By 2020, the global data generated will stand at 44 Zettabytes. As the amount of data continues to pile up, traditional data processing methods cannot suffice for processing vast volumes of data. This is where Big Data technologies and frameworks come in – these structures are designed to handle, process, analyze, interpret, and store vast volumes of data.
While there are numerous Big Data frameworks, today, we’re going to focus on two in particular – Hadoop and MongoDB.
What is Hadoop?
Hadoop was created by Doug Cutting. It is a Javed-based open-source platform for processing, modifying, and storing Big Data. Hadoop comprises of four core components, each designed to perform specific tasks associated with Big Data Analytics:
- Hadoop Distributed File System (HDFS) – It is a highly scalable, fault-tolerant file system that facilitates seamless data storage, access, and sharing across a huge network of connected servers.
- MapReduce – It is a software development framework used for processing large datasets in parallel by performing two crucial functions: mapping and reducing.
- YARN (Yet Another Resource Negotiator) – It is Hadoop’s architectural framework for scheduling and resource management.
- Hadoop Common – It is an assortment of libraries and functions that support the other three Hadoop components. YARN allows for simultaneous streaming, interactive, and batch processing.
What is MongoDB?
MongoDB is an open-source NoSQL database management framework. It is a document-oriented system that is highly scalable and flexible. One of the key features of MongoDB is that it can accommodate high volumes of distributed datasets and store data in collections (in key-value sets). MongoDB comprises of three core components:
- mongod: It is the primary daemon process for MongoDB.
- mongos: It is a controller and query router for sharded clusters.
- mongo: It is an interactive MongoDB shell.
Hadoop vs. MongoDB: A Comparison
- While Hadoop is a Java-based software application, MongoDB is a database written in C++. Hadoop is a suite/collection of products, but MongoDB is a standalone product in itself.
- Hadoop acts as a supplement to the RDBMS system for archiving data, whereas MongoDB can replace the existing RDBMS completely.
- Hadoop is best-suited for large-scale batch processing and long-duration ETL tasks, whereas MongoDB is excellent for real-time data mining and processing.
- MongoDB is highly useful in Geospatial Analysis since it comes with geospatial indexing which is absent in Hadoop.
- When it comes to the data format, Hadoop is pretty flexible. However, MongoDB can only import CSV and JSON data formats.
Which is more secure and better for Big Data?
Both Hadoop and MongoDB are built for handling and managing Big Data, and both have their fair share of advantages and disadvantages. As we mentioned before, Hadoop is the best fit for batch processing, but it cannot handle real-time data, although you can run ad-hoc SQL queries with Hive.
On the contrary, MongoDB’s greatest strength is its flexibility and capability to replace the existing RDBMS. It is also excellent at handling real-time data analytics. So, if your company has real-time data with low latency or you need to create a new system by replacing the existing RDBMS, MongoDB is the way to go. However, if you need large-scale batch solutions, Hadoop is the tool for you.
Although both Hadoop and MongoDB are highly scalable, flexible, fault-tolerant, and capable of handling large volumes of data. But when it comes to security, both have numerous drawbacks.
Hadoop’s shortcomings on the security front emerge from one central point – its complexity. Since Hadoop is an amalgamation of interrelated and co-operating components, it becomes difficult to configure and manage the platform. Also, if less experienced professionals are handling it, they may leave the attack vectors exposed to threats. More importantly, when Hadoop was designed, the concept of “security” was left out – initially, it was restricted only to private clusters in stable environments. And although now Hadoop has the necessary security features like authentication and authorization, they can be turned off as a default option.
As of now, there are four documented vulnerabilities of Hadoop in the CVE (Common Vulnerabilities and Exposures) database, and its average CVSS (Common Vulnerability Scoring System) score is 6.3. Hence, it falls in the medium-risk segment.
Coming to MongoDB, its security shortcomings may not be as highly publicized or highlighted like Hadoop, but it has many crucial vulnerabilities nonetheless. Since both Hadoop and MongoDB have originated from private data centers and then integrated with cloud platforms, they have generated an ocean of attack vectors. Just like Hadoop, MongoDB has no access control. MongoDB records seven documented vulnerabilities in the CVE database with an average CVSS score of 6. Thus, it also falls in the medium-risk segment.
So, as you can see, while both Hadoop and MongoDB can efficiently take care of the Big Data needs of your organization, they are not very reliable from the security perspective. Web applications built on these frameworks are usually shipped with the security features turned off by default. This only points to bad security practices, not just at the vendor’s end but also at the developer’s. The key to overcoming these drawbacks in security is to integrate Hadoop and MongoDB platforms with the proper control mechanisms that can promptly identify and remediate vulnerabilities within the software delivery pipeline, thereby facilitating security monitoring and evaluation for all endpoints in the system.
If you are interested to know more about Big Data, check out our PG Diploma in Software Development Specialization in Big Data program which is designed for working professionals and provides 7+ case studies & projects, covers 14 programming languages & tools, practical hands-on workshops, more than 400 hours of rigorous learning & job placement assistance with top firms.