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MongoDB vs Hadoop

By Priya PedamkarPriya Pedamkar

MongoDB vs Hadoop

Difference Between MongoDB vs Hadoop

The concept didn’t commence, leading 10gen to scrap the applying associated unharness MongoDB as an Open source project. MongoDB will actually be thought-about an enormous data answer, its price noting that it’s extremely a general platform. Hadoop is meant to be run on clusters of artifact hardware, with the power consumption data in any format, together with aggregative data from multiple sources. Hadoop became a platform for multiprocessing mass amounts of data across clusters of artifact hardware.

What is MongoDB?

MongoDB was originally developed by the corporate 10gen in 2007 as a cloud-based app engine that was meant to run different packages and services. They had developed 2 main elements, Babble (the app engine) and MongoDB (the database). The concept didn’t commence, leading 10gen to scrap the applying associated unharness MongoDB as an Open source project. MongoDB will actually be thought-about an enormous data answer, its price noting that it’s extremely a general platform, designed to exchange or enhance existing RDBMS systems, giving it a healthy type of use cases.

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How MongoDB work?

MongoDB stores data in collections, within which totally different data fields may be queried once. The database is held on as Binary JSON (BSON) and is quickly obtainable for ad-hoc queries, indexing, replication, and Map Reduced aggregation. Database Sharding may be applied to permit distribution across multiple systems for horizontal measurability PRN. MongoDB is written in C++ and may be deployed on a Windows or UNIX operating system machine, however particularly considering MongoDB for time period low-latency comes, UNIX operating system is a perfect alternative for the sake of potency. A primary distinction between MongoDB vs Hadoop is that MongoDB is truly a database, whereas Hadoop could be an assortment of various package elements that make a data process framework.

What is Hadoop?

In distinction, Hadoop was an open-source project from the start; created by Doug Cutting (known for his work on Apache Lucerne, a preferred search categorization platform), Hadoop originally stemmed from a project known as Nutch, open-source net crawler created in 2002. In 2004, Google introduced the thought of MapReduce. Hadoop isn’t meant as a replacement for transactional RDBMS systems, however rather as a supplement to them.

How Hadoop Work?

Hadoop, as previously mentioned, could be a framework comprised of a package scheme. The first elements of Hadoop are the Hadoop Distributed filing system (HDFS) and MapReduce that is written in Java. Secondary elements are a set of alternative Apache merchandise, including: Hive (for querying data), Pig (for analysing massive data-sets), HBase (column orientating database), Oozie (for programming Hadoop jobs), Sqoop (for interfacing with alternative systems like Bi, analytics, or RBDMS), and Flume (for aggregating and preprocessing data). Like MongoDB, Hadoop’s HBase database accomplishes horizontal measurability through database sharding.  Distribution of data storage is handled by the HDFS, with associate elective organization enforced with HBase that allocates data into columns (versus the two-dimensional allocation of associate RDBMS in columns and rows). data will then be indexed (through use of package like Solr), queried with Hive, or have numerous analytics or batch jobs run on that with selections obtainable from the Hadoop scheme or your alternative of business intelligence platform.

Head to Head Comparison Between MongoDB and Hadoop (Infographics)

Below is the top 5 difference between MongoDB and Hadoop:

MongoDB vs Hadoop Infographics

Key differences between MongoDB and Hadoop

Let us discuss some of the major Difference Between MongoDB and Hadoop:

  • Hadoop is versatile within the format data; it may be of any obtainable format whereas MongoDB imports solely CSV and JSON format data.
  • MongoDB has the power of geospatial categorization that is helpful in geospatial analysis. This feature isn’t available in Hadoop.
  • MongoDB belongs to the NoSQL family whereas Hadoop use of SQL for the process of data.
  • Hadoop relies on Java whereas MongoDB has been written in the C++ language.
  • Hadoop is Suite of merchandise whereas MongoDB could be a complete Product.
  • The hardware price of MongoDB is a smaller amount compared to Hadoop.
  • When compared to Hadoop, MongoDB is a lot of versatile it will replace existing RDBMS. Hadoop, on the opposite hand, may perform all the tasks, however, ought to add an alternative package.
  • Hadoop could be a Framework which will have a lot of package for process whereas MongoDB could be a database sort.
  • Hadoop is best for large-scale process applications whereas MongoDB is best for time period mining of data and process.

MongoDB and Hadoop Comparison Table

The primary Comparison between MongoDB and Hadoop are discussed below:

S.No.

 MongoDB

Hadoop

1

 It provides a lot of sturdy answers, a lot of versatile then Hadoop. It Will replace existing RDBMS. The most important strength of Hadoop is that it’s engineered to handle massive data. It’s wonderful for handling batch processes and long-running ETL jobs.

2

 Stores data in collections, every data fields may be queried promptly. Data is held on as Binary JSON or BSON and is accessible for querying, aggregation, indexing, and replication. Consists of different software, the important components are the Hadoop Distributed File System (HDFS) and MapReduce.

3

 It is truly a database and is written in C++ Collection of various package that makes processing framework. Its Java primarily based application.

4

 Designed to the method and analyze the immense volume of data. It’s a database, Primarily designed for data storage and retrieval.

5

 Major grievance relating to MongoDB is fault tolerance issue, which may result in data loss. It depends in the main on ‘Name Node’, that is that the sole purpose of failure

Conclusion

Through the various topics mentioned above during this comparison of Hadoop and MongoDB as a Big Data solution, it’s apparent that an excellent deal of analysis and concerns ought to surface before preferring which is the best choice for your organization. If you’ve got needs for process low-latency time period data or trying to find a lot of encompassing answer (such as commutation your RDBMS or beginning a completely new transactional system), MongoDB could also be a decent alternative. If you’re trying to find an answer for batch, long-running analytics whereas still having the ability to question data then Hadoop could be a definite choice.

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