Difference Between Hadoop and Spark in Big Data Category

When it comes to big data processing, Hadoop and Spark are two commonly used technologies, each with its own strengths and use cases. Here are some key differences between Hadoop and Spark:

Hadoop:

• Apache Hadoop is an open-source framework for distributed storage and processing of large data sets across clusters of computers.
• Hadoop uses the Hadoop Distributed File System (HDFS) for storage and MapReduce for processing data.
• Hadoop is well-suited for batch processing of large datasets and is optimized for handling large-scale data processing tasks.

Spark:

• Apache Spark is another open-source big data processing framework that is more versatile and faster than Hadoop.
• Spark can run in-memory computations to speed up data processing tasks, making it well-suited for iterative algorithms and interactive data analysis.
• Spark supports a wide range of programming languages and provides APIs for batch processing, real-time streaming, machine learning, and graph processing.

In summary, while Hadoop is excellent for large-scale batch processing, Spark offers faster data processing capabilities with support for a variety of data processing tasks in the big data space.