Hadoop vs Apache Spark

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Hadoop Vs Apache Spark
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Hadoop Introduction

• Hadoop helps in storing large data sets. It also
  helps in running processes related to
  distributed analytics. Hadoop is a framework that
  is open source and can be freely used. Large data
  sets can be quickly and easily stored using
  Hadoop. Hadoop is an efficient framework it
  does not require large amounts of data transfer.
• Hadoop makes sure that one job is processed at a
  time. Data warehousing is one of the core
  functions of Hadoop. The framework ensures that
  big data applications continue to run in case of
  a failures of individual servers.
• Hadoop is a framework that is highly prefered for
  batch processing. The Hadoop framework is written
  in Java . Developers also use Hive on Top of
  Hadoop for adding SQL compatibility.
• Hadoop can be used without any programming,
  because there are numerous integration services
  available out there.

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Hadoop Advantages
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Scalability

• One of the key advantages of developing with
  hadoop is scalability. Since large data sets can
  be easily stored and distributed, it is highly
  scalable.
• A large number of nodes are made possible by
  Hadoop, ensuring large amounts of data storage
  and distribution. In comparison to traditional
  RDMS, Hadoop is highly scalable.

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Cost Effective

• The big data requirements of today are humongous
  and these requirements can be fulfilled in a cost
  effective manner using Hadoop. The cost of data
  processing is much higher when it comes to
  traditional database management systems.
• The simplified processing of complex data ensures
  that Hadoop is a cost effective framework.

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Flexible Solution

• Operating on different types of data and having
  access to different types of data is possible
  with Hadoop and this makes it a very flexible
  solution. This helps in generating value from all
  sorts of data that is gathered.
• One could use a variety of data sources like
  social media and email etc. to gather as much
  useful data as possible.

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Speed

• Since there is a distributed system of files in
  Hadoop. The processing servers and storage
  servers are the same, making the process
  extremely fast.
• The processing of data is highly efficient using
  the Hadoop framework.

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Reliable

• The higher level of tolerance to faults, is found
  only in Hadoop. Data replication in different
  nodes ensures that a clear backup is available.
• This minimizes the chances of data failure.
  Hadoop is quite a reliable framework and helps in
  avoiding both single and multiple failures.

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Spark Introduction

• Spark, is a tool that works on processing the
  data that has been distributed, using the Hadoop
  framework. The Spark platform has be designed run
  on top of Hadoop. It works as an alternative the
  batch model. It can used for hastening
  interactive queries and processing real time
  data. Spark does not have its own file
  management system, but integrated with one.
• Spark is quite faster than hadoop when it comes
  to processing of data. Spark is different from
  Hadoop because it ensures complete data analytics
  of real time as well as stored data. Spark does
  not have the distributed storage system which is
  an essential for big data projects. Spark is
  also known for its advanced data processing and
  machine learning.

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Spark Advantages
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Faster

• Spark places the data into Resilient Distributed
  Datasets. This data gets stored in the memory
  making it easily accessible.
• Since the data is easily accessed from the
  memory, the MapReduce jobs can be undertaken very
  quickly.

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Real Time Processing

• There is a continuous growth of real time data.
  Processing large quantities of a real time data
  can be a big challenge.
• This can help in processing of logs for live
  streaming sites and also help in fraud detection
  and electronic trading data.

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Using Big Data Effectively

• Big data needs to be used effectively to reach
  the right set of people with the right messaging.
  Big data makes use of very specific audiences to
  bring out the best conversion rate for a retail
  business. Many retail marketers fail to bring out
  the right results for the business because of
  lack of understanding of how to make the data
  usable and how to analyse it.
• Technology has to be fully prepared and used for
  big data usage and integration.

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Processing of Graphs

• Graph processing helps in capturing the
  relationship between data and entities.
• The process helps in analysing social as well as
  advertising data. Machine learning helps in
  carrying out advanced analytics and getting
  consumer understanding.

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Power

• Most companies need 2 systems one for storing
  and streaming data and the other for analyzing
  the data.
• Spark helps in simplified application
  development, maintenance and deployment.

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