Hadoop vs. Spark: A Compare and Contrast

Hadoop vs Spark Apache

Hadoop vs. Spark: A Compare and Contrast

The two eminent platforms for Big Data processing are Hadoop and Spark. Both Spark and Hadoop help us to deal with the immense collection of data irrespective of the format – starting from the feedback of the users on the website to the Excel tables, videos, and images. But the major question remains which of the two platforms should one trust their information with? Let’s have a Hadoop vs Spark comparison.

Hadoop vs Spark: Roadmap

In order to compare between Hadoop vs Spark properly, it is important first to divide all the questions into multiple smaller ones:

  • Definition and nature of Hadoop?
  • How does the application work?
  • What are the limitations associated with Hadoop, and how can it be addressed?
  • Why did Spark emerge as a platform?
  • What are the big data tasks that Spark solves efficiently?
  • What are the disadvantages of using Spark?

Here in the article, answers to all the questions are given one by one. Already know some of them? Well, skip them! But we believe we do have something which will surprise you. Let’s start the battle – Hadoop vs Spark…

Apache Hadoop: Why Is It So Popular?

Apache Hadoop is one of the open-source structures that is written in Java for the distribution of storage as well as the processing of large datasets. All the keywords presented here are distributed efficiently as the data quantities within the questions appear to be larger and cannot be easily analyzed and assisted with the help of a single computer. 

Therefore, such a structure helps to provide a better way in dividing the large collection of data into smaller nuggets and present them within the interconnected nodes and computers and eventually make it a Hadoop cluster. Therefore, the Big Data analytics successfully splits up, helping all the machines to perform their bit within the parallel. However, the end-user would see all the splinters as one unit. 

Hadoop helps to hide the complications of the information distributed and therefore offers an engrossed API in order to get the direct approach to the functions of the system and the benefits associated with it:

  • Scalability: One can easily add brand new nodes to the collection and scale it up from one computer as proof to multiple other machines. There is no limit to storage capacity in Hadoop. 
  • Versatility: One can easily grasp data in Hadoop from multiple sources as well as its various formats, which means it can be both unstructured and structured. It is not necessary to archive the data stacking. 
  • Cost-effectiveness: Hadoop is a cost-effective application and is cheaper when compared to others. It is commodity hardware that eventually makes it more affordable in the market. 

Fail-safe composition: Hadoop replicates data in order to protect the data loss in any node failure scenario.

Hadoop Framework and Functions

There are two systematic ways to implement Hadoop – it can be done as both multi-node clump or single-node clump. Here, the substructure is set up as a virtual machine, which is more suitable for evaluating or in the test phase. 

In the other scenario, all the nodes run on an individual machine that is virtually assisted. Big Data includes multiple computing units, and its reading specifically deals with a multi-node option of deployment. 

Master and slaves of Hadoop nodes:

Master nodes coordinate key functions of the application: processing the data in a parallel way and storing it. Physically it needs a strong and secured resource of hardware that is available in the market. 

Slave nodes are majorly the nodes that are required to store as well as run the data computations as per the instruction provided by the master node.

A client Node is also popularly known as the Gateway Node, which acts as a connection between both outside networks as well as cluster networks. It is not a part of a master-slave prototype. It is responsible for loading the information into the cluster, defining it and its need to process to retrieve the output. 

Hadoop clusters have the following layers:

  • The storage layer which is established by the native system of Hadoop – HDFS
  • The layer of resource management introduced by YARN
  • The layer of processing is known as MapReduce. 

HDFS: The layer of storage

It is significantly known as the backdrop framework of Hadoop and manages the information, which is split into numerous blocks with the application, and it can be changed to a configuration file. 

The principle of HDFS is to “write once, read many times.” Therefore, if a file is stored inside the machine, it cannot be modified but can definitely be analyzed for multiple purposes. This approach helps to retrieve the data faster and in a proper way. All the blocks of HDFS have replicated automatically in various nodes to make sure there is tolerance. In case data is deleted, one can easily get access to the backup. There are three recommended replicas with a single copy on the same node and two copies within a similar rack.

Master-slave structure of HDFS

The Master Node of HDFS is also known as Name Node, which enables to keep the data involving critical information of the files as well as keep track of the capacity and the volume of the information that is being transferred. 

Multiple Worker Nodes: It is a space that contains large files. After every three seconds, the signal from the worker is sent to the master to inform that the work is under control and can be accessed easily.

YARN: A layer of resource management

YARN helps to check the usage of the CPU, which is the brain of a computer, the disk space, memory, and thereby allocates the resources that are needed to run the application as well as schedule the work based on the requirement of the application.

Structure of YARN

The resource manager delivers as a Master Node and is recognized as the head of the resource system. 

Multiple salves: They help monitor the resources related to the virtual machine and report the results to the designated source.

MapReduce: A layer of processing

MapReduce is considered the ultimate destination for batch processing when the files are collected in a period of time and eventually handled within a single group. 

The whole job is therefore divided into two phases- map and reduce. The map handles sorting, filtering, and splitting, and the reduce stage summarizes and generates the result. 

Limitations of Hadoop

There are various limitations of Hadoop, and below listed are a few of them:

  • Problem with small files: Hadoop is not suitable for small information. HDFS is not capable of supporting the random evaluation of small files as it has been designed as a high-capacity program. For each data, Name Node is required to store the metadata by including the proper names and locations. 
  • Processing speed is slow: The tasks in Hadoop need a lot of time, after which they can function by the increase in their latency. Information in Hadoop is distributed in clusters which eventually leads to an increase the time and therefore decreases the speed. 

Complicated programming environment: The programming is quite complex, and the engineers are required to be trained enough in order to understand them properly. It is mandatory to know the details of Java in order to get into a deeper route and effective use of the features present in Java.

Hadoop Vs Spark




Basic idea

Framework available from open sources for data storage distribution and data processing

Framework available from open sources for processing of in-memory data and app development

Date of launch



Language supported


java, R, Python, Scala

processing Method

Involves hard disc for reading or writing data through the method of batch processing

Usage of RAm for batch and micro batch processing

Capabilities it comes with

  • HDFS
  • Yarn resource management
  • MapReduce processing engine
  • Spark Core processing engine
  • Spark streaming for near real time streaming
  • Spark SQL for structured data processing
  • GraphX for graph data processing
  • MLib

Best suited for

Huge dataset processing tasks with delay tolerance 

Processing of live and instant data and quick app development 

Real life use case

  • Archived data processing
  • Analysis of log files
  • Predictable maintenance
  • Sentiment analysis
  • Detection of fraud
  • Analysis of telematics
  • Analysis of user behaviour
  • Prediction of stock market trends
  • Risk management

Apache Spark: Definition, Key concepts, Elements & Advantages

Spark is specifically designed to replace MapReduce and involves data in batches and workloads distributed all over the clusters in the interconnected servers. 

Like its predecessor, the engine braces the multi and single-node deployment scenes and the master-slave systems. Every Spark cluster includes a single master driver that is used to deal with the numerous executors to deliver the task. 

The major difference between Spark and Hadoop lies in its data processing ways. 

MapReduce stores halfway result in the form of local discs and then calculates it further. Sparks caches the information in RAM. The biggest possible disk can read far behind the speed of the RAM. However, Spark can run workloads that are 100x times faster compared to MapReduce. When the dataset is complex and has to be saved in disc, Spark enables us to outperform it by ten times. 

There are various key components in Spark that make it superior to Hadoop. They are listed below:

Spark Core and the data structure

The computation engine is the heart of the Spark program and is known as Spark Core. It is hugely responsible for the following:

  • Distribution of the data processing. 
  • Memory management.
  • Task scheduling.
  • Fault recovery 
  • Communications using data repository. 

Spark Core works with the help of Resilient Distributed Dataset (RDD) and is a collective record used to process the parallel data and helps to hide the partitioning from the last user. RDD has easily handle unstructured as well as structured data.

No resource manager and default storage system

Spark is responsible for processing and has no domestic storage system, whereas Hadoop takes over the storing, resource management, and processing. Spark can write and read information from multiple sources and is not limited to HDFS, Apache Cassandra. Spark is compatible with a plethora of various other data that are presented outside of the Hadoop ecosystem. 

After conducting data throughout various servers, Sparks is not capable of controlling resources, including memory and CPU. To do this, it is required to cluster and resource the management. Currently, the structure supports various options like:

  • Standalone- a habitual pre-built branch manager.
  • Hadoop YARN is one of the eminent choices for Spark. 
  • Apache Mesos is used in controlling the resources of the data centers. 
  • Kubernetes is an orchestration platform. 

Running Kubernetes and Spark is benefitted if an organization decides to move its entire tech stack towards the cloud-native fundament.

Native libraries: Spark Streaming, MLlib, GraphX, Spark SQL

Spark Streaming authorizes the internal engine with the real processing abilities as well as facilities establishing the analytics goods. Such a module can absorb live data from Apache Flume, Amazon Kinesis, and various other processes. 

MLlib is known as a library that is also a scalable learning machine and contains algorithms for various other ML tasks like clustering, regression as well as classification. It helps to give tools for creating the ML models, statistics as well as evaluation. 

GraphX helps to deliver various types of programs and helps to run the graphical data. 

Spark SQL helps to create communication between the relational data and RDDs. In addition, it helps in allowing the scientist to raise queries within the Spark programs.

Hadoop vs Spark: Limitations of Spark

  • Costly hardware: Prices of RAM are higher when compared to the hard discs utilized by MapReduce, making the operation far more expensive than Hadoop. 
  • Not real-time processing: The caching of Spark Streaming allows one to analyze information quickly. Yet, it is not a real-time program as the module functions with smaller batches and micro-batches of the programs that are generated over the pre-built interval. A real-time processing tool helps to stream the data during the time it is generated. 
  • Relating to this fact, Spark is not purely suited for IoT solutions. There are various other tools that can be used to analyze Apache’s portfolio. 

Problems with a small file: Sparks hugely lacks behind when it comes to storing small files.

Hadoop vs Spark: Which One To Choose

There is a very crystal-clear advantage of using MapReduce as it can cost-effectively operate the tasks. It is best suited for archived data, which can be determined later. However, Spark is beneficial if speed is looked at over the price. Yet, there are various other factors, such as the availability of the authorities, which also raise the scale. The regulation of Hadoop and Spark hugely depends on the correct type of tools that are amalgamated with. 

Although Spark can work without the assistance of Hadoop, it is usually teamed with the help of HDFS in the form of a data repository. Various other companies use both platforms. One can undertake the heavy operations, and the other can deal with the smaller ones when swift analytic outcomes are required.

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