Comparison between Apache spark vs Hadoop

Comparison between Apache spark vs Hadoop

Apache spark vs Hadoop

Hadoop is an open-source framework that allows to store and process big data, in a distributed environment across clusters of computers. Hadoop is designed to scale up from a single server to thousands of machines, where every machine is offering local computation and storage. Spark is an open-source cluster computing designed for fast computation. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. The main feature of Spark is in-memory cluster computing that increases the speed of an application.

Hadoop is designed to handle batch processing efficiently whereas Spark is designed to handle real-time data efficiently. Hadoop is a high latency computing framework, which does not have an interactive mode whereas Spark is a low latency computing and can process data interactively.

Apache spark



Data analytics engine


Basic Data processing engine
Process real-time data, from real-time events like Twitter, Facebook


Batch processing with a huge volume of data
Low latency computing


High latency computing

Can process stream, interactively



Process data in batch mode
Easier to use, abstraction enables a user to process data using high-level operators

Ease of Use

Hadoop’s MapReduce model is complex, need to handle low-level APIs
In-memory computation, no external scheduler required


External job scheduler is required
Less secure as compare to Hadoop


Highly secure
Costlier than Hadoop since it has an in-memory solution


Less costly since MapReduce model provide a cheaper strategy
Fast, distributed, near real-time analytics



Processing speed not a consideration – designed for distributed huge batch operations


DDs run in parallel. If an RDD is lost, it will automatically be recomputed by using the original transformations.

Fault Tolerance

Can significantly extend operation completion times


Hadoop MapReduce allows parallel processing of massive amounts of data. It breaks a large chunk into smaller ones to be processed separately on different data nodes and automatically gathers the results across the multiple nodes to return a single result. In case the resulting dataset is larger than available RAM, Hadoop MapReduce may outperform Spark.

Spark, on the other hand, is easier to use than Hadoop, as it comes with user-friendly APIs for Scala (its native language), Java, Python, and Spark SQL. Since Spark provides a way to perform streaming, batch processing, and machine learning in the same cluster, users find it easy to simplify their infrastructure for data processing.

Final decision to choose between Hadoop vs Spark depends on the basic parameter – requirement. Apache Spark is much more advanced cluster computing engine than Hadoop’s MapReduce, since it can handle any type of requirement i.e. batch, interactive, iterative, streaming etc. while Hadoop limits to batch processing only. At the same time, Spark is costlier than Hadoop with its in-memory feature, which eventually requires a lot of RAM. At the end of the day, it all depends on a business’s budget and functional requirement. I hope now you must have got a fairer idea of both Hadoop vs Spark.

I hope this post helps you to understand all the comparisons between “Apache Spark and Hadoop”.
Keep Learning 🙂

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