Hadoop MapReduce is considered to slow down the computation because it reads and writes from the disk. Whereas Apache spark is a lightning-fast big data framework that is designed to boost computational speed. Spark runs on the top of Hadoop to provide a better computational speed solution. It is the lightning-fast gathered computing tool that runs applications 10x faster on disk and 100x faster in memory as compared to Hadoop. Due to the decreasing number of reading and writing cycles to disk and storing the information in memory makes it far better than the other one.
In this blog, you will come across the difference between Hadoop MapReduce and spark and why spark is considered better. Let’s have a look at the feature-wise comparison of both:
– It is an open-source big data framework. It provides a faster and more general-purpose data processing engine. Spark is designed for fast computation. It also covers a wide range of workloads for example batch, interactive, iterative, and streaming.
It is an open-source framework to write applications, it can process unstructured and structured data that are stored in HDFS. It is designed in a way to process the huge volume of data on a bunch of commodity hardware.
It is easy to use as it has a huge level of operators with a Resilient distributed dataset.
It is very difficult to operate as it requires developers to hand-code every operation
- Level of management
It is capable of performing batch, interactive, machine learning, and streaming all in the same boat. As a result, it appears as a complete data analytics engine. There is no need to manage the specific component for every need. You just need to download spark and handle all the requirements easily.
You can have a batch engine but it depends on different engines. As you need to use different components to manage the multiple tasks that are more difficult to manage.