Apache Spark is the new processing engine that is powering the Big Data applications around the world. It is taking over from where MapReduce left or from where MapReduce is finding it increasing difficulty to cope with the exacting needs of fast-paced enterprise.
The large amounts of unstructured data and the need for increased speed to fulfill the real-time analytics have made this technology a real alternative for Big Data computational exercises.
|Strength||In-memory processing and iterative computation|
|Data processing||Streaming and batch processing|
Spark has some distinctive advantages compared to traditional processing engine of Hadoop:
Speed : Spark is exceptionally fast to a degree that it can even process data up to 100 times swifter than what MapReduce can achieve due to its ability to exploit in-memory storage rather than using the disk space.
Simplicity : The Apache Spark engine has some very useful APIs that makes it quite easy to work on extremely large data sets. There are a collection of over 100 operators that help to transform data to work with semi-structured data.
Versatile : Today’s Big Data consists of data in various formats and a progressive computational engine needs to be able to work SQL workloads, ability to process streaming data, performing machine learning operations and Spark seamlessly achieves it all.
Suitability for Hadoop YARN : Hadoop YARN is part of the Hadoop 2.0 version and Apache Spark is perfectly suited to working with YARN, sharing a common cluster and providing a consistent service and layout.
Iterative applications : Spark is especially good for working with applications that needs iterative access to data. Spark achieves this with RDD (Resilient Distributed Dataset) which is a read-only data abstraction that lets Spark to delegate smaller workloads to individual nodes for quicker turnaround times.
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The major components of Spark are as below:
Apache Spark Core
This is the fundamental processing engine of the Spark application. All the other components of Spark are directly dependent on the Spark core. Its most important features include the in-memory processing and the data referencing from external data sources.
This is another component of Spark that displays its high-speed computational prowess. It works excessively well with streaming data for providing real-time data analytics. The data is segregating into multiple batches and using the RDD abstraction the data is processed in a massively parallel fashion in a continuous manner to suit the needs of processing streaming data.
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This is the Spark component that creates a new level of data abstraction called the SchemaRDD for working exclusively with both structured and semi-structured data by deploying SQL querying language.
This is the Graph processing capability of Spark that is the amalgamation of iterative graphical computation, exploratory analysis and ETL capabilities. It is possible to view the data as both graphs and collections and also to combine graphs with RDD. It allows for customized iterative graph algorithms using certain specialized APIs.
Spark can also be used for Machine learning applications using its MLlib library that provides a machine learning framework for Spark in a memory-based distributed environment. The Spark MLlib functionalities can be extremely fast when compared to other machine learning frameworks like the Apache Mahout.
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It consists of fundamental processing engine called the Spark Core and this is accompanied with the set of libraries. Its distributed processing engine is written in either of the languages like Scala, Java or Python. There are multiple APIs in order to explore the distributed ETL application development. The multiple libraries written on top of the Spark Core lets perform varied applications like SQL data parsing, deploying machine learning and processing streaming workloads.
It conveniently replaces the Hadoop MapReduce, it goes much further. It has immensely profound implications for the data science community. The MLlib library is extensively deployed for machine learning applications. It increasingly fulfills the needs of the data science domains like classification, clustering, regression, collaborative filtering and dimensionality reduction.
Spark comes with the machine learning pipeline API that is used for high level abstraction for defining the data science workflow. Some of the abstractions provided with Spark ML are Estimator, Transformer, Pipeline and Parameters.
The shortcomings of MapReduce
MapReduce had some serious concerns in the way data is being processed and getting high-speed processing is next to impossible. The reasons for MapReduce’s slower throughput are as below:
- It has a linear dataflow structure on distributed programs
- It follows a lengthy process of reading data from disk, mapping the data with a function, reducing the mapped data, and storing the reduced data again in the disk.
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The RDD abstraction in Spark is the solution
The Spark RDD works in a fundamentally different way. It partitions each dataset in a logical manner so that it can be independently computed on different nodes of the cluster. The RDD is read-only data record deployed as a partitioned collection. It is highly fault-tolerant and helps for massive parallel processing in order to increase the speeds by several factors.
It is possible to achieve RDD by either of the two methods of deploying parallelization or by referencing external dataset from an external data storage system which could be from HDFS, HBase or other shared data source.
Spark is a newer way of computing Big Data on Hadoop cluster. It is designed in such a way that it can work independently even outside of Hadoop without a hitch. Spark already has its own Big Data computation and cluster management system so it basically uses Hadoop for its storage needs. But even that can be replaced with some other data storage method in order to completely replace its dependence on Hadoop.
According to MapR, Spark is gaining steady groundswell due to some of its distinctive advantages.
It is extremely good for batch processing, deploying iterative algorithms, querying in an interactive manner, and working with streaming data. These are some of the features that sets it apart from MapReduce but one other distinctive advantage that Spark has is that it makes away with managing multiple tools for multiple tasks.
It provides multiple APIs for Java, Scala and Python objects. Thus regardless of the language in which you have your application written it is possible to process the data. Spark also has scores of high-level operators for deploying interactive querying.
The various ways in which Spark is deployed
Standalone: In this deployment the Spark application runs on top of the Hadoop Distributed File System and Spark can work well in coordination with the traditional computing engine which is MapReduce.
Spark in MapReduce: Spark can be deployed inside of MapReduce applications in order to speed up the process of data computation i.e. speed up the Mapping and Reducing functions using the in-built Spark functionalities. Here the Spark shell can be deployed without the need for administrative rights.
Hadoop YARN: In this method the Spark application can work on YARN without any installation or any root access. This way the Spark program is integrated well into the Hadoop ecosystem and the other components can work on top of this stack.
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Applications of Spark in real world scenarios
Today there is a widespread deployment of Big Data. With each passing day the requirements of enterprises increases and therefore there is a need for a faster and more efficient form of processing data. Most of the data is in unstructured format and it is coming in thick and fast as streaming data. MapReduce falls short of handling all these requirements.
Spark helps to use Big Data in real world scenarios like real-time statistics, predictive analytics, working with sensor data, log data processing, fraud detection, and so on. Organizations in diverse fields like marketing, manufacturing, finance, law enforcement and scientific research are hugely benefiting from it.
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What is the right audience to learn Apache Spark?
Apache Spark can be mastered by professionals who are in the IT domain in order to increase their marketability. Big Data Hadoop professionals surely need to learn Apache since it is the next most important technology in Hadoop processing. Other than that even ETL professionals, SQL professionals and project managers can gain immensely if they master Apache Spark. Finally Data Scientists also need to gain in-depth knowledge in order to excel at their careers. Spark is extensively deployed in machine learning scenarios and Data Scientists are also expected to work in machine learning domain making them the right candidates for Apache Spark training.
Increased demand for Spark Professionals everywhere
Apache Spark is seeing widespread demand with enterprises finding it increasingly difficult to hire the right professionals to take on increasingly challenging roles in real world scenarios. It is a fact that today the Apache Spark community is one of the fastest Big Data communities with over 750 contributors from over 200 companies worldwide.
Also it is a fact that Apache Spark developers are among the highest paid programmers when it comes to programming for the Hadoop framework as compared to ten other Hadoop development tools. As per a recent survey by O’Reilly media it was evident that having the Apache Spark skills under your belt can give you a hike in salary of the tune of $11,000 and mastering Scala programming can give you a further jump of another $4,000 in annual salary.
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