MapR Technologies Chief Marketing Officer, Jack Norris, talks about the advantages of Hadoop. He elaborates and multiple use cases and explains how MapR Technologies is the best Hadoop distribution.
Let’s start with this chart. To reinforce you’re in the right room you picked the right session…HadoopNot only is it the fastest growing Big Data technology…It is one of the fastest technologies period….Hadoop adoption is happening across industries and across a wide range of application areas.What’s driving this adoption
There are many drivers for Hadoop adoption…
One of the drivers for Hadoop adoption is storage costs… Dramatically cheaper….. You might say I can’t use raw disks because I need the high end availability and data protection and speed. We agree with you that’s where MapR focused bringing the performance and features of high end to Disk Attached Storage…This is a paradigm shift
Map Reduce is a paradigm shiftGoogle Poster ChildWhat exactly does Hadoop look like?
This is a Hadoop distribution it includes a series of open source packages that are tested, hardened and combined into a complete suite. With MapR we’ve combined this with our own innovations at the data platform level to make it highly available, dependable and easier to access and integrate through industry standards like NFS, ODBC, etc…
How do you benefit. I mentioned that used wide variety of use cases…I’ve generalized these into 4 groups… The first
Is expanding data….Sampled to all of the transactions, ….. Netflix….recommends 5 movies to you and. It’s because they look at everybody’s movie watching and ratings and identify like clusters of individuals like you….Risk triangles for insurance companies go from zip code level down to the neighborhood street…Trading information going for last 3 months to 7 years….
Let’s look at a specific example…
Load CDR – Call detail records into the data warehouse and transform data into the proper format for processing and analysis…
The problem with this process is that 70% of the EDW load is related to the CDR normalization process AI: Why is this the case?CDR normalization difficult within the EDWBinary extraction and conversion to SQL is difficult
The first is “simple algorithms and lots of data trump complex models”. This comes from an IEEE article written by 3 research directors at Google. The article was titled the “Unreasonable effectiveness of Data” it was reaction to an article called “The Unreasonable Effectives of Mathematics in Natural Science” This paper made the point that simple formulas can explain the complex natural world. The most famous example being E=MC2 in physics. Their paper talked about how economist were jealous since they lacked similar models to neatly explain human behavior. But they found that in the area of Natural Language Processing an area notoriously complex that has been studied for years with many AI attempts at addressing this. They found that relatively simple approaches on massive data produced stunning results. They cited an example of scene completion. An algorithm is used to eliminate something in a picture a car for instance and based on a corpus of thousands of pictures fill in the the missing background. Well this algorithm did rather poorly until they increased the corpus to millions of photos and with this amount of data the same algorithm performed extremely well. While not a direct example from financial services I think it’s a great analogy. After all aren’t you looking for an approach that can fill in the missing pieces of a picture or pattern.
Okay interesting graphs how does this translate to the real world. Here are some broad examples.
Start with the right platform…Power to address your needs and the flexibility to grow with your expansion..If you haven’t started with this platform it is easy to switch….
Take all of Twitter400 x 10^6 tweets per day < 400 GB per day < 40MB/s