If you were just measuring speed, Spark would take Hadoop to the mat before you could say, “DAG execution engine.” On the other hand, Hadoop is a “weightier” opponent, meaning it distributes and stores giant data collections. Think of Hadoop as kind of the data equivalent of a sumo wrestler.
The reality is that Hadoop and Spark are more data management brothers than adversaries, so in a head-to-head matchup, they’d have a good-natured tussle, and then probably embrace and work together toward a common goal.
As data-frameworks, Hadoop and Spark have similarities, but they actually perform different functions. So data management probably would benefit the most by leveraging the strengths of both platforms, instead of one or the other.
Hadoop stores and moves big collections of data sets via the cloud. This means you can avoid the big expenditure that comes with a hardware purchase. Of course, the “con” here is that if you’re at all nervous about the on-demand functionality and security of cloud computing, you should probably go ahead and submit that budget for a server farm next year.
Hadoop is distributed computing at its finest. As an open source Apache resource, it certainly speeds up the process of scaling to business needs. Hadoop’s distributed file system features a processor called MapReduce. When compared to Spark, MapReduce is a little clunky — it doesn’t handle streaming data as well, operating in concrete steps as it reads and responds to data.
For the last several years, Hadoop has been the go-to big data open source framework. Spark is now the young upstart, challenging Hadoop for primacy in the data management marketplace.
We mentioned Spark is speedier than Hadoop — like, up to 10 times faster, according to InfoWorld. Spark is excellent for real-time analytics, especially related to cyber security or machine-based learning. Instead of writing each bit of data back to storage after an operation is complete like in MapReduce, Spark stores data in RDDs (resilient distributed datasets). Both functions were designed to protect data from catastrophic system failure, but Hadoop’s MapReduce is the “old school” approach. Both store data in a way that is completely recoverable, but the Spark upgrade is quicker.
Integrating Spark + Hadoop
Since Spark doesn’t have its own file management system, it might make sense to integrate with Hadoop or other cloud-centric SaaS. In fact, Apache intended integration of the two open source platforms. Typically, Spark is layered over Hadoop, making use of advanced data analytics applications while utilizing Hadoop’s Distributed File System.
Integrating Hadoop and Spark allow for the best of both applications. You can process data in real-time, feed it through an analytics portal, and make competitive business decisions at digital speeds. Together, these two frameworks offer data management options that foster a new era of business intelligence.