Evolution creates a strong, smart version of everything, and technology has been evolving since the day of its origin. Our data is piling up day by day and to store the data and process our device more efficiently we use software like Hadoop, Spark, etc. it has come to the point where we need to compare Hadoop and Spark software to identify the more efficient one.
What is Hadoop:
Hadoop is open-source software that assists in utilizing clusters of computers to solve problems involving a large amount of data and computations. It includes the framework to store and process a large amount of data using the MapReduce programming model. Hadoop helps the process of storing a large amount of data quickly as the clusters of computers are analyzing massive data.
What is Spark:
A spark is also open-source software that facilitates the data processing engine for a large amount of data. A large amount of data is spilt into different nodes. It is designed to perform more efficiently and use RAM for caching and processing data. The spark is not an updated version of Hadoop. It is designed to learn algorithms and complex data analytics. It is running as a standalone or as a pack on top of Hadoop.
There is a quite difference between Hadoop and Spark as discussed below;
|Easy to use||Yes||No|
The detailed Comparison is as follows;
|Speed||Spark is 100 times faster on memory and 10 times faster on disk. Spark does not wait for input-output concerns every time because a selected part of the MapReduce task is performed by it.||Hadoop is not as fast as Spark but its speed depends upon the package installed and data storage, maintenance, and analytics.|
|Scope||Spark is limited to its tools like spark core, spark SQL, and spark streaming.||Hadoop is broader than a spark in terms of scope.|
|Programming language||Spark has supported almost all the languages that are data scientists using. It includes Java, Scala, SQL, python, etc.||Hadoop support anther languages than the one it is written in, Java, and some portions are written in C.|
|Fault tolerance||Spark handles its failures respectably and uses RDD blocks to tolerate faults in the system.||Hadoop has its way to handle failure. If any issue appears during the process it resumes the work but shows the missing block at another location.|
|User Friendly||Spark is user-friendly.||Hadoop is hard to use.|
|Machine Learning||Spark has in-memory processing and uses MLib for computations.||Hadoop is slower because of the fragments which create bottlenecks.|
|Cost||Spark is expensive.||Hadoop is cheaper in terms of cost.|
|Data||Spark process data in real-time events, for example, Twitter and Facebook.||Hadoop processes data only in batch mode.|
|Security||Spark has to rely on Hadoop, which makes it not very secure.||Hadoop is secure and includes support for LDAP, ACLs, SLAs, etc.|
Hadoop and Spark are compared to analyze which is more efficient and has the most reliability. Both are open-source software but operate and process data in different ways. Technology has made us need more advanced software day by day because of the requirements of storing a large amount of data. Hadoop and spark are used to store and process data in unique ways.