Word count: 3000 words

Objectives to cover:

  • Introduction: The growing challenges of storing and managing big data efficiently.
  • Overview of Compression: The role of compression in optimizing big data storage and processing.
  • Compression Fundamentals: Key concepts, including lossless and lossy techniques, in data compression.
  • Popular Algorithms: Widely used big data compression methods such as Gzip, Snappy, and LZO.
  • Emerging Innovations: Recent advancements and novel approaches in compression technology.
  • Efficiency Metrics: Evaluating algorithms based on compression ratio, speed, and resource usage.
  • Implementation Challenges: Balancing speed, compatibility, and data integrity in big data environments.
  • Case Studies: Insights from real-world applications and industry best practices.
  • Conclusion: Future trends and the potential of adaptive compression for big data storage.

Reference:  IEEE style