Spark Interview Question 1 - Why is Spark preferred over MapReduce?

Apache Spark is an open-source distributed computing system meant for large data processing and analytics. It offers a single engine for distributed data processing that prioritizes speed, simplicity of use, and customization. Spark was developed at UC Berkeley's AMPLab and eventually submitted to the Apache Software Foundation.

Here are some of Apache Spark's main characteristics: 1. In-Memory Computation 2. Distributed Data Processing 3. Rich Collection of APIs 4. Fault Tolerance 5. Integration with Hadoop Let's understand each point in little bit in details - 💎In-Memory Computation - Unlike disk-based systems like MapReduce, Spark retains intermediate data in memory, enabling quicker processing. Interactive data analysis and iterative algorithms are ideal applications for this in-memory processing approach. 💎Distributed Data Processing - Spark has the ability to spread data among a group of computers and process it in parallel. It is available to a broad spectrum of developers, offering high-level APIs in several programming languages, including Scala, Java, Python, and R. 💎Rich Collection of APIs - Spark provides a wide range of APIs for machine learning (MLlib), streaming data (Spark Streaming), batch processing (Spark Core), SQL queries (Spark SQL), and graph analysis (GraphX). Because of this, it's a flexible platform that can handle different large data processing jobs within of one framework. 💎Fault Tolerance - Spark enables fault tolerance via resilient distributed datasets (RDDs), which are distributed collections of data that may be processed concurrently. If a node breaks, RDDs may be automatically recreated using lineage information, offering fault tolerance without requiring operator intervention. 💎Integration with Hadoop - Spark can operate on top of Hadoop YARN, taking use of Hadoop's resource management features. It may also access data stored on Hadoop Distributed File System (HDFS), HBase, and other Hadoop-compatible storage systems.

Overall, Apache Spark's speed enhancements, simplicity of use, diversity, and strong community support have contributed to its broad acceptance and preference over classic MapReduce for many large-scale data processing jobs.

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