What are the differences between Spark and Hadoop?
Spark and Hadoop are two different big data processing frameworks that have some distinctions as listed below:
- Spark is an open-source in-memory computing framework that allows for faster data processing and analysis compared to Hadoop MapReduce. On the other hand, Hadoop is a disk-based distributed computing framework that may experience performance bottlenecks when dealing with large-scale data.
- Spark offers a broader range of API support, including SQL, streaming processing, machine learning, allowing developers to use a variety of tools and technologies to handle data. In contrast, Hadoop is mainly used for batch processing jobs.
- Spark is ideal for scenarios that require fast processing of real-time data and iterative algorithms, while Hadoop is better suited for handling offline batch processing jobs.
- Spark can be more easily integrated into existing big data ecosystems like Hive and HBase, while Hadoop has its own ecosystem that requires more configuration and management.
Overall, Spark is more suitable for handling real-time data and complex computational tasks, while Hadoop is more suitable for processing offline batch jobs and storing large-scale data. Typically, both can be used together to meet different needs.