Hadoop MapReduce Vs Spark: Which big data framework to choose

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Hadoop MapReduce Vs Spark Which big data
framework to choose
MapReduce Vs Spark Amongst multiple large data
frameworks available on the market, choosing the
right is a difficult challenge. A classic
approach to compare the advantages and
disadvantages of each platform is unlikely to
help, as businesses should consider each and
every framework from the perspective of their
particular needs. First of all, what is Spark
and Hadoop MapReduce? Spark This is an open
source big data framework, providing a faster and
more general-purpose data processing
engine. Spark is originally designed for fast
calculations. It also includes a wide range of
workloads - for example, batch, interactive,
repeater, and streaming. Hadoop MapReduce Its
an open-source framework for writing
applications. It also processes structured and
unorganized data stored in HDFS. Hadoop
MapReduce is designed in a way to process a large
amount of data on a group of commodity hardware.
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MapReduce can process data in batch mode. A
quick glance at the market situation Hadoop and
Spark, both are open source projects by Apache
Software Foundation and the two are major
products in big data analytics. Hadoop is leading
a big data market for more than 5 years. As per a
recent market research, installed base of Hadoop
extends to 50,000 customers, while Spark has
only 10,000 installations. However, the
popularity of Spark jumped in recent times to
overcome Hadoop in only one year. A past
installation growth rate (2016/2017) shows that
the trend is still going on. Spark is performing
better than Hadoop with 47 vs 14
respectively. The key difference between Hadoop
MapReduce and Spark
Via quoracdn.net To make comparisons fair and
square, here we will contrast Spark with Hadoop
MapReduce since both of them are responsible for
data processing.
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• In fact, the important difference between them
  lies in the processing approach Spark does it
  in-memory, while Hadoop MapReduce has to read
  from and write to a disk.
• Consequently, the speed of processing varies
  greatly - Spark maybe 100 times faster. However,
  the quantity of processed data is also different
  Hadoop MapReduce is capable of working with far
  larger data sets than Spark.
• Now, let's take a look at the tasks that are good
  for each framework. Tasks Hadoop MapReduce is
  good for
• Linear processing of huge datasets
• Hadoop MapReduce allows parallel processing of
  large amounts of data.
• It breaks a large part into small sizes to
  separately process on different data nodes and
  automatically gathers the results across several
  nodes to return a single result.
• The resulting dataset is larger than the
  available RAM? O that occasion, Hadoop MapReduce
  may beat Spark.
• Economic solutions, if no immediate results are
  expected
• A study considers MapReduce a good solution on
  the condition, speed of processing is not
  important.
• It was observed that data processing done during
  night hours makes sense to consider using Hadoop
  MapReduce.
• Tasks Spark is good for
• Fast data processing
• Spark is faster in terms of In-memory processing
  than Hadoop MapReduce - up to 100 times for data
  in RAM and up to 10 times for data in storage.

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• Iterative processing
• With the condition that task is to process data
  repeatedly - Spark defeats Hadoop MapReduce.
• Hadoop Apache Hadoop offers batch processing.
  Hadoop develops a great deal in creating new
  algorithms and component stack to improve access
  to batch processing on a large scale.
• Sparc's Resilient Distributed Datasets (RDDs)
  enable several map
• operations in memory, while the Hadoop MapReduce
  has to write temporary results to a disk.
• Near real-time processing
• If a business needs instant insights, then they
  should choose Spark and its in-memory
  processing.
• Spark It processes real-time data, i.e. data
  approaching from real-time event streams at the
  rate of millions of events per second, such as
  Twitter and Facebook data. The strength of Spark
  is to process live streams efficiently.
• Hadoop MapReduce MapReduce has its failures when
  it comes to real-
• time data processing, as it was designed to
  perform batch processing on vast amounts of
  data.
• Graph processing
• Sparks computational/estimation model is good
  for iterative or repetitive computations that
  are typical in graph processing. And Apache Spark
  consists of GraphX an API for graph
  computation.
• Machine learning
• Spark has MLlib a built-in machine learning
  library, whereas talking about Hadoop, it needs
  a third-party to provide it.
• MLlib has unthinkable algorithms that also run in
  memory. But if

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• When talking of user-friendly, Spark is easier to
  use than Hadoop. As Spark has user-friendly APIs
  for Scala (its native language), Java, Python,
  and Spark SQL.
• An interactive REPL (Read-Eval-Print-Loop) allows
  Spark users to get
• immediate feedback for commands.
• Hadoop Hadoop, in contrast, is written in Java,
  is difficult to program, and requires
  abstractions.
• Although there is no interactive mode available
  with Hadoop MapReduce, tools like Pig and Hive,
  making it easier for users to work with it.
• Security in Spark
• Sparks security is currently in its immaturity
  stage, offering only authentication support
  through shared secret (password authentication).
• Hadoop MapReduce Hadoop MapReduce possesses
  better security features than Spark. Hadoop
  supports Kerberos authentication, a good
  security feature but difficult to manage.
• Also Read Who To Choose When Its Apache
  Cassandra Vs. Hadoop Distributed File System
• Examples of practical applications
• We examined several examples of practical
  applications and concluded that Spark is likely
  to perform far better than MapReduce in all
  applications below, thanks to fast, or even
  closer to real-time processing. Let's look at
  examples.
• Customer Segmentation
• Analyze customer behavior and identify customers'
  segments that display the same behavior
  patterns, help businesses understand customer
  preferences and create a unique customer
  experience.
• Risk management
• Predicting various potential scenarios can help
  managers make the right decisions by choosing a
  non-risky option.

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4. Industrial Big Data Analysis This is also
about the detection and prediction of anomalies,
but in this case, these anomalies relate to the
machinery breakdown. A well-configured system
collects data from the sensor to detect
pre-failure conditions. Cost Hadoop and Spark,
both come in the category of open-source
projects, therefore they are for free. When it
comes to cost, organizations need to see their
requirements. If its about processing large
amounts of large data, Hadoop will be cheaper
because the hard disk space comes at a very low
rate than memory space. Compatibility The
compatibility of both Hadoop and Spark is good
with each other. The compatibility of Spark is
with all data sources and file formats supported
by Hadoop. Therefore, its not wrong to say
that the compatibility of Spark with data types
and data sources is similar to that of Hadoop
MapReduce. Which framework to choose? Its your
particular business requirements which should
determine the choice of a framework. Linear
processing of huge datasets has the advantage of
Hadoop MapReduce, while Spark provides faster
performance, iterative processing, real-time
analytics, graph processing, machine learning,
and more. In many cases, Spark is far better and
much more advanced than Hadoop MapReduce. The
good news is, Spark is fully compatible with
Hadoop Eco- System and works comfortably with
Hadoop Distributed File System, Apache Hive
etc. Spark can handle any type of requirement
you ask for, whether its batch, interactive,
iterative, streaming or graph, whereas MapReduce
limits to batch processing. Also Read React
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