“In the last quarter of 2015, IBM announced its plans to ingrain Spark into its industry-leading Analytics and Commerce platforms, and to offer Apache Spark as a service on IBM Cloud.”
The Experts also mentioned that IBM will proceed to put more than 3,500 IBM researchers to work on the Spark-related projects.
According to Technavio’s market research analysts, the global Hadoop market is predicted to grow at a compound annual growth rate (CAGR) of more than 53% over the next four years (2016-2019).
Now, having read the current growth trends in both the Apache’s open source technology platform- Hadoop and Spark, we could conclude their competency is no less than one another. So, the clutter here is what to choose for managing the big data explosion.
The selection depends on several factors, which directly concludes to knowing the difference in features and implementation. See below how Hadoop and Spark are comparable and how can they help you differently.
Individuality of tasks
On the other hand, Spark does not support distributed storage but allows data reuse on those distributed collections in an array of applications. It rather works on RDDs (Resilient Distributed Datasets), which provide excellent mechanisms for disaster recovery across clusters.
Can be used Separately
Nevertheless, many data analysts sill confides both of them to be used together and thus, Spark is said to operate on top of HDFS.
Speedier data management with Spark
Henceforth, Apache Spark can be 10-100X times faster.
Spark’s briskness isn’t necessary
RDDs enforce fault-tolerance and failure recovery in Spark
Spark uses a different resilient storage model, which guarantees 100% fault-tolerance and data recovery using built-in resiliency, minimizing the network I/O.
An important advantage coupled with Hadoop over Spark’s speed is that if the data size is larger than the memory, Spark will not be able to pull out its cache, and there is a possibility that it performs slower than the batch processing.
To conclude this, we can just give an evenhanded opinion that the selection between Apache Hadoop and Apache Spark is completely user-based and bound to information requirements and availability. Having a contemporary built-in memory structure, Spark is in prominence among developers and administrators; however, Analysts still focus on employing Hadoop processing to handle bulks of data.
This article is written by Vaishnavi Agrawal. She loves pursuing excellence through writing and have a passion for technology. She has successfully managed and run personal technology magazines and websites. She currently writes for intellipaat.com, a global training company that provides e-learning and professional certification training. The courses offered by Intellipaat address the unique needs of working professionals. She is based out of Bangalore and has an experience of 5 years in the field of content writing and blogging. Her work has been published on various sites related to Hadoop Training, Big Data, Business Intelligence, Cloud Computing, IT, SAP, Project Management and more. Follow her on Linkedin.