Advantages of Hadoop
- Scalability: Hadoop is designed to scale linearly, allowing you to easily add more nodes to your cluster as your data grows, without major architectural changes.
- Cost-Effectiveness: Hadoop runs on commodity hardware, making it a more cost-effective solution compared to traditional enterprise data management systems.
- Fault Tolerance: Hadoop is designed to be fault-tolerant, automatically replicating data and redistributing tasks in the event of hardware failures.
- Flexible Data Storage: Hadoop can handle a wide variety of data types, including structured, semi-structured, and unstructured data, making it a versatile platform for data processing and analysis.
- Batch Processing: Hadoop is well-suited for batch processing of large datasets, allowing you to run complex analytical jobs efficiently.
Limitations of Hadoop
- Latency: Hadoop is primarily designed for batch processing and is not as well-suited for real-time or near-real-time processing of data, which may be required in certain applications.
- Complex Setup and Management: Setting up and managing a Hadoop cluster can be a complex and time-consuming process, requiring specialized skills and knowledge.
- Limited Support for Interactive Queries: While Hadoop has improved in this area, it still lacks the robust support for interactive queries and ad-hoc analysis that some traditional data management systems provide.
- Limited Transactional Support: Hadoop’s data processing model is not well-suited for applications that require strong transactional guarantees, such as those found in traditional database management systems.
- Steep Learning Curve: Mastering Hadoop and its associated technologies can be challenging, as it requires a significant investment in training and skill development.
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