Hadoop Conceptual Map

Hadoop Conceptual Map

1. Characteristics and Environment of Hadoop

   • Works within the Hadoop ecosystem (HDFS, MapReduce, YARN, etc.)
   • Distributed storage and processing framework
   • Designed to handle large-scale data
   • Utilizes commodity hardware
   • Fault tolerance through data replication

2. Timeline of the Development of Hadoop

   • 2003–2004: Google publishes papers on GFS and MapReduce
     • 2005: Hadoop begins as part of Apache Nutch
       • 2006: Hadoop becomes its own project under Apache
         • 2008: Hadoop achieves top-level Apache status
           • 2010s: Growth of Hadoop ecosystem (Hive, HBase, Spark, etc.)
             • Present: Widely used in Big Data projects, integrated with cloud platforms

3. Hadoop Advantages

   • Flexible data handling (structured, semi-structured, unstructured)
   • Scalability for massive datasets
   • Cost-effective (uses inexpensive hardware)
   • High availability and reliability
   • Supports parallel processing

4. RONALD DURAN POLO

   • Big Data Integration

5. Google File System (GFS) and Hadoop

   • HDFS inspired by Google File System; GFS provided core principles: large block sizes, replication, master/worker architecture; Hadoop implemented an open-source alternative using similar design ideas.

6. MapReduce in Hadoop

   • Programming model for processing large datasets
     • Consists of two phases:
       • Map: Processes input into key-value pairs
       • Reduce: Aggregates results from map tasks
   • Enables distributed and parallel processing across clusters

7. Relation of Big Data Analytics with Machine Learning and Cloud Computing

   • Big Data Analytics provides massive datasets used to train ML models
     • Machine Learning extracts patterns and predictions from Big Data
       • Cloud Computing offers scalable storage and processing for Big Data and ML workloads
         • Together they form a modern data-driven ecosystem enabling advanced analytics