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.
Integration with Other Technologies
Both Hadoop and MongoDB can be integrated with several different technologies to expand their functionality. For instance, Apache Spark, a quick and versatile cluster computing system, can be linked with Hadoop to use its in-memory processing capabilities for quicker data analysis. Apache Kafka, a distributed streaming technology, can be integrated with MongoDB to ingest and analyze real-time data streams.
Hadoop and MongoDB are used in various use cases based on their advantages. Hadoop is frequently used for massive batch processing and analytics in the banking, retail and telecommunications sectors. Due to its flexible document model and real-time capabilities, MongoDB is frequently utilized in use cases like content management systems, catalog management, customer data management, and IoT applications.
Performance and Scalability
The basic difference between MongoDB and Hadoop is that Hadoop’s distributed architecture enables horizontal scaling by adding affordable commodity hardware to the cluster. It can manage enormous amounts of data by spreading the effort across several nodes. By partitioning and distributing data across various servers, sharding allows MongoDB to scale horizontally. Due to this, MongoDB can effectively handle high workloads in writing and reading.
Hadoop and MongoDB take distinct methods for data consistency. Updates to the data may not be instantly accessible to all cluster nodes since Hadoop’s distributed file system, HDFS, offers eventual consistency. On the other hand, MongoDB enables many data consistency levels, allowing customers to select the level that best meets their application needs.
Data Processing Paradigm
MongoDB’s query language and aggregation architecture enable real-time querying and ad-hoc data analysis, unlike Hadoop’s MapReduce framework, which is well-suited for batch processing and offline analytics. MongoDB’s document-oriented approach is perfect for use cases where data models change over time since it allows for flexible schema building and simple retrieval of layered data structures.
Machine Learning Integration
Hadoop and MongoDB can be connected with machine learning frameworks to perform sophisticated analytics and predictive modeling. Apache Mahout, an open-source machine learning toolkit, may be used with Hadoop without any issues for applications like clustering, classification, and recommendation. The use of machine learning techniques on MongoDB data is made possible by MongoDB’s integration with well-known machine learning platforms like Apache Spark MLlib and sci-kit-learn.
This is an important factor to consider while weighing the MongoDB and Hadoop differences. Cost elements such as hardware infrastructure, storage needs, and operational overhead should be considered when assessing the expenses related to Hadoop and MongoDB. Due to its distributed nature, Hadoop frequently necessitates a cluster of computers, which can result in increased hardware and maintenance costs. Due to MongoDB’s adaptable scaling, businesses can start small and grow as needed, potentially saving money on initial infrastructure expenditures.
Improvements in stream processing tools like Apache Flink and Apache Beam have been made in the Hadoop ecosystem. MongoDB has provided features including multi-document transactions, distributed transactions, and automated sharding upgrades to boost scalability and transactional capabilities further.
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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.
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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.
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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.
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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.
The debate of Hadoop vs MongoDB continues to grow but both are powerful management and processing solutions for big data, each with advantages and applications. Making an informed choice regarding which technology is more appropriate for your particular use case will involve understanding your organization’s specific requirements, including data processing requirements, real-time capabilities, scalability, and security considerations.
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