The amount of data that is being produced in today’s world, the growth is nothing short of tremendous. The speed at which data is being produced across the globe, the amount is doubling in size every two years.
This leads to an estimation by Statista that by the year 2024, the amount of data at hand will reach 159 zettabytes or 159 trillion gigabytes.
To manage and make use of such huge amounts of data produced, data scientists across the globe are making use of big data analytics tools. Hadoop and MongoDB are among those tools.
In this blog, we will learn how MongoDB and Hadoop operate differently on a massive amount of data using its particular components.
In brief, MongoDB is a very famous NoSQL database and keeps information in the JSON setup whereas Hadoop is a famous Big data tool that is constructed to size up from one server to thousands of machines or systems where each system is allowing local calculation and storage.
"If we have data, let’s look at data. If all we have are opinions, let’s go with mine."
- Jim Barksdale, former Netscape CEO
With so much data being produced, the traditional methods of storing and processing data will not be suitable in the coming time. The traditional method has been known as Big Data Analytics and it has gained a lot of popularity in recent years. It has been around for more than a decade now.
To store and process this massive amount of data, several Big Data technologies have been made which can help to structure the data in the coming times. This has led to 150 NoSQL solutions right now.
(More to learn, this is how Big data analytics is shaping up IoT).
These solutions are platforms that are not driven by the non-relational database and are often associated with Big Data. However, not all of them qualify as a Big Data solution.
Although the number of solutions might look really impressive, many of these technologies have to be used in conjunction with one another. Also, these are customized for niche markets or may have a low adoption rate in their initial stages.
Out of these many NoSQL solutions, some have gained a substantial amount of popularity. Two of these popular solutions are Hadoop and MongoDB.
Although both the solutions share a lot of similarities in terms of features like no schema, open-source, NoSQL, and MapReduce, their methodology for storing and processing data is significantly different.
Here’s looking at the differences between MongoDB and Hadoop based on
History of the platforms
The function of the platforms
Limitations of the platforms
The MongoDB database solution was originally developed in 2007 by a company named 10gen which is now known as MongoDB. It was developed as a cloud-based app engine with a motive for running multiple services and software.
The company developed two components—Babble and MongoDB. The product could not leave its mark and consequently led to the scrapping of the application and releasing MongoDB as an open-source project.
Post its launch as open-source software, MongoDB took off and gained the support of a growing community. There were multiple enhancements that took place intending to improve and integrate the platform.
MongoDB can be considered an effective Big Data solution. However, it is important to remember that it is a general-purpose platform that is designed to replace or enhance the existing DBMS systems.
Unlike MongoDB, Hadoop had been an open-source project from the very beginning. It was created by Doug Cutting and it originated from a project called Nutch, which was an open-source web crawler created in 2002.
After its launch, Nutch followed the footsteps of Google for several years. For example, when Google released its Distributed File System or GFS, Nutch also came up with theirs and called it NDFS.
Similarly, when Google came up with the concept of MapReduce in 2004, Nutch also announced the adoption of MapReduce in 2005. Then, in 2007, Hadoop was released officially.
Hadoop carried forward the concept from Nutch and it became a platform to parallelly process huge amounts of data across the clusters of commodity hardware.
The traditional relational database management systems or the RDBMS are designed around schemas and tables which help in organizing and structuring data in columns and rows format.
Most of the current database systems are RDBMS and it will continue to be like that for a significant number of years in the time to come. (Understand the difference between data lakes and data Warehouses & databases).
Although RDBMS is useful for many organizations, it might not be suitable for every case to use. Problems with scalability and data replication are often encountered with these systems when it comes to managing data in large amounts.
Since MongoDB is a document-oriented database management system, it stores data in collections. These data fields can be queried once which is opposite to the multiple queries required by the RDBMS.
MongoDB stores data in Binary JSON or BSON. This data is easily available for any ad-hoc queries, replication, indexing, and even MapReduce aggregation.
The language used to write MongoDB is C++ and it can be deployed on Windows as well as on a Linux system.
However, since MongoDB is considered for real-time low-latency projects, Linux machines should be the ideal choice for MongoDB if efficiency is required.
One of the main differences between MongoDB and Hadoop is that MongoDB is a database while Hadoop consists of multiple software components that can create a data processing framework.
Hadoop is a framework that consists of a software ecosystem. Hadoop Distributed File System or HDFS and MapReduce, written in Java, are the primary components of Hadoop.
A collection of several other Apache products forms the secondary components of Hadoop. These products include Hive, Pig, HBase, Oozie, Sqoop, and Flume.
While Hive is for querying data, Pig is for doing an analysis of huge data sets. HBase is a column-oriented database, Oozie helps in scheduling jobs for Hadoop, and Sqoop is used for creating an interface with other systems which can include RDBMS, BI, or analytics. (Learn more about top BI tools and techniques)
The design of Hadoop is such that it runs on clusters of commodity hardware. It also has the ability to consume any format of data, which includes aggregated data taken from multiple sources.
In Hadoop, the distribution of data is managed by the HDFS. It also provides an optional data structure that is implemented with HBase. This helps in the structuring of data into columns.
This is unlike the data structuring of RDBMS which is two-dimensional and allocated the data into columns and rows. Software like Solr is used to index the data in Hadoop.
Both MongoDB and Hadoop exhibit great features but they have their limitations as well. We have listed some of the limitations of both the platforms so that you can decide on which is less limited.
Although MongoDB incorporates a lot of functionalities but has its own set of limitations, such as:
To make use of joins, a user has to manually enter codes. It can lead to slower execution and below-optimum performance.
If a user wishes to proceed without joins, then tha lack of joins would mean that MongoDB requires more memory as all files then will need to be mapped from disk to memory.
Document size cannot exceed 16MB
The nesting functionality is limited and cannot exceed 100 levels.
Being a great platform for big data analytics, Hadoop too have some limitations, such as:
Hadoop makes use of MapReduce that is suitable for simple requests due to its programming. But when a user tries to perform advanced analytics that involves interactive and iterative tasks that require multiple maps and reduce processes to complete, numerous files are created between map and reduce phases. This results in a decrease in the efficiency of the task.
Most Entry-level programmers are unable to work with Hadoop as the operation of Mapreduce requires high java skills. This leads to preference of SQL over Hadoop because SQL is easy to operate for entry-level programmers.
Hadoop is a complex platform and requires a complex level of knowledge to enable functions like security protocols.
Hadoop has a limited suite of tools required to handle metadata or for cleansing, ensuring, and managing data quality.
Hadoop can not efficiently manage small files due to its complex design.
It is concluded that Hadoop is the most genuine and attractive tool in Big data. It collects a massive group of data in an allocated system and operates the data simultaneously on a bunch of nodes.
On the other hand, MongoDB is famous for sharp performance or implementation, leading availability and spontaneous scaling.
Both Hadoop and MongoDB are great choices when we talk about data analytics. Although they share many similarities like open-source, schema-free, MapReduce, and NoSQL, their approach to data processing and storage is different.
In this blog, we have listed both functionalities and limitations before you so that you can decide which is better. We hope the blog is informative and was able to add value to your knowledge.
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