SparkContext sends tasks to the executors to run.

Spark Standalone

A standalone scheduler is the simplest way to get started with Spark. It’s easy to set up and requires no extra dependencies. While it lacks the advanced features of Mesos and YARN, it is suitable for many workloads when you want to keep things simple.

In standalone mode, Spark’s cluster manager can be used to create a cluster. It is easy to set up and is excellent for testing purposes. The standalone mode offers a web-based user interface to monitor the cluster.

Apache Mesos

Apache Mesos is a cluster manager that provides efficient resource isolation and sharing across distributed applications. Mesos is a flexible, scalable, and efficient distributed systems kernel. It was developed at the University of California at Berkeley.

Mesos supports a wide range of resource isolation technologies, including Linux Containers, Docker, and others. It provides APIs for resource management and scheduling across entire datacenter and cloud environments.

Hadoop YARN

YARN stands for Yet Another Resource Negotiator. It is a resource management layer of Hadoop. YARN is often used with Hadoop installations, but it’s capable of running Spark applications, too.

YARN has been described as a large-scale, distributed operating system for big data applications, and it allows multiple data processing engines to handle data stored in a single platform. YARN provides its own scheduling and resource management capabilities, so it can manage resources for applications running on it.

Spark Applications

Spark applications consist of a driver program, which runs the user’s main function and executes various parallel operations on a cluster. The main abstraction Spark provides is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel.

Spark applications can be written in Java, Scala, or Python. They are built using the SparkContext object, which coordinates and monitors the execution of tasks. Spark applications can be run on a local machine or distributed across a cluster.

Spark in Data Analysis

Spark is widely used in data analysis due to its ability to handle large volumes of data efficiently. It can process data from a variety of sources, including Hadoop Distributed File System (HDFS), Cassandra, HBase, and Amazon S3.

Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It offers over 80 high-level operators that make it easy to build parallel apps, and it can be used interactively from the Scala, Python, R, and SQL shells.

Spark in Machine Learning

Spark’s MLlib library provides various machine learning algorithms that can be used for classification, regression, clustering, collaborative filtering, dimensionality reduction, and more. It also includes utilities for linear algebra, statistics, data handling, and more.

MLlib allows data scientists to focus on their data problems and models instead of solving the complexities surrounding distributed data (such as infrastructure, configurations, and so on). It contains high-quality algorithms that leverage iteration, and can yield better results than the one-pass approximations sometimes used on MapReduce.

Spark in Real-time Processing

Spark Streaming enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Data can be ingested from many sources like Kafka, Flume, Kinesis, or TCP sockets, and can be processed using complex algorithms expressed with high-level functions like map, reduce, join and window.

Furthermore, processed data can be pushed out to filesystems, databases, and live dashboards. In fact, you can apply Spark’s machine learning and graph processing algorithms on data streams. This makes it a powerful tool for real-time analytics.


Apache Spark is a powerful tool for data processing and analysis. Its ability to handle large data sets, support for multiple programming languages, and ease of use make it an essential tool for any business that works with big data.

Whether you’re performing complex data analysis, building machine learning models, or processing real-time data streams, Spark provides the tools and flexibility you need to get the job done. Its robust architecture and wide range of capabilities make it a go-to solution for big data processing.