Disk-Based Analytics
Disk-based analytics refers to data processing and analysis techniques that primarily rely on disk storage (e.g., HDDs, SSDs) rather than in-memory computation, enabling the handling of datasets larger than available RAM. It involves reading data from and writing intermediate results to disk during analytical workflows, such as sorting, aggregating, or querying. This approach is essential for big data scenarios where data volumes exceed system memory capacity, allowing scalable analysis through disk I/O operations.
Developers should learn disk-based analytics when working with large-scale datasets that cannot fit into memory, such as in data warehousing, log analysis, or financial reporting systems. It is crucial for building scalable data pipelines and ETL processes in big data frameworks like Apache Spark or Hadoop, where disk I/O is used to manage data spilling and persistence. This skill is also valuable for optimizing performance in database systems (e.g., PostgreSQL, MySQL) that rely on disk storage for query execution and indexing.