Apache Spark Vs. MapReduce
Introduction
As the volume of data generated worldwide skyrocketed in the early 2000s, traditional data processing systems needed help. The birth of distributed computing frameworks like MapReduce offered a powerful solution to the challenges of handling big data, making massive parallel processing possible. However, as data processing needs evolved, the limitations of MapReduce became apparent. Enter Apache Spark, a newer, faster, and more flexible framework that has gradually outshined MapReduce. In this blog, we’ll explore why Apache Spark is superior to MapReduce, focusing on critical aspects like performance, ease of use, fault tolerance, and flexibility.
Overview of MapReduce and Apache Spark
What is MapReduce?
MapReduce, a distributed computing paradigm, was introduced by Google in 2004 and later became the cornerstone of the Hadoop ecosystem. It breaks data processing tasks into two phases: the Map phase, where input data is divided into chunks and processed, and the Reduce phase, where intermediate results are aggregated. The entire process relies on shuffling data between nodes, with frequent disk I/O operations to store intermediate data. This makes MapReduce highly fault-tolerant and scalable, enabling processing across distributed environments. However, its batch-processing…
