The data is increasing, and to digest all this data, there are many distributed systems available. Hadoop and Spark are the most famous ones. Choosing one out of two depends entirely upon the requirement of your project. Read more to know which of these two frameworks is right for you.
The critical thing to remember about Spark and Hadoop is they are not mutually exclusive or inclusive but they work well together and makes the combination strong enough for lots of big data applications.
Hadoop World 2011: Hadoop and RDBMS with Sqoop and Other Tools - Guy Harrison...Cloudera, Inc.
As Hadoop graduates from pilot project to a mission critical component of the enterprise IT infrastructure, integrating information held in Hadoop and in Enterprise RDBMS becomes imperative. We’ll look at key scenarios driving Hadoop and RDBMS integration and review technical options. In particular, we’ll deep dive into the Apache SQOOP project, which expedites data movement between Hadoop and any JDBC database, as well as providing an framework which allows developers and vendors to create connectors optimized for specific targets such as Oracle, Netezza etc.
The critical thing to remember about Spark and Hadoop is they are not mutually exclusive or inclusive but they work well together and makes the combination strong enough for lots of big data applications.
Hadoop World 2011: Hadoop and RDBMS with Sqoop and Other Tools - Guy Harrison...Cloudera, Inc.
As Hadoop graduates from pilot project to a mission critical component of the enterprise IT infrastructure, integrating information held in Hadoop and in Enterprise RDBMS becomes imperative. We’ll look at key scenarios driving Hadoop and RDBMS integration and review technical options. In particular, we’ll deep dive into the Apache SQOOP project, which expedites data movement between Hadoop and any JDBC database, as well as providing an framework which allows developers and vendors to create connectors optimized for specific targets such as Oracle, Netezza etc.
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Proc...Agile Testing Alliance
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Processing by "Sampat Kumar" from "Harman". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
Hadoop is getting replaced with Scala.The basic reason behind that is Scala is 100 times faster than Hadoop MapReduce so the task performed on Scala is much faster and efficient than Hadoop.
Quick Brief about " What is Hadoop"
I didn't explain in detail about hadoop, but reading this slides will give you insight of Hadoop and core product usage. This document will be more useful for PM, Newbies, Technical Architect entering into Cloud Computing.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
This is a presentation about big data with Java. In those slides, you can find why big data is so important and some of the tools that are used for creating big data applications like Apache Hadoop, Apache Spark, Apache Kafka and etc.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
The Future of Hadoop: A deeper look at Apache SparkCloudera, Inc.
Jai Ranganathan, Senior Director of Product Management, discusses why Spark has experienced such wide adoption and provide a technical deep dive into the architecture. Additionally, he presents some use cases in production today. Finally, he shares our vision for the Hadoop ecosystem and why we believe Spark is the successor to MapReduce for Hadoop data processing.
Apache Tez : Accelerating Hadoop Query ProcessingTeddy Choi
호튼웍스 아시아 기술 총괄 이사 제프 마크햄 (Jeff Markham) 이 테즈에 대한 소개를 합니다. 테즈는 맵리듀스를 대체하여 하둡의 질의 처리를 가속하는 소프트웨어입니다. 왜 테즈를 만들었고, 어떻게 구성되었으며, 최적화는 어떻게 진행되고, 그 성능은 얼마나 좋아졌는지 전반에 대해 설명합니다.
Spark vs Hadoop: Which Big Data Framework to Choose?Ria Katiyar
This is a comparison presentation between two popular big data frameworks, Hadoop and Spark. Here you will get detailed information about their pros and cons, alongside getting familiar with different factors to consider during ‘Spark vs Hadoop’ battle.
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Proc...Agile Testing Alliance
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Processing by "Sampat Kumar" from "Harman". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
Hadoop is getting replaced with Scala.The basic reason behind that is Scala is 100 times faster than Hadoop MapReduce so the task performed on Scala is much faster and efficient than Hadoop.
Quick Brief about " What is Hadoop"
I didn't explain in detail about hadoop, but reading this slides will give you insight of Hadoop and core product usage. This document will be more useful for PM, Newbies, Technical Architect entering into Cloud Computing.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
This is a presentation about big data with Java. In those slides, you can find why big data is so important and some of the tools that are used for creating big data applications like Apache Hadoop, Apache Spark, Apache Kafka and etc.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
The Future of Hadoop: A deeper look at Apache SparkCloudera, Inc.
Jai Ranganathan, Senior Director of Product Management, discusses why Spark has experienced such wide adoption and provide a technical deep dive into the architecture. Additionally, he presents some use cases in production today. Finally, he shares our vision for the Hadoop ecosystem and why we believe Spark is the successor to MapReduce for Hadoop data processing.
Apache Tez : Accelerating Hadoop Query ProcessingTeddy Choi
호튼웍스 아시아 기술 총괄 이사 제프 마크햄 (Jeff Markham) 이 테즈에 대한 소개를 합니다. 테즈는 맵리듀스를 대체하여 하둡의 질의 처리를 가속하는 소프트웨어입니다. 왜 테즈를 만들었고, 어떻게 구성되었으며, 최적화는 어떻게 진행되고, 그 성능은 얼마나 좋아졌는지 전반에 대해 설명합니다.
Spark vs Hadoop: Which Big Data Framework to Choose?Ria Katiyar
This is a comparison presentation between two popular big data frameworks, Hadoop and Spark. Here you will get detailed information about their pros and cons, alongside getting familiar with different factors to consider during ‘Spark vs Hadoop’ battle.
In the past, emerging technologies took years to mature. In the case of big data, while effective tools are still emerging, the analytics requirements are changing rapidly resulting in businesses to either make it or be left behind
Apache Spark is a lightning-fast cluster computing technology, designed for fast computation. It extends the MapReduce model of Hadoop to efficiently use it for more types of computations, which includes interactive queries and stream processing.
Spark is one of Hadoop's subproject developed in 2009 in UC Berkeley's AMPLab by Matei Zaharia. It was Open Sourced in 2010 under a BSD license. It was donated to Apache software foundation in 2013, and now Apache Spark has become a top-level Apache project from Feb-2014.
This document shares some basic knowledge about Apache Spark.
Apache Hadoop es un framework de software que soporta aplicaciones distribuidas bajo una licencia libre.1 Permite a las aplicaciones trabajar con miles de nodos y petabytes de datos. Hadoop se inspiró en los documentos Google para MapReduce y Google File System (GFS).
Hadoop es un proyecto de alto nivel Apache que está siendo construido y usado por una comunidad global de contribuyentes,2 mediante el lenguaje de programación Java. Yahoo! ha sido el mayor contribuyente al proyecto,3 y usa Hadoop extensivamente en su negocio
Big data is a popular term used to describe the large volume of data which includes structured, semi-structured and unstructured
data. Now-a-days, unstructured data is growing in an explosive speed with the development of Internet and social networks like Twitter,Facebook
& Yahoo etc., In order to process such colossal of data a software is required that does this efficiently and this is where Hadoop steps in. Hadoop
has become one of the most used frameworks when dealing with big data. It is used to analyze and process big data. In this paper, Apache Flume
is configured and integrated with spark streaming for streaming the data from twitter application. The streamed data is stored into Apache
Cassandra. After retrieving the data, the data is going to be analyzed by using the concept of Apache Zeppelin. The result will be displayed on
Dashboard and the dashboard result is also going to be analyzed and validating using JSON
Apache Spark vs. Hadoop Is Spark Set to Replace Hadoop.pdfMounikaPolabathina
Explore Apache Spark, a high-speed data processing framework, and its relationship with Hadoop. Discover its key features, use cases, and why it's not a Hadoop replacement.
Transitioning Compute Models: Hadoop MapReduce to SparkSlim Baltagi
This presentation is an analysis of the observed trends in the transition from the Hadoop ecosystem to the Spark ecosystem. The related talk took place at the Chicago Hadoop User Group (CHUG) meetup held on February 12, 2015.
The Apache Hadoop software library is essentially a framework that allows for the distributed processing of large datasets across clusters of computers using a simple programming model. Hadoop can scale up from single servers to thousands of machines, each offering local computation and storage.
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Hadoop Vs Spark — Choosing the Right Big Data Framework
1. Hadoop Vs Spark — Choosing the Right
Big Data Framework
We are surrounded by data from all sides. It is estimated that by 2020, the digital
universe will be as large as 44 zettabytes—as many digital bits as there are stars in
the universe.
2. The data is increasing and we are not getting rid of it any time soon. And to digest
all this data, there seems to be an increasing number of distributed systems on the
market. Among these systems, a battle that is most famous is Hadoop vs Spark—
frameworks that are often pitted against one another as direct competitors.
When deciding which of these two frameworks is right for you, it’s important to
compare them, based on the few essential parameters. In this blog, we have shed
some light upon such parameters.
3. 1. Performance
Spark is lightning-fast and has been found optimal over the Hadoop framework. It
runs 100 times faster in-memory and 10 times faster on disk. Moreover, it is found
that it sorts 100 TB of data 3 times faster than Hadoop using 10X fewer machines.
The reason that Spark is so fast is because it processes everything in memory.
Particularly, Spark is faster on machine learning applications, like Naive Bayes and
k-means. Thanks to Spark’s in-memory processing, it delivers real-time analytics for
data from marketing campaigns, IoT sensors, machine learning, and social media
sites.
However, if Spark, along with other shared services, is running on YARN, its
performance might degrade and can lead to RAM overhead memory leaks. And in
4. this particular scenario, Hadoop emerges out to be the real hero. If a user has a tilt
towards batch processing, Hadoop is much more efficient than its counterpart.
Hadoop is a big data framework that was never built for lightning speed, it uses
batch processing. Its original aim was to incessantly gather information from
websites with no requirements for this data in or near real-time.
Bottom Line: Both Hadoop and Spark have a different way of processing. Thus, it
entirely depends upon the requirement of the project, whether to go ahead with
Hadoop or Spark in the Hadoop vs Spark performance battle.
Facebook and its Transitional Journey
with Spark Framework
Data on Facebook increases with each passing second. In fact, it is even growing
while you are reading this blog. So, in order to handle this data and visualize it to
make an intelligent decision, Facebook uses analytics. And for that, it makes use of
a number of platforms as follows:
Hive platform to execute some of
Facebook’s batch analytics
Corona platform for the custom
MapReduce implementation
Presto footprint for ANSI-SQL-based
queries
5. The Hive platform discussed above was computationally “resource intensive”. So
maintaining it was a huge challenge. Thus, Facebook decided to switch to Apache
Spark framework step-by-step to manage their data. Today, Facebook has
deployed a faster manageable pipeline for the entity ranking systems by integration
of Spark.
2. Security
Spark’s security is still in its emergence stage, supporting authentication only via
shared secret (password authentication). Even Apache Spark’s official
website claims that, “There are many different types of security concerns. Spark
does not necessarily protect against all things.”
6. Hadoop, on the other hand, has better security features than Spark. The security
benefits—Hadoop Authentication, Hadoop Authorization, Hadoop Auditing, and
Hadoop Encryption gets integrated effectively with Hadoop security projects like
Knox Gateway and Sentry.
Bottom Line: In Hadoop vs Spark Security battle, Spark is a little less secure than
Hadoop. However, on integrating Spark with Hadoop, it can use the security
features of Hadoop.
3. Cost
First of all, both Hadoop and Spark are open-source frameworks, and thus, come
for free. Both use commodity servers and run on the cloud, and seem to have
somewhat similar hardware requirements:
7. So, how to evaluate them on the basis of cost?
Note that Spark makes use of huge amounts of RAM to run everything in memory.
And it is a fact that RAM comes under a higher price tag than hard-disks.
On the other hand, Hadoop is disk-bound. Thus, your cost of buying an expensive
RAM gets saved. However, Hadoop needs more systems to distribute the disk I/O
over multiple systems.
Therefore, when comparing Spark and Hadoop framework on the parameters of
cost, organizations will have to ponder at their requirements.
If the requirement has more tilt towards processing large amounts of historical big
data, definitely, Hadoop is the choice to go ahead with because hard disk space
comes at a much cheaper price than memory space.
On the contrary, in the case of Spark, it can be cost-effective when we deal with
the option of the real-time data as it makes use of less hardware to perform the
same tasks at a much faster rate.
Bottom Line: In Hadoop vs Spark cost battle, Hadoop definitely costs less, but Spark
is cost-effective when an organization has to deal with less amount of real-time
data.
8. 4. Ease of Use
One of the biggest USPs of the Spark framework is its ease of use. Spark has user-
friendly and comfortable APIs for its native language Scala and Java, Python, and
Spark SQL (also known as Shark).
The simple building blocks of Spark make it easy to write user-defined functions.
Moreover, since Spark allows for batch processing and machine learning, it
becomes easy to simplify the infrastructure for data processing. It even includes an
interactive mode for running commands with immediate feedback.
On the other hand, Hadoop is written in Java and has a bad reputation of paving
the way for the difficulty in writing a program with no interaction mode. Although
9. Pig (an add-on tool) makes it easier to program, it demands some time to learn the
syntax.
Bottom Line: In ‘Ease of Use’ Hadoop vs Spark battle, both of them have their own
ways to make themselves user-friendly. However, if we have to choose one, Spark
is easier to program and moreover includes an interactive mode.
Is it Possible for Apache Hadoop
and Spark to Have a Synergic
Relationship?
Yes, it is very much possible and we recommend too. Let’s get into the details on
how they can work in tandem.
Apache Hadoop ecosystem includes HDFS, Apache Query, and HIVE. Let’s see how
Apache Spark can make use of them.
An Amalgamation of Apache Spark
and HDFS
The purpose of Apache Spark is to process data. However, in order to process data,
the engine needs the input of data from storage. And for this purpose, Spark uses
HDFS (not the only option, but the most popular one since Apache is the brain
behind both of them).
10. A Blend of Apache Hive and Apache
Spark
Apache Spark and Apache Hive are highly compatible as together they can solve
many business problems.
For instance, a business is into analyzing consumer behavior. Now for this, the
company will need to gather data from various sources like social media,
comments, clickstream data, customer mobile apps, and many more.
Now, an intelligent move by the organization will be to make use of HDFS to store
the data and Apache hive as a bridge between HDFS and Spark.
11. Uber and its Amalgamated Approach
To process the big data of their consumer, Uber uses a combination of Spark and
Hadoop. It uses real-time traffic situation to provide drivers in a particular time and
location. And to make this possible, Uber uses HDFS for uploading raw data into
Hive, and Spark for processing of billions of events.
Hadoop vs Spark: And the
Winner Is
While Spark is faster than thunder and is easy to use, Hadoop comes with robust
security, mammoth storage capacity, and low-cost batch processing capabilities.
Choosing one out of two depends entirely upon the requirement of your project,
12. the other alternative being combining parts of Hadoop and Spark to give birth to
an invincible combination.
Remember!
“Betweentwoevils,chooseneither;betweentwogoods,chooseboth.”—Tryon Edwards
Mix some attributes of Spark and some of Hadoop to come up with a brand new
framework: Spoop.
Source - https://www.netsolutions.com/insights/hadoop-vs-spark/