AI News, Spark Or Hadoop: Which Is The Best Big Data Framework?
- On 17. oktober 2018
- By Read More
Spark Or Hadoop: Which Is The Best Big Data Framework?
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.
Machine learning–creating algorithms which can “think” for themselves, allowing them to improve and “learn” through a process of statistical modelling and simulation, until an ideal solution to a proposed problem is found, is an area of analytics which is well suited to the Spark platform, thanks to its speed and ability to handle streaming data.
This sort of technology lies at the heart of the latest advanced manufacturing systems used in industry which can predict when parts will go wrong and when to order replacements, and will also lie at the heart of the driverless cars and ships of the near future.
There is some crossover of function, but both are non-commercial products so it isn’t really “competition” as such, and the corporate entities which do make money from providing support and installation of these free-to-use systems will often offer both services, allowing the buyer to pick and choose which functionality they require from each framework.
For example, if your Big Data simply consists of a huge amount of very structured data (i.e customer names and addresses) you may have no need for the advanced streaming analytics and machine learning functionality provided by Spark.
The increasing amount of 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).
- On 14. april 2021
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