It wasn’t too long ago that big data was thought of as a niche concept, something reserved for only those companies that were especially tech-savvy. Fast forward a few years and the popularity of big data has increased tremendously, with businesses of all shapes and sizes using it. Such is the way with most technologies, and Apache Spark is no exception. This should certainly come as no surprise considering how comfortable organizations have become with big data analytics, but this impressive growth deserves mentioning. Apache Spark has become mainstream, a framework in demand by all sorts of industries.
Spark’s popularity has once again entered the conversation thanks to a recent survey from Databricks showing how Spark’s momentum has strengthened in the past year. According to the survey, the number of users within the Spark community has increased threefold from 2015. Now they number around 225,000 members in total. This indicates that Spark adoption has made clear progression among businesses. Most technologies go through an early adoption phase as only a few organizations have the knowledge and resources to experiment with how best to use it. Once people become experienced with the technology, it then spreads further. We’re seeing this happening with Spark right now as more enterprises grow interested in Spark’s capabilities.
The Databricks survey isn’t the only place to go to see Spark’s growth. Other companies and vendors have shown similar numbers that speak to the versatility of Apache Spark. Qubole, for example, released some internal numbers which showed that half of their customers now used Spark for analytic processing. That processing on Qubole’s platform actually rose by an impressive 36 percent compared to the previous year. It’s also worth noting that Apache Spark has become the most active open source project related to big data. Not only are more people working on it, but they’re using Spark is more ways than ever before.
So what are the reasons for Spark’s rise in recent months? For one, the growth of cloud computing has facilitated the use of Spark by more businesses. Cloud services are becoming more varied, and that means more vendors are offering Spark as a cloud service that organizations can now take advantage of. Without the rise of the cloud, the rise of Spark would likely be severely muted.
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It’s not just the cloud that’s playing a major role in Spark’s mainstream appeal; many of its attributes are also major factors. Just as an example, the simplicity of spark when compared with other big data frameworks has led to its adoption by many businesses. These companies are trying to pursue big data projects but end up failing at it due to big data’s complexity. But Spark provides them with better performance and easier use which, when combined with other technologies like flash storage, helps them to handle their big data analytics needs. This, in turn, leads to more successful big data projects. Spark is also accessible to more people, in part because it allows for more common programming languages like SQL and R to be used. Since a wider array of talents can use Spark, it only makes sense for businesses to adopt it.
As a result, industries of all types have the ability to gain something from using Spark. Even industries that may not seem like they fit with big data, like betting sites or railways, have found that Spark provides them with added capabilities they didn’t have before. What’s equally important to note is that this surge in Spark use doesn’t appear to be slowing down. All the signs point to it continuing as more people get on board with the technology. Big data isn’t going away anytime soon, so Spark is going to keep growing along with it. The combination of new users and integration with other growing technologies indicates that Apache Spark is only in the beginning stages of its mainstream appeal. With more time, it will spread to even more businesses.
