The information age that we live in is characterized by the copious amount of data generated from numerous devices and processes. Big data analysis is gaining popularity to gain intensive insights out of the data to improve processes and achieve greater efficiency.
Choosing the right platform like Hadoop or Spark is an imperative business decision that affects the accuracy, efficiency, and ease of big data analysis.
A short answer to this big question is that Hadoop and Spark should not even be compared! Both of them have some unique features besides the common functionalities. In fact, these were designed to be used in conjunction with each other to enhance performance.
Let’s discuss various aspects of both of these platforms to understand this short answer.
What is Hadoop?
Apache.org developed project Hadoop to enable anyone to process big data stored across remote computer clusters in a distributed manner with simple programming models. Hadoop is a framework comprising of several modules that synchronously work over several commodity systems, besides having its own software library.
The core modules of Hadoop are Common, Distributed File System, YARN, and MapReduce, besides many other extended modules like Oozie and Flume.
It has become a standard resource for companies handling humongous amounts of data, like Facebook.
What is Spark?
Spark was developed as a faster alternative for big data processing. It uses real-time in-memory processing along with disk-computing to stream workloads and is great for machine learning too.
The interesting thing about Spark is that Hadoop lists it as one of its modules! This makes this comparison quite tricky because Spark is great as a standalone unit as well as integrated with Hadoop.
Veterans expect Spark to grow into a more robust standalone platform in the future.
Comparison between Hadoop and Spark
Processing and Performance
Spark does the same work that Hadoop’s MapReduce does, but in lesser steps, hence making it faster. This is achieved because of in-memory processing as compared to batch processing of Hadoop.
This makes Spark a great platform for real-time analytics while Hadoop is suitable only to gather continuous information from different websites, that is not required in real-time.
You must define your requirements clearly before choosing the platform for analyzing the large-scale data for your website.
Spark was awarded the 2014 Daytona GraySort Benchmark for sorting 100 TB data around thrice as faster than its counterpart Hadoop, and that too with one-tenth of computers.
User-friendly Operation
Spark is a clear winner in terms of ease of use. Thanks to its interactive mode that gives developers and users the same feedback for various actions, like queries, in real-time. Besides, it has inbuilt applications for its native language, Scala, as well as Java, Python and even its own Spark SQL which is basically SQL 92 with slight modifications.
On the other hand, Hadoop requires plug-ins like Hive and Pig to make it slightly user-friendly to operate.
Cost Effectiveness
Well both of them are open source projects of Apache, making them absolutely free of cost to purchase. So it all boils down to the operational cost.
As seen in the performance section, Hadoop uses disk processing, while Spark deploys in-memory processing. Hence, Hadoop requires a lot of disk space and faster disks, while Spark requires faster RAM. This makes Spark costlier than Hadoop. But there is a catch in this. Spark needs much fewer machines to achieve the same results as that of Hadoop, thus making it more cost-effective for an increased amount of data.
Fault Tolerance
Spark’s use of Resilient Distributed Datasets (RDDs) makes its operations fault-tolerant without sacrificing the processing speed. These RDDs can run in parallel and be automatically computed from the original transformations in the event of a loss or fault.
On the other hand, Hadoop deploys TaskTrackers, which is great at tolerating faults but compromises on processing speed for that.
Conclusion
As seen from our discussions above, Spark emerges as a clear winner for large-scale data processing for all applications. But, that is not the exact case.
Spark is faster, easier to use and is great for real-time analytics but it is not cost-effective. Also, there are a lot of functionalities like Distributed File System that make Hadoop a better choice for many organizations.
So, we can fairly conclude that Spark and Hadoop are not mutually exclusive, rather they are symbiotic.
