‘An open source data analytics cluster computing framework’ is the most standard definition of Apache Spark. A big step in Big Data computing, Apache Spark has been receiving positive responses from the Big Data community as a quick, high stream, inclusive framework.
Apache Hadoop, the open source framework known for distribution and processing of massive sets of data, traditionally holds fort since a couple of years and has its own revolutionary features to attract its audience and hold the Big Data community in its loyalty. Born with a need to handle variety, volume and velocity of data, Apache Hadoop has always enabled organizations to have a quick insight into their heaps of structured and unstructured data and hence there is no doubt that Hadoop has its own share of advantages, benefits and fan following which span across a multitude of industries like finance, government, health care, retail, entertainment, media and many more.
But, as popularly known, ‘There is nothing permanent but change’. So, here comes Apache Spark to compete with Apache Hadoop as the next upcoming framework in Big Data computing. Will Apache Spark overrule Apache Hadoop and take over? Will Hadoop prove its stability and worth over Spark and stay confirmed as one of the best SDKs? These questions are sure to get answered over a period of time, with facts and figures, which will come out of implementation of both these stalwarts.
Let us glance through the innovative features that each of them offers and even though Spark may offer many more salient features, Hadoop has its feet grounded in the Big Data industry and it is tough to get its popularity low.
As of now, it has been ruling the Big Data community with its enriched features which have been tried and tested since years.
- Hadoop, because of its age and seniority is much more mature and there are numerous tools written on top of it. Owing to its popularity, there is much more built on Hadoop than Spark.
- Stability is yet another key feature Hadoop possesses staunchly. Since it has its foothold since years, it has created a stable environment for its users and if you are looking for a mature, stable framework, Hadoop still fits the bill.
Since it is the latest, it is bound to come with certain novel features on top of existing frameworks and hence attract users.
- It offers high velocity analytics by stream processing huge amounts of data as against the traditional way of batch oriented approach.
- Quicker and multiple analytics processing possible because of presence of a more inclusive framework with viable options like graph and streaming analytics, swift queries, machine learning and interactive features.
- Because it runs in memory and does not deal with the disk I / O, it is quite faster as compared to Hadoop.
- It skill to link datasets across numerous different data sources is quite welcomed in the developer community which is why many developers have started adapting Spark.
- It is much easier to configure and execute and can support any Hadoop I/O format, so it is much easier to connect to your Hadoop configuration along with Spark.
The good part is that if needed, it is possible for Hadoop and Spark to co-exist and run together. Tough to say, which supersedes the other, as of now, both of them share their own loyalty space amongst the user community but Hadoop will have to try hard to subdue the eye striking benefits that Spark has been showering and Spark will have to gain stability and maturity to outshine Hadoop.