Everyone around the internet is constantly talking about the bright future of Apache Spark. How cool it is, how innovative it is, how fast it is moving, how big its community is, how big the investments into it are, etc. But what is really hiding behind this enthusiasm of Spark adepts, and what is the real future of Apache Spark?
In this article I show you the real data and real trends, trying to be as agnostic and unbiased as possible. This article is not affiliated with any vendor.
Yesterday my blog has got the 100th subscriber. To commemorate this, I prepared the post on the major industry trends happening in the field of “data”. I might miss something, so feel free to comment and extend the article with your opinion!
Big data is falling down the hype curve
Even though Gartner has removed “Big Data” from the last year’s hype diagram, it does not mean it suddenly moved from the peak of the “hype” to the plateau of adoption. Here is how the hype cycle look like:
Open source data community has been rapidly growing over the last 10 years. You can feel this by the emerge of projects like Apache Hadoop, Apache Spark and the likes. It is growing this fast that there is almost no chance of keeping up with its growth without constantly monitoring the related events, announcements and other changes. 10 years ago it was enough to know “just Oracle” or “just MySQL” to make a successful career in data. Now the things has greatly changed, and if you cannot answer questions like “what is the difference between MapReduce and Spark?” and “when would you prefer to use Flink over Storm?” at your job interview you are screwed.
Starting Apache Spark version 1.6.0, memory management model has changed. The old memory management model is implemented by StaticMemoryManager class, and now it is called “legacy”. “Legacy” mode is disabled by default, which means that running the same code on Spark 1.5.x and 1.6.0 would result in different behavior, be careful with that. For compatibility, you can enable the “legacy” model with spark.memory.useLegacyMode parameter, which is turned off by default.
The question regarding running Hadoop on a remote storage rises again and again by many independent developers, enterprise users and vendors. And there are still many discussions in community, with completely opposite opinions. I’d like to state here my personal view on this complex problem.