Over the last few years, you have invested heavily in building your Data Warehouse and Business Intelligence capabilities. And if you have done it right, you have been reaping the benefits of it in a meaningful way too. But we all know that it is handling only a reasonable amount of your structured data. Now we are in the Big Data era and you need to harness insights out of the large data sets – structured and un-structured – internal as well as external.
Monthly archive for March 2016
Apache Flink is the new star in the town. It is stealing the thunder from Apache Spark (at least in the streaming system) which has been creating buzz for some time now. This is because Spark streaming is built on top of RDDs which is essentially a collection, not a Stream. So now would be the right time to try your hands on Flink, even more so since Flink 1.0 was released last week.
Flink has two types of Windows – Tumbling Window and Sliding Window. The main difference between these windows is that Tumbling windows are non-overlapping whereas Sliding windows can beoverlapping.
In this article, I will try to explain these two windows and will also show how to write Scala program for each of these. Code used in this blog is also available in my Github Read more →