When it comes to the Big Data World, Spark and Hadoop are both famous and well-known Apache projects. They are similar indeed, but if you take a more in-depth look at them, you will spot a few differences.
The process of comparison has been complicated due to their similarities and dissimilarities, which is undeniable. For example, Spark is not quite efficient as Hadoop when it comes to business applications, but at the same time, Spark takes over the big science space because of its speed.
According to their performance, the critical difference is the approach to processing. For example, Spark is able to do it in-memory, unlike Hadoop MapReduce, which has to read from and write to a disk. This is the main reason why their speed of processing is different. Also, the volume of data is quite different because Spark is unable to work with big data sets.
The Difference in Performance
Spark is faster than Hadoop, and it runs applications up to 100x in memory and 10x faster on disk. That is not the case with Hadoop. As previously stated, MapReduce reads and writes from disk, thus lowers the process.
The Ease Of Use
Not only is it good in performance but also its ease of use. Spark, or to be more precise, its abstraction, known as RDD, allows a user to process data using high-level operations. Hadoop, written in Java, is hard to program. Pig makes it easier, but much effort is required. It is important to remember that they are not connected when it comes to installation and maintenance.
The Data Processing
For sure, Hadoop MapReduce is a fantastic batch processing engine. Its steps are sequential, and it reads data from the cluster, performs its operation on the data, writes the results back to the cluster, reads updated data from the cluster, performs the next data operation, writes the results back, etc.
The fact that Spark similarly performs operations should not be left out, but unfortunately, its performance includes all tasks in one step and memory.
Both of them are free and open-source products, but there are additional costs involved.
The Real-Time Analysis
The winner in this category is Spark due to its capability to support the streaming of data along with distributed processing. MapReduce is not capable of real-time data processing.
The Machine Learning
Spark here wins once again because it has a built-in machine learning library. MapReduce requires a third-party for it.
It is essential to realize that both of them are good and are not in war with each other and that they are equally important. Businesses before making a final decision should consider both frameworks and choose one according to their needs.