Hadoop

Hadoop provides the backbone for distributed storage and computation. It uses HDFS (Hadoop Distributed File System) to split large datasets across clusters of servers, while MapReduce enables parallel processing. It’s well-suited for Batch Processingasks, though newer tools like Spark often outperform Hadoop in terms of speed and ease of use.

1. Architecture:

   • Open-Source Framework: Hadoop is an open-source framework for distributed storage and processing of large datasets using clusters of commodity hardware.
   • Distributed File System: The Hadoop Distributed File System (HDFS) stores data across multiple machines, providing high throughput access to data.
   • MapReduce: Originally designed for Batch Processing using the MapReduce programming model, though newer frameworks like Apache Spark are often used now.

2. Data Storage:

   • Unstructured, Semi-Structured, and Structured Data: Hadoop can handle a wide variety of data formats, including unstructured, semi-structured, and structured data.
   • Scalable Storage: HDFS can store vast amounts of data by adding more nodes to the cluster.

3. Management:

   • Complex Management: Requires more administrative effort to manage and maintain the infrastructure, including handling failures, load balancing, and tuning.

4. Performance:

   • Batch Processing: Hadoop is optimized for batch processing of large datasets, though it can be less efficient for real-time processing compared to other systems.
   • Latency: Higher latency for query processing compared to Snowflake, particularly for complex analytical queries.

5. Use Cases:

   • Big Data Processing: Ideal for large-scale data processing tasks, including ETL (Extract, Transform, Load), data mining, and large-scale machine learning.
   • Data Lake: Commonly used as a data lake to store vast amounts of raw data.