High-Performance, Big Data Analytics Takes a Certain Understanding
- by Julie Lockner
- by Daniel, Data Doctor
- 2/22/2012 2:17:14 AM
I think for analysis along with platform, tools also matters. Whatever may be the platform, the ability of tools to interactive and make use of the underlined resources are important. If the tools are not compactable with the deployed hardware and associated software, then the analysis process may happen with lesser speed, there may be always certain bottlenecks.
- by Lyndon_Henry, Blogger
- 2/21/2012 10:10:25 PM
if you need to be able to execute a ranking algorithm in near real-time to identify a fraudulent activity, the model needs to be able to complete in microseconds to milliseconds. Delivering results in such speedy fashion means giving the business the ability to react quickly and with the agility that's much desired.
One would think credit card companies would be big users. I wonder approximately what the percentage is of credit card companies that are delopying this technology (HPC analytics etc.). I'd also wonder if this enables some more technologically forward-thinking companies to react to fraud faster than others.
- by Shawn Hessinger, Blogger
- by Hospice_Houngbo, Prospector
- 2/21/2012 6:51:42 PM
" high performance is relalitve"..."What makes one company agile isn't the same for everyone. "
High performance evaluation may indeed depend on the company's data processing needs and requirements. Companies should first try to audit and understand such needs in oder to choose the right analytics tools.
- by SethBreedlove, Data Doctor
- 2/21/2012 4:45:11 PM
I guess it would only make sense high performance is relalitve. that different companies would have different values on saving costs, performance and speed.
As mentioned in the article, banks need answers in milliseconds when it comes to fraud prevention, while Procter and Gamble would have very different needs.
What makes one company agile isn't the same for everyone.
- by Julie Lockner, Blogger
- 2/21/2012 2:39:18 PM
Hi Louis, This environment had both an HPC and data warehouse environment. The team wanted to be able to enhance the DW with more detailed data and have the HPC environment have access to it. This new architecture added Hadoop as the landing place for detailed data that was accessible to both environments. Once they found how much quicker it was to add data elements tot he model, that was just the beginning of new opportunities.
- by louisw900, Blogger
- 2/21/2012 12:45:01 PM
Hi Julie, Was this difference in job processing time in an Oracle World versus Hadoop due to HPC's alone ? Or were there any other changes ?
Are these high-end systems on the backend or HPC's ?
- by BethSchultz, Blogger
- 2/21/2012 12:14:14 PM
@JulieLockner, fewer false positives and quicker response time certainly would be ideal in fraud detection analytics! I'm curious, outside of latency-sensitive operational analytics are there some areas where companies just aren't willing to get quite as automated with their responses as they might?
- by Julie Lockner, Blogger
- 2/21/2012 11:42:45 AM
Hi Shawn, From an agility perspective, one Hadoop user had a challenge with their ability to add attributes to their Cognos cube for a risk model. The job took 4 days to complete with Oracle. When they switched the underlying data store to Hadoop, they were able to reduce that rebuild to 1 day. From a new opportunity perspective, another user was not able to support their business users' request to mine detailed transactional data to identify opportunities to improve business process efficiencies because the warehouse could not scale. The warehouse was only designed to look at aggregate data. By opening up detailed transactional data cost affordably on a Hadoop cluster, they could investigate as to why processes were inefficient.
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- by James M. Connolly