Hadoop is an open-source framework designed for distributed storage and processing of massive datasets across clustered computers. At its core, it solves two fundamental big data challenges:

// Simplified Hadoop architecture components
HDFS (Hadoop Distributed File System) = Storage layer
YARN (Yet Another Resource Negotiator) = Resource management
MapReduce = Processing paradigm

The framework's real power comes from its ability to handle both structured and unstructured data at petabyte scale. Unlike traditional databases, Hadoop follows a "schema-on-read" approach, providing extreme flexibility for diverse data types.

Here are three common scenarios where Hadoop shines:

// Example: Log processing pipeline
1. Web server logs → HDFS storage
2. MapReduce job to count 404 errors
3. Output aggregated results to HBase

Other prominent use cases include:

• Recommendation engines (e-commerce product suggestions)
• Fraud detection in financial transactions
• Genomic sequence analysis in bioinformatics

Hadoop's distributed nature provides three critical benefits:

1. Fault tolerance: Data replicated across nodes
2. Linear scalability: Add more commodity servers
3. Cost efficiency: No need for expensive hardware

Here's a simple WordCount implementation showing Hadoop's processing model:

public class WordCount {

  public static class TokenizerMapper
       extends Mapper<Object, Text, Text, IntWritable>{
    
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    public void map(Object key, Text value, Context context
                    ) throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        context.write(word, one);
      }
    }
  }
  
  public static class IntSumReducer
       extends Reducer<Text,IntWritable,Text,IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> values,
                       Context context
                       ) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }
}

Despite its strengths, Hadoop isn't ideal for:

• Low-latency transactional systems
• Small datasets (under 100GB)
• Frequently updated records

The ecosystem has evolved with tools like Spark addressing some limitations, but Hadoop remains foundational for batch processing at scale.

Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. At its core, it solves the fundamental challenge of handling data that's too large for single machines through parallel processing.

The framework consists of four primary modules:

• HDFS (Hadoop Distributed File System): The storage layer that splits files into blocks and distributes them across cluster nodes
• YARN: Resource management and job scheduling component
• MapReduce: Programming model for parallel computation
• Common: Shared utilities and libraries

Typical use cases where Hadoop outperforms traditional systems:

// Example: Simple WordCount MapReduce Job
public class WordCount {
  public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
    
    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        context.write(word, one);
      }
    }
  }
  
  public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> {
    private IntWritable result = new IntWritable();
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }
}

Web Analytics Case: A major e-commerce platform processes 10TB of daily clickstream data using Hadoop to:

• Track user behavior patterns
• Generate real-time product recommendations
• Detect fraud patterns

Scientific Research Example: Genomics researchers use Hadoop to analyze DNA sequencing data where:

// Pseudocode for genomic sequence alignment
input = loadDNASequencesFromHDFS();
results = input.map(sequence => alignToReference(sequence))
               .reduce((a,b) => findCommonMutations(a,b));
saveResultsToHDFS(results);

While powerful, Hadoop isn't ideal for:

• Low-latency applications (consider Spark instead)
• Small datasets (under 100GB)
• Transactional systems requiring ACID properties

For optimal cluster configuration:

# Sample hdfs-site.xml configuration
<property>
  <name>dfs.replication</name>
  <value>3</value>
  <description>Default block replication</description>
</property>

# MapReduce memory settings
mapreduce.map.memory.mb=4096
mapreduce.reduce.memory.mb=8192

Complementary tools that extend Hadoop's capabilities: