Though it works similar way, big data projects needs both Apache Spark and Hadoop!

In this revolutionary era of big data technology, Hadoop and Apache Spark remains strong contenders in spite of being an open source resource. Both Hadoop and Apache Spark are products of Apache and more or less intended for similar purposes. There are plenty of differences you can notice when you learn Apache Spark and Hadoop but they are not exclusive to one another. Hadoop and Apache Spark are both Big Data frameworks–they provide some of the most popular tools used to carry out various Big Data-related tasks.

For years, Apache Hadoop remained the king of open-source Big Data framework until then the Apache Spark is released with highlighting advantages. We can say that this two foundation software from Apache is not mutually exclusive because the can effectively works together. Although Apache Spark is reported to work up to 100 times faster than Hadoop in certain circumstances, it does not provide its own distributed storage system.

Distributed storage is fundamental to many of today’s Big Data projects as it allows vast multi-petabyte datasets to be stored across an almost infinite number of everyday computer hard drives, rather than involving hugely costly custom machinery which would hold it all on one device. These systems are scalable, meaning that more drives can be added to the network as the data set grows in size.

Apache Spark does not include its own system for organizing files in a distributed way (the file system) so it requires one provided by a third-party. For this reason, many Big Data projects involve installing Apache Spark on top of Hadoop, where Apache Spark’s advanced analytics applications can make use of data stored using the Hadoop Distributed File System (HDFS).

What really gives Apache Spark the edge over Hadoop is speed. Apache Spark handles most of its operations “in memory” – copying them from the distributed physical storage into far faster logical RAM memory. This reduces the amount of time-consuming writing and reading to and from slow, clunky mechanical hard drives that need to be done under Hadoop’s MapReduce system.

MapReduce writes all of the data back to the physical storage medium after each operation. This was originally done to ensure a full recovery could be made in case something goes wrong – as data held electronically in RAM is more volatile than that stored magnetically on disks. However, Apache Spark arranges data in what are known as Resilient Distributed Datasets, which can be recovered following failure.

Apache Spark’s functionality for handling critically advanced data processing jobs. It can perform fluently the processes such as real-time stream processing and machine learning is way ahead of what is possible with Hadoop alone. This, along with the gain in speed provided by in-memory operations, is the real reason, in my opinion, for its growth in popularity. The increasing amount of Apache Spark activity taking place (when compared to Hadoop activity) in the open source community is, in my opinion, a further sign that everyday business users are finding increasingly innovative uses for their stored data. The open source principle is a great thing, in many ways, and one of them is how it enables seemingly similar products to exist alongside each other – vendors can sell both (or rather, provide installation and support services for both, based on what their customers actually need in order to extract maximum value from their data).