I have just read the “Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics” paper and decided to write a short blog post going through some of the key moments of the paper’s motivation. Let’s start.
I’d start with a bold statement: Hadoop is rapidly losing the momentum. We can see it from the following Google Trends chart:
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!
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:
Here is the video of my talk on Modern Data Architecture from Java Day Kiev 2015
The slides are available here: Modern Data Architecture – JD Kiev v05
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.
Github Data Community Graph Snapshot
Also, what would be the “next big thing” in data?
This is the talk I made on Java Day Kiev 2015. It was a great conference after all
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.
Previously I have described the “legacy” model of memory management in this article about Spark Architecture almost one year ago. Also I have written an article on Spark Shuffle implementations that briefly touches memory management topic as well.
This article describes new memory management model used in Apache Spark starting version 1.6.0, which is implemented as UnifiedMemoryManager.
Here are the slides for the talk I just gave at JavaDay Kiev about the architecture of Apache Spark, its internals like memory management and shuffle implementation:
If you’d like to download the slides, you can find them here: Spark Architecture – JD Kiev v04
Here are the slides for the talk I just gave at JavaDay Kiev about the modern data architecture and different modern approaches of data processing:
If you’d like to download the slides, you can find them here: Modern Data Architecture – JD Kiev v05