September 2017
Beginner to intermediate
360 pages
8h 13m
English
book
Practical Real-time Data Processing and Analytics
by Shilpi Saxena, Selva raj Ramasamy, Prateek Bhati, Saurabh Gupta
Content preview from Practical Real-time Data Processing and Analytics
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Lambda architecture – analytics possibilities
Now that we have introduced this wonderful architectural pattern, let's take a closer look at it before delving into the possible analytic use cases that can be implemented with this new pattern.
We all know that, at base level, Hadoop gives me vast storage, and has HDFS and a very robust processing engine in the form of MapReduce, which can handle a humongous amount of data and can perform myriad computations. However, it has a long turnaround time (TAT) and it's a batch system that helps us cope with the volume aspect of big data. If we need speed and velocity for processing and are looking for a low–latency solution, we have to resort to a real–time processing engine that could quickly process ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.