Explain Hadoop architecture?

Question

Accepted Answer

Hadoop architecture is a distributed computing framework for processing large data sets across clusters of computers.

Hadoop consists of HDFS (Hadoop Distributed File System) for storage and MapReduce for processing.
HDFS divides data into blocks and stores them across multiple nodes in a cluster.
MapReduce is a programming model for processing large data sets in parallel across a distributed cluster.
Hadoop also includes YARN (Yet Another Resource Negotiator) for resource management and job scheduling.
Hadoop ecosystem includes various tools like Hive, Pig, Spark, and HBase for different data processing tasks.

Accepted Answer

Hadoop is the echo system. It uses to develop large scale data processing work. Specific use case: Kappa is ideal for systems that need real-time processing and don’t require a separate batch layer. This is common in systems that work with continuous event data, like real-time monitoring or analyzing sensor data. Example: Real-time monitoring (e.g., network traffic, server health) observes system activity. Sensor data analysis (e.g., temperature, pressure) interprets sensor data. Event-driven architectures (e.g., fraud detection, recommendations) respond to specific real-time events. Mapreduce (Batch processing): This methodology is used for large batch-processing tasks, such as sorting, filtering, and data aggregation. Typical applications includes log analysis, large-scale data transformation, and mining activities. Example: Data transformation and large scale workloads processing: Batch processing often involves operations like sorting, filtering, aggregating, or joining large datasets. Reference: https://hadoop.apache.org

Explain Hadoop architecture?

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