Spark vs Hadoop: Key Differences Explained

Both Spark and Hadoop are open-source frameworks used for big data processing, but they have some key differences.

1. Data processing models: Hadoop is a data processing framework based on MapReduce, which breaks tasks into two phases – Map and Reduce. In comparison, Spark utilizes a more flexible in-memory computing model, allowing for storing intermediate results in memory to improve processing speed.
2. Processing speed: Spark is generally faster than Hadoop because it can store intermediate results in memory. Spark is ideal for scenarios requiring interactive data analysis and real-time data processing.
3. Execution Engine: Hadoop utilizes YARN as its resource manager, while Spark uses its own scheduler. Additionally, Spark offers more advanced features such as the machine learning library MLlib and the graph processing library GraphX.
4. Programming Model: Hadoop is typically programmed using Java, while Spark supports multiple programming languages, including Java, Scala, Python, and R. This makes Spark easier to use and learn.

In general, Spark is suitable for scenarios requiring fast data processing and complex analysis, while Hadoop is more suitable for traditional batch processing tasks. In practical applications, it is common to choose the appropriate framework based on specific requirements or to combine them for use.