HADOOP

HADOOP

1. Hadoop Features and Environment

   • Open Source Framework
     • Designed to process large volumes of data volumes
       • Storage and distributed processing
         • Developed in Java

2. Hadoop Development Timeline

   • 2003: Google publica GFS y MapReduce 2005: Doug Cutting y Mike Cafarella crean Hadoop
     • 2006: Yahoo adopts Hadoop
       • 2008: Becomes Apache project 2011+: Expansion of the ecosystem (Hive, Pig, HBase...)

3. Advantages of Hadoop

   • Scalability
   • Fault tolerance
   • Low cost (standard hardware)
   • Processes structured and unstructured data
   • Active community and extended support

4. Google File System (GFS) y Hadoop

   • GFS inspired HDFS (Hadoop Distributed File System)
   • Based on large blocks of data
   • Designed for high fault tolerance and high performance

5. MapReduce in Hadoop

   • Parallel processing programming model
     • Map: Filter and sort data
     • Reduces: Groups and summarizes results

6. Relationship of Big Data Analytics

   • Machine Learning
     • Large volumes of data feed the models
     • Hadoop enables data storage and processing for training purposes
   • Cloud Computing
     • Hadoop can run on cloud services (AWS, Azure, GCP)
     • Scalability and on-demand processing