Hadoop or Spark? That's the Big Data Question
Big Data is no longer an upcoming term; it is slowly becoming synonymous with the future of technology. With the ever-escalating volume of data to manage, companies must be equipped with efficient Big Data processing tools to thrive in this competitive era. Two platforms have been at the forefront of Big Data Processing: Hadoop and Spark. This post aims at making an unbiased and factual comparison between the two platforms.
Hadoop Brief Overview
Hadoop is the pioneer platform for Big Data processing. Apache Hadoop offers distributed storage and distributed processing of extensive data-sets. It is an open-source software framework that is used to store and process large amounts of data across multiple machines. It is based on a simple programming model of map-reduce.
Spark Brief Overview
Spark, on the other hand, is relatively new to the Big Data market. It is an open-source, distributed computing system that offers an advanced and faster data processing engine than Hadoop. Spark's primary feature is its in-memory computation capabilities, which allow it to work up to 100 times faster than Hadoop.
Comparison between Hadoop and Spark
The comparison between Hadoop and Spark revolves around several factors, including processing speed, fault tolerance, data processing models, and memory management. Below is a detailed comparison of the two Big Data processing platforms.
Processing Speed
Spark takes the lead in processing speed. With its in-memory computation capabilities, Spark can work up to 100 times faster than Hadoop.
Hadoop Processing Time
A standard Hadoop cluster can process around 7 terabytes of data in 30 hours.
Spark Processing Time
A standard Spark cluster can process the same amount of data in less than 15 minutes.
Fault Tolerance
In terms of fault tolerance, Hadoop has an edge over Spark. This is because Hadoop has a distributed file system that facilitates data replication across multiple nodes, ensuring that the data is always safe.
Data Processing Models
Hadoop mainly relies on its map-reduce model to process data. In contrast, Spark offers a wide range of models for data processing, including SQL queries, machine learning, and stream processing.
Memory Management
Spark's in-memory computing capabilities make it more efficient in memory management than Hadoop. Hadoop processes data from disk, while Spark processes data in-memory, making it faster and more efficient.
Conclusion
In conclusion, the choice between Hadoop and Spark depends largely on your organizational needs. If you are processing a massive, complex data set that requires fault tolerance, Hadoop is your best choice. On the other hand, if processing speed and efficiency are a concern, Spark is the way to go. With this post, we hope you can make an informed decision when choosing the best Big Data processing platform.
References
- Apache Hadoop. www.hadoop.apache.org.
- Apache Spark. www.spark.apache.org.
- Vavilapalli, V., Murthy, A., Douglas, C. et al. Apache Hadoop YARN: Yet Another Resource Negotiator. Proceedings of the 4th annual Symposium on Cloud Computing. ACM, 2013.
- Zaharia, M., et al. "Apache Spark: A Unified Engine for Big Data Processing." Communications of the ACM 59, no. 11 (2016): 56-65.