Portal:Storage

Wikitip

Operational Data Science

Team with diversity can work together for an enterprise to deliver Data Science workflow. 1. Person(s) with strong business acumen with Visualization skills. 2. Data Integration Engineer(s) with ETL skills on structured and unstructured data 3. Statistician with R skills 4. Smart programmer with Predictive modeling skills.

Interestingly, there is no right and specific order of delivery from these people. Having said that the person who has strong business background can work at both the ends of a shore i.e. in data discovery as well as in communicating the final result (either in terms of prediction or pattern or summarization). However, programmers can pretty much independently work in all areas of data preparation, data analysis and scripting to build datasets for modeling. In a same way, statistician can very much communicate the business result and reflection. Now after all these efforts what left is just a game of effective collaboration. Moreover, along with the right collaboration channel there should be a Data scientist(s) who can watch over and architect the whole work flow and should always be ready to design+code+test the prototype of the end product. So, this whole Operation Data Science need a collaborative team and an architect(s) with diverse skills. Read complete study on: http://datumengineering.wordpress.com/

View Another Wikitip

Featured Case Study

Virtualization Energizes Cal State University

John Charles is the CIO of California State University, East Bay (CSUEB) and Rich Avila is Director, Server & Network Operations. In late 2007 they were both looking down the barrel of a gun. The total amount of power being used in the data center was 67KVA. The maximum power from the current plant was 75kVA. PG&E had informed them that no more power could be delivered. They would be out of power in less than six months. A new data center was planned, but would not be available for two years.

read more...

Featured How-To Note

Storage Virtualization Design and Deployment

A main impediment to storage virtualization is the lack of multiple storage vendor (heterogeneous) support within available virtualization technologies. This inhibits deployment across a data center. The only practical approach is either to implement a single vendor solution across the whole of the data center (practical only for small and some medium size data centers) or to implement virtualization in one or more of the largest storage pools within a data center.

read more...