In this blog, we will learn about Apache Spark and Hadoop. We will cover their features and limitations. We will also make a proper table comparison. These are the two most popular platforms used for Big Data processing. Let's dive into the world of Big Data.
Let's start the article by introducing Apache Spark and Hadoop.
A brief about Apache Spark and Hadoop
Apache Spark provides a distributed computing engine. It offers in-memory processing ability. It allows faster data analytics and machine learning tasks. It uses RAM to cache and process data. It performs faster than Hadoop. Spark does not have a distributed file storage system. So it does the computations on top of Hadoop's Distributed File System.
Apache Hadoop is an open-source software framework. It provides distributed storage. It can process large data sets of any size. It enables a network of computers to solve various data problems. It is a scalable and cost-effective solution. It stores and processes structured, semi-structured, and unstructured data.
Features of Apache Spark
Apache Spark processes data in parallel across a cluster. The main difference is that it uses memory. Spark uses RAM for caching and processing the data. The engine supports single and multi-node deployment scenarios. It follows master-slave architecture. Each Spark cluster has a single master node to manage tasks. And numerous slaves to perform operations.
The following are the features of Apache Spark:
Spark offers a user-friendly API in various programming languages. It supports Scala, Java, Python, and R.
It provides a built-in interactive shell for quick trials and development.
Spark supports multiple data processing tasks. These include batch processing and interactive queries. It supports streaming and machine learning.
It provides a single unified engine for handling workloads.
Spark includes libraries for advanced analytics. These are Spark SQL, MLlib, GraphX, and Spark Streaming.
Spark offers high scalability. It can scale from a single machine to thousands of nodes. Thus it handles large-scale data processing requirements.
It enables seamless integration with other big data technologies like Hadoop.
Five main components of the Apache Spark Ecosystem
The five main components of the Apache Spark Ecosystem are:
Apache Spark Core
Apache Spark Core provides basic usage and APIs for the following
Distributed data processing.
Memory management.
Fault recovery.
Inter-node communication.
Resilient Distributed Dataset (RDD.) is the fundamental data structure. Spark core used RDD.
Spark SQL
Spark SQL module provides an interface for working with structured and semi-structured data. It creates a communication layer between RDDs and relational databases.
Spark Streaming
Spark Streaming empowers near-real-time processing capacity. It facilitates building streaming analytics products. It intakes real-time data from Kafka, Flume, and HDFS sources.
Machine Learning Library
ML lib is Spark's machine learning library. It provides scalable machine-learning algorithms and utilities. It works with large-scale datasets. It integrates seamlessly with other Spark components.
GraphX
GraphX is a component in Spark that supports processing graph-structured data. It is used for social network analysis and graph-based analytics.
Features of Apache Hadoop
Apache Hadoop offers distributed storage. It processes massive datasets. This framework distributes a huge dataset into smaller packets. These packets distribute across connected computers. They are known as the Hadoop cluster. It splits up the Big Data task across machines. Every computer performs its part in parallel. The end-user sees it as a single task.
The following are the features of Apache Hadoop:
(HDFS) stands for Hadoop's Distributed File System. It allows distributed storage of data across clusters of computers.
It delivers fault tolerance by replicating data across multiple nodes. This ensures data availability in the event of node failures.
Hadoop scales horizontally by adding more machines to the cluster.
Faster processing speed.
Hadoop uses the principle of data locality. The computation takes place on the same node where the data resides. Thus, it minimizing data transfer.
Hadoop is well-suited for batch processing tasks. It can efficiently handle batch analytics and data cleaning tasks.
It is cost-effective. It eliminates the need for expensive specialized hardware.
Four Main Components of the Hadoop Ecosystem
The four main components of the Hadoop Ecosystem are:
Hadoop Distributed File System (HDFS)
HDFS is the distributed file system at the core of Hadoop. It stores and manages large datasets across multiple nodes in a Hadoop cluster.
Yet Another Resource Negotiator (YARN)
YARN is the resource manager of Hadoop. It is responsible for managing resources across the nodes. These resources can be CPU, memory, etc.
Hadoop MapReduce
Map Reduce is a programming model for distributed data processing. It divides a large dataset into smaller chunks. It processes them in parallel across the nodes in a cluster.
Hadoop Common (Hadoop Core)
Hadoop Common provides libraries and utilities. These work with other components of Hadoop. It includes the tools and framework required. It helps Hadoop's distributed computing environment.
Limitations of Apache Spark
Spark has some advantages over Hadoop's Map Reduce engine. Yet, it comes with certain drawbacks.
Pricey hardware: RAM prices are higher than those of hard discs. Spark operations are more expensive.
Nearly Real-time Processing: Spark Streaming allows you to analyse data quickly. But it won't be real-time.
Issues with small files: Spark doesn't cope well with many small datasets like Hadoop.
Limitations of Apache Hadoop
Apache Hadoop alone is far from being all-powerful. It has multiple limitations. Below we list the most significant drawbacks of Hadoop.
Small file problem: Hadoop can't perform in small data environments. Millions of small files will occupy too much memory. It slows the processing.
High Latency: Hadoop’s system can deliver large data batches. It causes a delay between user action and system response. It is unsuitable for tasks requiring real-time data access.
No real-time data processing: Map Reduce performs batch processing only. It doesn't fit time-sensitive data or real-time analytics jobs.
Complex programming environment: Data engineers who worked only with RDBMS need the training to work with Hadoop.
Comparison Table of Apache Spark and Hadoop
Basis Of Difference
Spark
Hadoop
Definition
An Open-source framework for in-memory distributed data processing and development.
An Open-source framework for distributed data storage and processing.
Processing Methods
Batch and micro-batch processing in RAM
Batch Processing using Hard disks.
Cost
More Expensive
Less Expensive
Scalability
Difficult to scale
Easy to scale
Supported languages
Scala, Java, Python, R
Java
Ease of Use
Highly user friendly.
More challenging to use with less supported languages.
Machine Learning
Highly swift in-memory processing. Comes with MLlib.
Slower than Spark. Data fragments can be huge and initiate bottlenecks. Mahout is the primary library.
Used for
Instant processing of live data and quick analytics app development
Delay tolerant tasks involving massive datasets.
Frequently Asked Questions
What is the difference between Spark and Hadoop?
Spark offers faster in-memory processing. It offers a flexible programming model. Hadoop excels in distributed storage of large-scale data. It offers batch processing.
What is the advantage of using Hadoop with Spark?
Hadoop provides a scalable distributed storage platform. It is fault-tolerant. Spark offers faster data processing. It offers a flexible programming model. It creates a powerful combination for big data processing.
Is Spark suitable for real-time processing?
Yes, Spark Streaming enables real-time processing of streaming data. It can intake data continuously. This makes it ideal for near-real-time analytics.
What are the everyday use cases for Spark?
Spark is commonly used for large-scale data processing and real-time analytics. It is also used for machine learning and graph processing.
Can Hadoop work with other data processing frameworks?
Yes, Hadoop can work with other data processing frameworks. It can work with Apache Spark, Apache Flink, etc. This allows more flexibility.
Conclusion
This article provides an overview of Apache Spark and Hadoop. Two prominent frameworks in big data processing. We explored their features and limitations. We highlight their respective strengths and use cases. We looked at the differences between Spark and Hadoop.
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