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MM - Explain in detail the Hadoop Ecosystem with suitable diagram.

To create effective mind maps for future recall of the Hadoop Ecosystem, you can distill the detailed explanation into concise keywords and short sentences. Here are some suggestions for each component and concept discussed:

Hadoop Ecosystem Mind Map Keywords and Short Sentences

Core Components

  1. HDFS (Storage)

    • NameNode: Metadata management
    • DataNode: Actual data storage
    • Distributed, fault-tolerant

  2. YARN (Resource Management)

    • ResourceManager: Cluster resource allocation
    • NodeManager: Manages resources on nodes
    • Job scheduling, resource management

  3. MapReduce (Processing)

    • JobTracker: Manages jobs
    • TaskTracker: Executes tasks
    • Parallel, distributed data processing

  4. Hadoop Common

    • Utilities, libraries
    • Compatibility, interoperability

Ecosystem Tools

  1. Apache Hive

    • Data warehouse
    • SQL-like queries (HiveQL)
    • Data summarization, analysis

  2. Apache Pig

    • Scripting platform
    • Pig Latin language
    • Complex data transformations

  3. Apache HBase

    • NoSQL database
    • Real-time read/write
    • Built on HDFS

  4. Apache Sqoop

    • Data transfer
    • RDBMS ↔ Hadoop
    • Efficient bulk transfer

  5. Apache Flume

    • Log data ingestion
    • Collect, aggregate, move logs
    • Reliable, scalable

  6. Apache Oozie

    • Workflow scheduler
    • Manage Hadoop jobs
    • Scheduled, data-driven workflows

  7. Apache ZooKeeper

    • Coordination service
    • Configuration management
    • Synchronization, naming

  8. Apache Mahout

    • Machine learning library
    • Scalable algorithms
    • Clustering, classification

  9. Apache Spark

    • Fast data processing
    • General engine
    • Java, Scala, Python, R

Evaluation and Visualization Tools

  1. Evaluation Metrics

    • Accuracy, precision, recall, F1-score
    • Confusion matrix
    • AUC-ROC curves

  2. Optimization Techniques

    • Parameter tuning
    • Holdout method
    • Random subsampling

  3. Visualization Tools

    • Elbow plot (K-Means)
    • Result interpretation
    • Scikit-learn integration

Diagram Components

  1. User Interface

    • Front-end interaction
    • Access to ecosystem tools

  2. Distributed Storage

    • HDFS: NameNode, DataNode
    • Fault-tolerance, scalability

  3. Resource Management

    • YARN: ResourceManager, NodeManager
    • Job scheduling, resource allocation

  4. Data Processing

    • MapReduce: JobTracker, TaskTracker
    • Parallel processing

Additional Concepts

  1. Text Analysis

    • Preprocessing: Tokenization, stemming
    • Bag of Words, TF-IDF
    • Topic modeling

  2. Social Network Analysis

    • Relationship patterns
    • Influence metrics
    • Practical applications

  3. Business Analysis

    • Data-driven decisions
    • Analytical techniques
    • Real-world use cases

These keywords and short sentences should help you create a concise and effective mind map for the Hadoop Ecosystem, aiding in future recall and understanding.