This is the complete detail of MongoDB vs Hadoop (https://programmershelper.com) the volumes of big data that can be handled by NoSQL systems, like MongoDB vs Hadoop, outstriphttps://programmershelper.com/mongodb-vs-hadoop

1. HTTPS://PROGRAMMERSHELPER.COM
MongoDB vs Hadoop

2. MONGODB VS HADOOP
 The using a single database fit for all situations is a problem. The
data upload one day in Facebook approximately 100 TB and
approximately transaction processed 24 million and 175 million
twits on twitter.
 How the data manipulation in the relational database. In order to
increase or grow data the difference, big data tools are used.
 There are many solutions are available in context to manage big
data. The approximately 120 solutions are available.
 The Hadoop vs MongoDB both of these solutions has many
similarities NoSQL Open source MapReduce schema-less.

3. MONGODB VS HADOOP
 The volumes of big data that can be handled by NoSQL
systems, like MongoDB vs Hadoop, outstrip.
 The big data consists huge amount of information which
consist of volume, variety, velocity, veracity.
 “The amount of data increasing exponentially and is
currently doubling in size every two years. The data will
reach 44 zeta bytes (44 trillion gigabytes) in the year
2025”.

4. BACKGROUND
 Over the increase of automation number of the system
generated a huge amount of the data. In IT industries
generated the huge amount of data. The amount of the last 5
year is more than the data generated last 20 year.
 The Data is growing too fast have complex data in volume
terabyte to petabyte and variety of format hybrid structure or
unstructured. This is called the big data phenomenon. The
conventional database does not handle the massive amount of
data.

5. HADOOP
 In early 2000 two, the most popular company suffer the scalability issue
which is Google and Amazon. These companies are decided to build
thereon solutions such as Google with Big table and Amazon later with
Dynamo DB. In 2007 and 2006 most of the paper it came with concepts of
NoSQL database system.
 As 2017 there more than 225 solutions of NoSQL database. NoSQL data
model are schema-less and the result is more fixable data model. The
schema changes at any time because NoSQL database has a dynamic
schema.
 The majority of NoSQL database is open source. These databases are
horizontally scalable and distributed shared nothing architecture. The
node is working through network do not share any memory or disk.

6. HADOOP
 HDFS(Hadoop distributed file system) run on commodity
hardware. The main difference between a distributed file
system and Hadoop distributed file system it provides facility
to fault tolerance and design to developed for low-cost
hardware.
 The Hadoop is suitable for the application that managed a
large amount of data. The Apache Hadoop is 100 % open
source provides the new way of storing and processing data.
its developed by Apache Nutch web search engine project. but
currently, HDFS is part of Apache Hadoop subproject.
 .

7. MONGODB
 The MongoDB is leading NoSQL database also store data in
form of the document. They have forty-three thousand
customers and thirty million downloads. The first version of
Mongo dB was developed in 2007 New York-based
organization it’s also called Mongo DB Inc. Its first release
developed in PAAS (platform as serveries).
 Later on, MongoDB comes in a market as open source. In
2013 changed its name 10 gen to MongoDB Inc. They store
data in the form of collection instead of the table in the case of
a relational database. The document in the MongoDB
supports any data types such as array numbers string.

8. MONGODB
 A good example of horizontal scaling is Cassandra, Mongo DB. The
Mongo DB handle the dynamic schema and consists of server dynamic
field. The Mongo DB like schema-less support array data types.
 The MongoDB best when the data structure is large and structure of
data change continuously this strangle example Mongo DB handle the
dynamic schema and consists of server dynamic field. the MongoDB
also provides very rich functionality shading indexing replication and
map reduce.
 This a high-level architecture of MongoDB vs Hadoop. The multiple
mappers read the input via Hadoop MongoDB. The output is saved in
one reducer which writes back the result to MongoDB or Hadoop.

9. COMPLETE DETAIL ON THIS PAGE
HTTPS://PROGRAMMERSHELPER.COM
Complete detail on this page
https://programmershelper.com
https://programmershelper.com/mongodb-vs-
hadoop