A Deep Dive into Apache Spark Architecture

Apache Spark is an open-source, distributed computing system used for big data processing and analytics

Spark supports multiple widely used programming languages (Python, Java, Scala, and R), includes libraries for diverse tasks ranging from SQL to streaming and machine learning, and runs anywhere from a laptop to a cluster of thousands of servers. This makes it an easy system to start with and scale up to big data processing

Why Apache Spark is so popular?

Apache Spark has gained immense popularity in the field of big data processing for several reasons:

Speed: A key advantage of Apache Spark is its speed and performance capabilities, especially when compared to Hadoop MapReduce- Spark is 100 times faster than Hadoop.

Ease of Use: Provides high-level APIs in Scala, Java, Python, and R, making it accessible to developers with different language preferences. Simplifies complex data processing tasks, reducing the amount of code required

Multiple Languages Support: Spark supports a range of programming languages including Java, Python, R, and Scala.

General Purpose Distributed Processing engine: Supports a wide range of applications, including batch processing, interactive queries, streaming analytics…