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 |
Comparison |
Hadoop |
| Data analytics engine |
Category |
Basic Data processing engine |
| Process real-time data, from real-time events like Twitter, Facebook |
Usage |
Batch processing with a huge volume of data |
| Low latency computing |
Latency |
High latency computing |
|
Can process stream, interactively
|
Data |
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 |
Scheduler |
External job scheduler is required |
| Less secure as compare to Hadoop |
Security |
Highly secure |
| Costlier than Hadoop since it has an in-memory solution |
Cost |
Less costly since MapReduce model provide a cheaper strategy |
| Fast, distributed, near real-time analytics |
Performance |
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 |
Conclusion
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 🙂
