How does Spark achieve faster data processing compared to traditional MapReduce?
- By using in-memory processing
- By executing tasks sequentially
- By running on a single machine
- By using persistent storage for intermediate data
Apache Spark achieves faster data processing by using in-memory processing. Unlike traditional MapReduce, which writes intermediate results to disk, Spark caches intermediate data in memory, reducing I/O operations and speeding up data processing significantly. This in-memory processing is one of Spark's key features for performance optimization.
Loading...
Related Quiz
- In the context of Data Science, which tool is most commonly used for data manipulation and analysis due to its extensive libraries and ease of use?
- When scaling features, which method is less influenced by outliers?
- The pairplot function, which plots pairwise relationships in a dataset, is a feature of the _______ library.
- When you want to visualize geographical data with customizable layers and styles, which tool is commonly used?
- What is often considered as the primary goal of Data Science?