Why is Apache Spark more popular than Hadoop?

Apache Spark has gained popularity over Hadoop for several reasons: 1. **Speed**: Spark is significantly faster than Hadoop MapReduce due to its in-memory computing capabilities. It can cache data in memory, which reduces the need to read from disk, leading to faster processing times. 2. **Ease of Use**: Spark provides a more user-friendly API compared to Hadoop, making it easier for developers to write applications. It supports multiple languages like Java, Scala, Python, and R, allowing users to choose the language they are most comfortable with. 3. **Versatility**: Spark is not just limited to batch processing like Hadoop MapReduce. It also supports real-time processing, interactive queries, machine learning, and graph processing, making it a more versatile tool for various use cases. 4. **Unified Platform**: Spark offers a unified platform for various data processing tasks, whereas Hadoop requires different components like MapReduce for batch processing, Hive for SQL queries, and others. Spark's unified approach simplifies the development and deployment of big data applications. 5. **Community Support**: Spark has a large and active community of developers and contributors, which leads to continuous innovation and improvement of the platform. This community support contributes to Spark's popularity and adoption. Overall, while Hadoop laid the foundation for big data processing, Spark has emerged as a more efficient, versatile, and user-friendly alternative, leading to its widespread adoption and popularity. read less