Comparison of Apache Hadoop & Apache Spark

Introduction to Big Data Frameworks

Big data has created significant excitement in the corporate world. Hadoop and Spark are two prominent big data frameworks that provide some of the most widely used tools for managing big data-related tasks. While they share several common features, there are notable differences between these frameworks. Below are some of these differences explained in detail.

Understanding Hadoop and Spark

Hadoop is fundamentally a distributed data structure. It distributes large data collections across numerous nodes within a collection of commodity servers. It indexes and keeps track of data, enabling big-data processing and analytics far more efficiently than was possible before its existence (White, 2015). Spark, on the other hand, is a data-processing tool that operates on distributed data collections. The flexibility of these tools is evident as they can be used independently. Hadoop consists of a storage component known as the HDFS (Hadoop Distributed File System) and a processing component called MapReduce, eliminating the necessity for Spark to accomplish processing tasks. Conversely, Spark can also be used without Hadoop, although it requires integration with a file management system such as HDFS or another cloud-based platform (Zaharia et al., 2016). Spark was developed with Hadoop in mind, and many agree that they work more effectively together.

Performance and Use Cases

Spark is considerably faster than MapReduce due to its data processing method. While MapReduce operates in steps, Spark processes the entire data set as a whole (Guller, 2015). You might not need the speed of Spark if your data operations and reporting needs are generally static and batch-mode processing is sufficient. However, if you require analytics on continuously streaming data, such as sensor data from an airplane, or have applications that need numerous operations, Spark might be the preferable choice. Common implementations for Spark include online product recommendations, real-time marketing campaigns, cyber-security analytics, and log monitoring.

Failure Recovery Mechanisms

Failure recovery is an essential aspect of both frameworks. Hadoop is inherently resilient to system faults because data is written directly to disk after every operation. Spark, in contrast, offers similar fault tolerance as data is stored in resilient distributed datasets (RDDs) spread across the entire data cluster. These data objects can be stored in memory or on disks, and RDD provides complete recovery from faults or failures (Zaharia et al., 2016). This resilience ensures that data integrity is maintained even in the event of hardware or software failures.

Conclusion

In summary, both Hadoop and Spark offer robust solutions for big data processing, each with its strengths and limitations. Understanding these differences can help organizations choose the right tool for their specific needs, ensuring efficient and effective data management and analysis.

References

Guller, M. (2015). Big Data Analytics with Spark: A Practitioner’s Guide to Using Spark for Large Scale Data Analysis. Apress.

White, T. (2015). Hadoop: The Definitive Guide. O'Reilly Media.

Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M. J., Shenker, S., & Stoica, I. (2016). Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. In Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation (pp. 15-28).