Hadoop Vs. MongoDB: What Should You Use for Big Data?

What is Hadoop?

Hadoop is an open-source set of programs that you can use and modify for your big data processes. It is made up of 4 modules, each of which performs a specific task related to big data analytics.

These platforms include:

• Distributed File-System
• MapReduce
• Hadoop Common
• Hadoop YARN

Distributed File-System

This is one of the two most crucial components of Hadoop. A distributed file system (or DFS for short) is important because:

• It allows data to be easily stored, shared, and accessed across an extensive network of linked servers.
• It makes it possible to work with data as though you were working from local storage.
• Unlike storage options such as a shared disk file system that limits data access for offline users, you can access data even when offline.
• Hadoop’s DFS is not limited to the host computer’s OS; you can access it using any computer or supported OS.

MapReduce

MapReduce is the second of the two most crucial modules, and it’s what allows you to work with data within Hadoop. It performs two tasks:

• Mapping - which involves transforming a set of data into a format that can be easily analyzed. It accomplishes this by filtering and sorting.  
• Reducing - which follows mapping. Reducing performs mathematical operations (e.g., counting the number of customers over the age of 21) on the map job output.

Hadoop Common

Hadoop Common is a collection of tools (libraries and utilities) that support the other three Hadoop modules. It also contains the scripts and modules required to start Hadoop, as well as source code, documentation, and a Hadoop community contribution section.

Hadoop YARN 

It is the architectural framework that enables resource management and job scheduling. For Hadoop developers, YARN provides an efficient way for writing applications and manipulating large sets of data. Hadoop YARN makes possible simultaneous interactive, streaming, and batch processing.

Why Should We Use Hadoop?

Alright, so now that we know What Hadoop is, the next thing that needs to be explored is WHY Hadoop. Here for your consideration are six reasons why Hadoop may be the best fit for your company and its need to capitalize on big data.

1. You can quickly store and process large amounts of varied data. There’s an ever-increasing volume of data generated from the internet of things and social media. This makes Hadoop’s capabilities a key resource for dealing with these high volume data sources.
2. The Distributed File System gives Hadoop high computing power necessary for fast data computation.  
3. Hadoop protects against hardware failure by redirecting jobs to other nodes and automatically storing multiple copies of data.
4. You can store a wide variety of structured or unstructured data (including images and videos) without having to preprocess it.  
5. The open-source framework runs on commodity servers, which are more cost-effective than dedicated storage.
6. Adding nodes enables a system to scale to handle increasing data sets. This is done with little administration.

Limitations of Hadoop

As good as Hadoop is, it nevertheless has its own particular set of limitations. Among these drawbacks:

1. Due to its programming, MapReduce is suitable for simple requests. You can work with independent units, but not as effective with interactive and iterative tasks. Unlike independent tasks that need simple sort and shuffle, iterative tasks require multiple maps and reduce processes to complete. As a result, numerous files are created between the map and reduce phases, making it inefficient at advanced analytics.
2. Only a few entry-level programmers have the java skills necessary to work with MapReduce. This has seen providers rushing to put SQL on top of Hadoop because programmers skilled in SQL are easier to find.  
3. Hadoop is a complex application and requires a complex level of knowledge to enable functions such as security protocols. Also, Hadoop lacks storage and network encryption.
4. Hadoop does not provide a full suite of tools necessary for handling metadata or for managing, cleansing, and ensuring data quality.
5. Its complex design makes it unsuitable for handling smaller amounts of data since it can't support random reading of small files efficiently.
6. Thanks to the fact that Hadoop’s framework is written almost totally in Java, a programming language increasingly compromised by cyber-criminals, the platform poses notable security risks

What Is MongoDB?

MongoDB is a highly flexible and scalable NoSQL database management platform that is document-based, can accommodate different data models, and stores data in key-value sets.  It was developed as a solution for working with large volumes of distributed data that cannot be processed effectively in relational models, which typically accommodate rows and tables. Like Hadoop, MongoDB is free and open-source.

Some Key Features of MongoDB Include:

1. It’s a query language that is rich and supports text search, aggregation features, and CRUD operations.  
2. It requires lesser input and output operations due to embedded data models, unlike relational databases. MongoDB indexes also support faster queries.
3. It provides fault tolerance by creating replica datasets. Replication ensures data is stored on multiple servers, creating redundancy, and ensuring high availability.
4. It features sharding, which makes horizontal scalability possible. This supports increasing data needs at a cost that is lower than vertical methods of handling system growth.
5. It employs multiple storage engines, thereby ensuring the right engine is used for the right workload, which in turn enhances performance.

The storage engines include:

• WiredTiger

  This is the default engine used in new deployments for versions 3.2 or higher. It can handle most workloads. Its features include checkpointing, compression, and document-level concurrency for write operations. The latter feature allows multiple users to use and edit documents concurrently.  

• In-Memory Storage Engine

  This engine stores documents in-memory instead of on-disk. This increases the predictability of data latencies.

• MMAPv1 Storage Engine

  This is the earliest storage for MongoDB and only works on V3.0 or earlier. It works well for workloads involving bulk in-place updates, reads, and inserts.

Interested to learn about the WiredTiger Storage Engine and MMAPv1 Storage Engine? Then check out the MongoDB Certification Course now.

Why Should We Use MongoDB?

Businesses today require quick and flexible access to their data to get meaningful insights and make better decisions. MongoDB's features are better suited to help in meeting these new data challenges. MongoDB’s case for being used boils down to the following reasons:

1. When using relational databases, you need several tables for a construct. With Mongo’s document-based model, you can represent a construct in a single entity, especially for immutable data.  
2. The query language used by MongoDB supports dynamic querying.
3. The schema in MongoDB is implicit, meaning you do not have to enforce it. This makes it easier to represent inheritance in the database in addition to improving polymorphism data storage.
4. Horizontal storage makes it easy to scale.

Limitations of MongoDB

While MongoDB incorporates great features to deal with many of the challenges  in big data, it comes with some limitations, such as:

1. To use joins, you have to manually add code, which may cause slower execution and less-than-optimum performance.
2. Lack of joins also means that MongoDB requires a lot of memory as all files have to be mapped from disk to memory.
3. Document sizes cannot be bigger than 16MB.
4. The nesting functionality is limited and cannot exceed 100 levels.  

What Should We Use For Big Data? MongoDB or Hadoop?

In trying to answer this question, you could take a look and see which big companies use which platform and try to follow their example. For instance, eBay, SAP, Adobe, LinkedIn, McAfee, MetLife, and Foursquare use MongoDB. On the other hand, Microsoft, Cloudera, IBM, Intel, Teradata, Amazon, Map R Technologies are counted among notable Hadoop users.

Ultimately, both Hadoop and MongoDB are popular choices for handling big data. However, although they have many similarities (e.g., open-source, NoSQL, schema-free, and Map-reduce), their approach to data processing and storage is different. It is precisely the difference that finally helps us to determine the best choice between Hadoop vs. MongoDB.  

No single software application can solve all your problems. The CAP theorem helps to visualize bottlenecks in applications by pointing out that distributed computing can only perform optimally on two out of three fronts, those being processing, partition tolerance, and availability. When choosing the big data application to use, you have to select the system that has the two most prevalent properties that you need.

What About Relational Database Management Systems?

Both Hadoop and MongoDB offer more advantages compared to the traditional relational database management systems (RDBMS), including parallel processing, scalability, ability to handle aggregated data in large volumes, MapReduce architecture, and cost-effectiveness due to being open source. More so, they process data across nodes or clusters, saving on hardware costs.

However, in the context of comparing them to RDBMS, each platform has some strengths over the other. We discuss them in detail below:

RDBMS Replacement

MongoDB is a flexible platform that can make a suitable replacement for RDBMS. Hadoop cannot replace RDBMS but rather supplements it by helping to archive data.

Memory Handling

MongoDB is a C++ based database, which makes it better at memory handling. Hadoop is a Java-based collection of software that provides a framework for storage, retrieval, and processing. Hadoop optimizes space better than MongoDB.

Data Import and Storage

Data in MongoDB is stored as JSON, BSON, or binary, and all fields can be queried, indexed, aggregated, or replicated at once. Additionally, data in MongoDB has to be in JSON or CSV formats to be imported. Hadoop accepts various formats of data, thus eliminating the need for data transformation during processing.

Big Data Handling

MongoDB was not built with big data in mind. On the other hand, Hadoop was built for that sole purpose. As such, the latter is great at batch processing and running long ETL jobs. Additionally, log files are best processed by Hadoop due to their large size and their tendency to accumulate quickly. Implementing MapReduce on Hadoop is more efficient than in MongoDB, again making it a better choice for analysis of large data sets.

Real-time Data Processing

MongoDB handles real-time data analysis better and is also a good option for client-side data delivery due to its readily available data. Additionally, MongoDB’s geospatial indexing makes it ideal for geospatial gathering and analyzing GPS or geographical data in real-time. On the other hand, Hadoop is not very good at real-time data handling, but if you run Hadoop SQL-like queries on Hive, you can make data queries with a lot more speed and with more effectiveness than JSON.

What Next? Recommended Courses for Hadoop and MongoDB

Now that you have all the information you need about MongoDB vs. Hadoop, your next step should be to get certification in the software that best fits your needs. You can go through the following courses:

1. Big Data Hadoop Certification Training Course
2. Apache Spark Certification Training Course
3. MongoDB Certification Training Course

Each company and individual comes with its own unique needs and challenges, so there’s no such thing as a one-size-fits-all solution. When determining something like Hadoop vs. MongoDB, you have to make your choice based on your unique situation. But once you make that choice, make sure that you and your associates are well-versed in the choice. The above training courses will go a long way towards giving you the familiarity you need in helping you get the maximum results from whichever choice you make.