Spark Vs Hadoop: The New Age of Big Data Analysis

Both Spark and Hadoop are software frameworks from the Apache Software Foundation. These frameworks are ideal tools to manage big data. To be precise, there’s no proper threshold to define big data. But in simple terms, one can understand big data to be a dataset which is high in volume, the velocity is such that it cannot be easily stored and processed with a single computing system.

According to Wikibon, the big data market is predicted to reach USD 60 billion in spending by the end of 2020, from USD 27 billion in 2014. The statistics depict a clear picture stating the demand for big data professionals will keep rising. The increasing need for professionals to process data is because we can see how data keeps increasing with each passing day. Imagine the fact that within two years’ 90 percent of data was generated and these numbers are bound to accelerate from 4.4 zettabytes in 2018 to 44 zettabytes by the end of 2020.

Let us talk in brief about the differences between Spark and Hadoop – their importance in big data analysis.

Spark

Spark, a software framework to help to process big data. With the help of in-memory processing, the process in big data in fast. It also acts like a distributed data processing engine and it does not have a storage system like Hadoop which is why it needs a storage platform like HDFS (Hadoop Distributed File System). Being a big data professional, it is likely to acquire knowledge and understanding in every aspect that concerns big data. This storage system can run on even on local mode as well as cluster mode. It also supports programming languages like R, Python, Scala, and Java.

It follows a master-slave architecture. Besides the master node and slave node, Spark architecture consists of a spark manager that acquires and allocates resources to run a task.

In the master node, there exists a driver program responsible to create the Spark Context. The Spark Context is a gateway for the execution of spark application. The Spark Context further breaks into other tasks jobs and distributes them further to the slave nodes called the Worker nodes. And inside the Worker nodes, exists the executors that help in the execution of the tasks.

Both the cluster manager and driver program communicate with each other so that the allocation of resources is in place. Then the cluster manager launches the executors and the driver program sends the tasks to the executors and monitors their end-to-end execution.

Now if these worker nodes can be increased, the task will be divided into different partitions making the execution much quicker.

Hadoop

Hadoop is a software framework that is used for data storage and big data processing. These large datasets are further broken down into smaller pieces and process them saving huge amounts of time. Hadoop is a processing system and a disk-based storage system.

Now the distributed storage processing system can scale up to thousands of machines even from a single server increasing the data storage capacity and making the computation of data run faster.

For instance, a single machine won’t be able to handle 100GB of data. However, if the same data is split into 10GB, then 10 machines can process them parallelly.

In Hadoop, we have multiple machines that are connected are collectively known as a single system.

Hadoop composes of two components:

1. MapReduce – is a programming framework that is used to process big data. MapReduce helps divide large datasets into smaller chunks which can further ‘map’ task processes parallelly that produces key-value pairs to be the output. Now the output of the Mapper is the input for ‘reduce’ task in a way where all the key-value pairs and the same key go to the same Reducer. Further on, the Reducer aggregates these sets of key-value pairs into smaller sets of key-value which comes out as the final output.
2. HDFS – the Hadoop Distributed File System is the storage system of Hadoop that has a master-slave architecture. This master-slave architecture composes of a single master server called the ‘NameNode’ and other multiple slaves are called the ‘DataNodes’. Now the NameNode and DataNodes form a cluster, however, there are possibilities of having multiple clusters in HDFS.

A big data professional may often get confused about whether to choose Hadoop or Spark as a big data framework. While both frameworks cannot be compared, they still share similar uses. However, Spark is said to have taken over Hadoop to be the most active open source big data project.

Closing remarks

Although at a certain point in time, many may say that Spark is the default choice for any big data application. However, it is not always the same case with every project. MapReduce has managed to pave its path into the big data market for businesses that require huge data sets to be brought under control by commodity systems.

All in all, Spark and MapReduce share a symbiotic relationship. Whereas we can see Hadoop possess features that Spark does not vice versa. Precisely, the big data scenario would be to have both Spark and Hadoop work together on the same level.