Key Difference Between MongoDB and Hadoop
Open-source Hadoop stores and processes huge data. The Java-based application includes a distributed file system, resource management, data processing, and interface components.
Data storage and retrieval are MongoDB’s main goals. Scalability and data processing are also possible. It is C++-based and NoSQL. It does not require relational tables. It keeps records as documents.
MongoDB vs Hadoop Comparison Table
MongoDB and Hadoop are popular big data technologies with different aims and structures. A table comparing MongoDB with Hadoop shows their main differences:
| Feature | MongoDB | Hadoop |
|---|---|---|
| Type | NoSQL database, specifically a document-oriented database | Distributed processing framework and ecosystem for big data |
| Data Model | Document-oriented (JSON-like BSON format) | Primarily used for batch processing of large datasets using MapReduce |
| Storage | Supports flexible, schema-less documents with horizontal scalability | Stores data across a distributed file system (Hadoop Distributed File System – HDFS) |
| Query Language | MongoDB Query Language (MQL) | Primarily uses MapReduce, but also supports languages like Pig, Hive, and Spark |
| Schema | Dynamic schema allows for flexible data models | Schema-on-read approach, enabling flexibility in handling different data structures |
| Scaling | Horizontally scalable, enabling the distribution of data across multiple servers | Horizontally scalable by adding more nodes to the Hadoop cluster |
| Use Case | Well-suited for applications requiring fast and efficient retrieval of structured data | Designed for processing and analyzing large volumes of data, especially unstructured data |
| Indexing | Supports various types of indexes for efficient querying | Hadoop Distributed File System (HDFS) does not use traditional indexing; relies on processing frameworks |
| Complexity | Simpler setup and management, suitable for applications requiring real-time querying | More complex to set up and manage, as it involves distributed storage and processing components |
| Real-time Processing | Offers support for real-time processing through features like change streams | Historically not designed for real-time processing; recent components like Apache Flink provide real-time capabilities |
| Data Partitioning | Supports automatic sharding for data partitioning across multiple nodes | Manages data partitioning through the Hadoop Distributed File System (HDFS) and MapReduce programming model |
| Consistency Model | Provides tunable consistency, allowing users to choose between strong and eventual consistency | Generally adheres to the eventual consistency model, which may be acceptable in certain use cases |
| Schema Evolution | Easily accommodates changes in the data model without requiring a predefined schema | Supports schema evolution through data transformations and compatible file formats |
| Integration with Tools | Integrates well with various programming languages and frameworks | Integrates with a wide range of tools and frameworks, including Apache Spark, Hive, HBase, etc. |
| Concurrency Control | Provides multi-document ACID transactions, supporting high concurrency | Primarily focuses on eventual consistency, with less emphasis on ACID transactions |
| Commercial vs Open Source | Offers both a free, open-source community edition and a commercially supported version | Predominantly open source, with various commercial distributions and support options available |
| Companies Using | Used by various companies for applications requiring flexible, scalable document storage | Adopted by companies for big data processing, analytics, and large-scale data storage and retrieval |