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Spark Hadoop tool for Big Data Analytics

Today it is one of the most in-demand processing frameworks or I would say in-memory computing frameworks which are used across the big data industry. Apache spark is an open source in-memory computing framework. You could say data processing engine which is used to process data in batch. It’s also in real time across various cluster computers and it has a very simple programming language behind the scenes that is Scala which is used.

Although if users would want to work on spark  for database assignment help they can work with python they can work with Scala they can work with java and so on even R for that matter so it supports all these programming languages and that’s one of the reasons that it is called polyglot wherein you have good set of libraries and support from all the programming languages and developers and data scientists incorporate spark into their application or build spark based applications to process, analyse query and transform data at a very large scale so these are key features of apache spark. 

Compare Hadoop vs spark

We know that Hadoop is a framework and it basically has Map Reduce which comes with Hadoop for processing data. However processing data using Map Reduce in Hadoop is quite slow. Because it is a batch oriented operation and it is time consuming. Spark can process the same data 100 times faster than Map Reduce for HADOOP assignment help from experts. As it is an in-memory computing framework.

Hadoop performs batch processing and that is one of the paradigms of Map Reduce programming model. It involves mapping and reducing and that’s quite rigid so the intermittent data is written to HDFS. It’s written read back from HDFS and that makes Hadoop’s Map Reduce processing slower. In the case of SPARK it can perform both batch and real-time processing. I would say near real-time processing so when we say real-time or near real-time. It is about processing the data as it comes in.

Use Of Programming Language

The programming language used in Hadoop was Java. Now you could also write it in Scala or in Python. However, if you talk about Map Reduce it will have more lines of code since it is written in Java. It will take more times to execute you have to manage for database assignment help together the dependencies. You have to do the right declarations you have to create your mapper and reducer and Driver classes. However, if you compare spark it has few lines of code as it is implemented in Scala. Scala is a statically typed dynamically inferred language. It’s very concise and the benefit is it has features from both functional programming and object-oriented language. In the case of Scala whatever code is written that is converted into byte codes and then it runs in the JVM.

One of the key features of SPARK is fast processing. So, SPARK contains resilient distributed datasets (RDD) so RDD are the building blocks for SPARK. RDD saves huge time taken in reading and writing operations so it can be 100 times faster than Hadoop. 

Different Type Of Computing

There is a difference between aching and in-memory computing. Caching is mainly to support read-ahead mechanism where you have your data pre-loaded in big data assignment help analytics from top database experts so that it can benefit further queries. However, when we say in-memory computing we are talking about lazy evaluation. We are talking about data being loaded into memory only and only when a specific kind of action is invoked. So data is stored in RAM so here we can say Ram is not only used for processing but it can also be used for storage. We can again decide whether we want that RAM to be used for persistence or just for computing. So it can access the data quickly and accelerate the speed of analytics.

SPARK is quite flexible, it supports multiple languages. As I already mentioned and it allows the developers to write applications in Java , Scala, R or Python. Its fault-tolerance so SPARK contains these rdd’s or you could say execution logic or you could say temporary data sets. Which initially do not have any data loaded and the data will be loaded into rdd’s only when execution is happening. So these can be fault tolerant as these rdd’s are distributed across multiple nodes so failure of one worker node in the cluster will really not affect the rdd’s because that portion can be recomputed so it ensures loss of data. It ensures that there is no data loss and it is absolutely fault tolerant. 

Components of spark 

Spark core is the core component which basically has rdd’s. Which has a core engine which takes care of your processing. Spark SQL, people who would be interested in working on structured data or data which can be structure they would want to prefer using spark SQL and spark SQL internally has components or features like data frames and datasets. Which can be used to process your structured data in a much faster way. you have spark streaming now that’s again an important component of spark which allows you to create your spark streaming applications which not only works on data which is coming or data which is constantly getting generated but also transform the data you could analyse or process the data as it comes in in smaller chunks.

Spark mllib, this is basically a set of libraries which allows developers or data scientists to build their machine learning algorithms so that they can do predictive analytics  or they could build their recommendation systems or bigger smarter machine learning algorithms using these libraries. Spark GraphX, data which naturally has a network kind of flow or data which could be represented in the form of graphs. Data which can be networked together which can have some kind of relationship like Facebook or LinkedIn where you have one person connected to other person or one company connected to other company so if we have our data which can be represented in the form of network graphs then spark has a component called graphx which allows you to do graph based processing.


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