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  1. Apache Hadoop: An open-source framework for distributed storage and processing of large datasets across clusters of computers using simple programming models.
  2. Core Components:
    • HDFS (Hadoop Distributed File System): A distributed file system that provides high-throughput access to application data.
    • MapReduce: A programming model for processing large datasets in parallel across a distributed cluster.
  3. HDFS:
    • NameNode: The master server that manages the file system namespace and regulates access to files by clients.
    • DataNode: The slave nodes that store the actual data and perform read/write operations as instructed by the NameNode.
    • Replication: HDFS replicates data blocks across multiple DataNodes to ensure fault tolerance.
  4. MapReduce:
    • Map Phase: Processes input data and converts it into a set of key-value pairs.
    • Reduce Phase: Aggregates the output of the Map phase to produce the final result.
  5. YARN (Yet Another Resource Negotiator): A resource management layer that manages resources in a cluster and schedules tasks.
  6. Advantages:
    • Scalability: Can scale from a single server to thousands of machines.
    • Fault Tolerance: Automatically handles node failures without data loss.
    • Cost-Effective: Uses commodity hardware, reducing costs.
  7. Use Cases:
    • Big Data Analytics: Processing and analyzing large datasets.
    • Data Warehousing: Storing and managing large volumes of structured and unstructured data.
    • Log Processing: Analyzing large volumes of log data generated by web servers.
  8. Ecosystem:
    • Hive: A data warehouse infrastructure built on top of Hadoop for providing data summarization and querying.
    • Pig: A high-level platform for creating MapReduce programs used with Hadoop.
    • HBase: A distributed, scalable, big data store that supports structured data storage for large tables.
  9. Challenges:
    • Complexity: Requires expertise to set up and manage.
    • Latency: Not suitable for real-time processing due to high latency.
    • Data Security: Requires additional tools and configurations to ensure data security.
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