Hadoop And Apache SparK: Which Is Suitable for Your Domain of Work?

Let us figure out that which Big Data Framework is more useful for your kind of work. For this we need to know their individual features, analyze their strengths and weaknesses by comparing both on different parameters.

What is Hadoop?

Hadoop is comprised of two components I.e. HDFS and Yarn It is a framework which allows storing of Big Data for parallel processing, in distributed environment.

First Component: HDFS

HDFS is a single unit where Big data is stored. It creates abstraction of resources where the data is stored and distributed across multiple nodes. This kind of architecture is known as Master-Slave, where Namenode is master node and Datanode is slave.

Let us know about Namenode :

As master node, it maintains and manages the DataNodes or slave nodes by recording the metadata from files stored in cluster. Thus, recording every single change to the metadata of file system.

Upon deletion of a file in HDFS, Namenode records it in the Editlog. It receives a block report from all DataNodes in cluster.It ensures that DataNodes are live, recording information about all blocks of HDFS and the nodes where the blocks are stored.

Let us know about DataNode:

DataNode stores the actual data and run on slave machines. They create blocks, delete blocks and replicate them on basis of decision of NameNode. They respond to read and write commands of the clients.

Second Component: Yarn

Yarn has two daemons, one is Resource Manager and other is NodeManager. It is responsible for every processing activity. It allocates resources and schedules tasks.

Role of Resource Manager

As the name suggests, it manages resource. It runs on master machine. Resource Manager is cluster level component I.e one for every single unit of a cluster. It also schedules applications that run on top of Yarn

Role of NodeManager

NodeManager is node level component (one for every single node). It runs on every slave machine. It monitors resource utilization in every container which is managed by it. Moreover, it also tracks node health and manages log. It maintains continuous communication with the ResourceManager for regular updates for both to be on the same level. One can use HDFS for parallel processing using MapReduce.

What is Apache Spark?

In distributed computing environment, Apache Spark framework is used for real time data analytics. It helps in increasing speed of processing of data for in-memory computations. It has high processing power needs.

What is Resilient Distributed Dataset (RDD)?

RDD is collection of objects which are immutable distributed. Every dataset consists of logical partitions, computed on varied cluster nodes. It has user-defined classes and contain various kind of objects like Scala Objects, Python or Java,

Various Components of Apache Spark:

1. Spark Core

Base engine for large-scale data processing of parallel and distributed data. Additional Libraries built atop core allow different workloads for streaming, SQL, and machine learning. It helps in

a)    Memory Management

b)   Fault Recovery

c)    Scheduling

d)   Distributing jobs

e)    Monitoring jobs

f)     Interacting with storage systems

1. Spark Streaming

Used for processing real-time streaming data, it is a useful addition to core Spark API.

1. Spark SQL

It is used for relational processing along with functional programming API. It supports querying data through SQL or Hive Query Language. Those who work on Relational Data Base Management System, Spark SQL is easy to use with some new and advanced features for processing data.

1. GraphX

Spark API version which is used for computations that involve graphs and equivalent. The higher version is an extension of Spark RDD abstraction. It has introduced Resilient Distributed Property Graph which is a directed multigraph, wherein each vertex and edge has properties attached to it.

1. MLib (Machine Learning Library)

This is used for performing machine learning in Apache Spark.

While you enroll in Apache Spark training in Delhi or Big Data training in Delhi at an IT institute, you will learn about the high -level libraries like SQL, Scala, Python, Java and so on are all included in Apache Spark. Thus, you shall learn to manage the complex workflow with the help of these libraries which provide avenues for seamless integrations.

Choosing one of the two: Apache Spark or Hadoop?

1) Performance

Apache Spark with its in memory processing capability, produces results very fast. It makes use of the disk space for storing data if it runs out of memory. Thus, it helps in delivering real-time analytics.It can be used for:

A) Credit Card Processing System

B) Machine Learning

C) Security Analytics

D) Internet of Things

Hadoop, on the other hand, gathers data continuously from various sources. It stores all kinds of data across distributed network. MapReduce does not do real -time processing and uses batch processing. Yarn is used for parallel processing of distributed dataset. 

The processing for both big data frameworks, Hadoop and Apache are different. Thus, comparison is not possible.

2) Ease of Use

Spark has a user- friendly API for Python, Scala, Java and Spark SQL. Spark SQL functions similarly as SQL, thus easy for SQL developers to use and learn it.

Integrating Hadoop with the use of multiple tools like Flume, Sqoop etc, it can be fetched with data easily.

3) Costs

Since Hadoop and Spark are both open source software, so no cost is involved for the software. But infrastructure cost cannot be avoided. Design of both products is such that they require commodity hardware with low TCO to run and function properly.

In terms of storage and processing, Hadoop uses disk-based memory. Thus, it requires faster disks and huge volume of space along with multiple system for distributing disk I/O.

Apache Spark’s in-memory processing needs ample memory. It requires standard amount of disk with standard speed. Apache requires RAM for execution process in memory, thus incurring more cost. But with requirement of fewer systems, Spark costs per unit computation is reduced.

4) Data Processing

Yarn studies data in batches. Spark also does the same thing but it makes use of in-memory processing for optimizing steps.

5)Fault Tolerance

Spark Apache and Hadoop have different approaches to fault tolerance

There is more to this Big Data network industry. Once you enroll for the Big Data Training course in Delhi with any Big Data Training Institute in Delhi, you shall study in detail about more factors that will help you to understand make decisions based on analysis about which technology to use in a particular set up.