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Spark is a cluster computing engine of Apache and is purposely designed for fast computing process in the world of Big Data. Spark is Hadoop based efficient computing engine that offers several computing features like interactive queries, stream processing, and many others. In memory cluster, computing offered by Spark enhances the processing speed of the applications. Apache Spark has a huge workload that includes the batch application processing, interactive query processing, and iterative algorithms that results in decreasing the burden of managing separate tools. This article discusses Apache Spark terminology, ecosystem components, RDD, and the evolution of Apache Spark.
Let us discuss on each of the concepts one by one throughout the article.
Apache Spark is nothing but just a sub-project of Hadoop. It was developed in AMPLab by Matei Zaharia in 2009. Under BSD license, Spark was declared open source in the year 2010. In 2013, Apache Software foundation adopted Spark and since February 2014, it has become a top-level Apache project.
In several features, Spark is quite ahead from Hadoop that makes it high in demand.
Speed -Speed is the major reason for its popularity and it offers 100 times faster processing speed than Hadoop. Also, it is cost-effective as it uses a few numbers of resources only.
Compatibility - Spark is compatible with the resource manager and runs with Hadoop just like MapReduce. Other resource managers like YARN and Moses are also compatible with Spark.
Real-time Processing–The other reason for the popularity of Spark includes real-time processing in batch mode. It remains high in demand due to in-memory processing feature.
Faster computation and easy development are offered by the Spark but without proper components,this is not possible. So, let’s discuss all of the Spark components one by one. Spark has following components that are discussed below:
Read: Scala VS Python: Which One to Choose for Big Data Projects
All of the Spark functionalities are built upon Apache Spark Core. It is basically underlying general execution and processing engine. It can refer to the external storage system’s datasets and provides in-memory computation features.
A new data abstraction is offered by Spark Core component and this abstraction is called Schema RDD. Apache SQL supports both structured and unstructured data.
Real-time processing is possible just because of Spark Streaming. Streaming analytics is performed by this component of Spark. Data processing is done in batches by dividing the data into mini-batches. DStream, which is a series of RDDs is also performed by this component of Spark through which real-time processing is performed.
Machine learning framework of Spark is known as MLib and it consists of machine learning utilities and algorithms. The libraries include clustering, regression, classification and many other functions. In-memory data processing due to which iterative algorithm performance increases also gets enhanced.
Distributed graph processing framework ‘GraphX’ works on the top of Spark and it enabled the speed of data processing at a large scale.
SparkR is a combination of Spark and R. Different techniques can be explored by SparkR. Spark functionalities are enhanced by merging the R functionalities and Spark scalability features together. So, the above-mentioned Spark components increase its capabilities and the user can easily use it to enhance processing speed and efficiency of the Hadoop system.
Spark has a number of features and that are described below:
Spark has 100 times faster execution speed than Hadoop MapReduce, that is beneficial for large-scale data processing. Through controlled partitioning, Spark achieves this speed. Data is managed through partitioning with the help of which parallel distributed processing can be performed even in minimal traffic.
Read: How to install Hadoop and Set up a Hadoop cluster?
Multiple data sources like JSON, Cassandra, and Hive which are not in the text file, RDBMS tables or CSV formats are supported by Spark. Even pluggable mechanism to access structured data is provided by the Data Source API of Spark SQL. Various data sources can be a part of Spark database.
Spark real-time computation has low latency in nature due to in-memory computing. Spark is basically designed for massive scalability and can support the users having production clusters with thousands of nodes and several computational models.
Evaluation is usually delayed by Apache Spark and is done only when it becomes necessary. Due to this reason, its speed increases a lot. It has been added to a DAG or Direct Acyclic Graph for transformation, which gets executed whenever some data is required by drivers.
Smooth compatibility with Hadoop is available in Apache Spark. The candidates who have started their career with Hadoop can be really helpful for them. It is basically a potential MapReduce replacement. Spark can run on Hadoop cluster for resource scheduling by using YARN.
Spark’s MLib is a machine learning component and it is quite handy in data processing. Due to this reason, Spark component use multiple tools, like one tool for data processing and other for machine learning is eradicated. Spark provides powerful and unified machine learning engine for data engineers and data scientists.
Apache data frames are the collection of distributed data. In data frames, the data is organized in columns and optimized tables. Spark data frames can be constructed from various data sources that include data files, external databases, existing RDDs and Spark data frames.
They are equipped with the following features:
Conceptually when data is organized in columns and the data of data frames can be constructed from various data sources like data files, Hive, external databases or tables.
Read: Frequently Used Hive Commands in HQL with Examples
RDD or Resilient Distributed Datasets are offered by the Spark, which is also a fundamental unit of data. RDDs are basically a collection of data sets that are distributed across various cluster nodes. RDDs can support parallel operations and are immutable by nature. RDDs can be created in Spark by three ways that are through external datasets or by parallel collections or by existing RDDs.
Following operations are offered by RDD:
No changes can be made to RDDs but they can be transformed which results in new RDDs. Few transformations are the map, flatMap, filtersetc.
Action operations are reducedand they return a new value that can be written to the external datasets as well.
This is clear from the above discussion how Spark has dominated the world of Big Data. This powerful framework enhances the capabilities of Big data, system efficiency is also enhanced by Spark framework. Spark has become beneficial for developers at phenomenal speed. This powerful engine provides the ease of use feature and it is taken as one of the popular tools for Big Data. If you are planning to start a career in Apache Hadoop, Spark or Big Data then you are on the right path to pave an established career with JanBask Training right away.
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