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HDFS Use Cases

1. Definition

HDFS Use Cases are practical applications leveraging HDFS's distributed storage capabilities to solve specific business and technical problems involving large-scale data.

2. Ideal Use Cases for HDFS

2.1 Large File Storage

Requirement: Store files from gigabytes to terabytes.

HDFS Solution: Optimized for large files with 128 MB block size.

Examples:

  • Video files (hundreds of GBs per file)
  • Genomic sequencing data (hundreds of GBs per genome)
  • Satellite imagery (terabytes of image data)
  • Scientific simulations (large result datasets)

Why HDFS: Distributes large files across cluster, no single-machine storage limits.

2.2 Log File Analysis

Requirement: Store and analyze massive volumes of log files.

Example: Web server access logs, application logs, system logs.

Workflow:

Thousands of Servers → Generate logs daily → 
Aggregate into HDFS → MapReduce analysis →
Identify patterns, errors, security threats

Benefits:

  • Store years of historical logs cost-effectively
  • Parallel processing reduces analysis time from days to hours
  • No data loss even with hardware failures

3. Industry-Specific Use Cases

3.1 E-commerce - Clickstream Analysis

Problem: Analyze billions of user clicks to understand behavior.

HDFS Implementation:

User Clicks → Real-time ingestion to HDFS (via Flume/Kafka) →
Daily batch processing (MapReduce/Spark) →
Insights: Popular products, abandoned carts, user journeys

Value: Optimize website, improve recommendations, increase conversions.

3.2 Finance - Risk Analytics

Problem: Analyze historical transaction data for risk models.

Implementation:

  • Store 10+ years of transaction history (petabytes)
  • Run Monte Carlo simulations on HDFS-stored data
  • Build credit risk models using machine learning

HDFS Advantage: Cost-effective storage + parallel processing power.

3.3 Healthcare - Medical Image Storage

Problem: Store and retrieve medical images (X-rays, MRIs, CT scans).

Implementation:

  • Each image: 100-500 MB
  • Hospital generates thousands daily
  • HDFS stores with 3x replication for reliability
  • PACS (Picture Archiving System) reads from HDFS

Benefits: Reliable storage, disaster recovery, compliance with retention policies.

4. HDFS vs Other Storage

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Exam Pattern Questions and Answers

Question 1: "Explain use cases where HDFS is most suitable." (6 Marks)

Answer:

Large File Storage (2 marks): HDFS excels at storing very large files from gigabytes to terabytes such as video files, genomic data, and satellite imagery. Its 128 MB block size and distributed architecture enable storing files beyond single-machine capacity while maintaining fault tolerance through replication.

Log File Analysis (2 marks): Organizations use HDFS to store massive volumes of server and application logs generated daily across thousands of machines. Logs are aggregated into HDFS where MapReduce jobs analyze them in parallel to identify patterns, errors, and security threats, reducing analysis time from days to hours.

Machine Learning (2 marks): HDFS stores large training datasets for machine learning models including historical transaction data, user behavior logs, and sensor readings. Data scientists use Spark on HDFS to train models on petabytes of data, leveraging parallel processing and data locality for efficient computation.

Summary

Key Points:

  1. Best For: Large files, batch analytics, write-once-read-many
  2. Not For: Small files, low latency, random writes
  3. Industries: E-commerce (clickstreams), Finance (risk), Healthcare (images)
Exam Tip

Always contrast HDFS suitability with unsuitability. Mention specific file sizes (GBs-TBs) and real examples (logs, videos, genomics).

Quiz Time! 🎯

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