Both Apache Spark and Hadoop are big-data frameworks; however they don’t exactly serve the same purposes. Apache Spark is a framework for executing general data analytics on distributed computing cluster such as Hadoop, whereas, Hadoop is a distributed data infrastructure, which distributes huge data collections across multiple nodes inside a cluster. I guess this clears things up that Spark doesn’t do distributed storage, it is a data-processing tool which operates on those distributed data collections.
Apache Spark runs on top of hadoop cluster and accesses hadoop data store (HDFS), it can also process Streaming data from HDFS, Kafka, Flume, Twitter.
Can we use Hadoop with Apache Spark?
Yes, Hadoop includes its own storage component, called the Hadoop Distributed File System, and also a processing component known as MapReduce, so we don’t essentially require Spark when working with Hadoop. Conversely, we can also use Spark without using Hadoop. Spark doesn’t have its own file management system, therefore, it needs integration with one – if you don’t want to use HDFS, then you can go for some other cloud-based data platform.
Why Spark is speculated to replace Hadoop?
The main reason behind this speculation is that Spark is a lot faster than MapReduce due to of the way it processes data. MapReduce operates in steps; while Spark operates on the complete data set in one go. Spark could be so far as 10 times faster than MapReduce for batch processing and nearly 100 times faster in in-memory analytics.
Other key features that make Spark Stand-out:
- Platform Independence
Coming back to the question “Is Apache Spark going to replace Hadoop”, to conclude we’ll say Hadoop Training is not just one thing which can be replaced by another thing. Hadoop is in fact a large ecosystem of many components. Spark on its own has no corresponding item for a lot of what’s inside Hadoop ecosystem (HDFS, M/R, Sentry, Zookeeper, Hue etc.) yet the fact remains that Spark is already replacing MapReduce for a number of batch-processing job within Hadoop clusters. Perhaps, the rise of Spark is a sign for Hadoop to expand beyond its existing services.