Big Data: Hype Vs. Hope At Interop

See on Scoop.it – Corporate Challenge of Big Data

Upcoming Interop workshop will focus on helping organizations see through big data hype and focus on what matters…For a consumer company like Netflix, which delivers movies and TV shows to a vast and diverse audience, big data can be used to improve suggestions and recommendations for what customers might want to watch next. Jeremy Edberg, reliability architect for Netflix and former Information Cowboy for reddit, will provide practical examples of how to use big data to help make businesses more successful. 
Municipalities and other government entities and agencies also are making gains by using big data to improve services and spend money more wisely. Brett Goldstein, CIO for the city of Chicago, will appear at Interop to describe how Chicago Department of Innovation and Technology is using big data to accelerate the city’s growth as a global hub of innovation and technology.

See on www.informationweek.com

In Media, Big Data Is Booming but Big Results Are Lacking

See on Scoop.it – Corporate Challenge of Big Data

Until recently, we have had startlingly few case studies of the transformative power of Big Data on which to model our own big changes in media. Instead we’ve had IT initiatives that promised big insights, but ended up delivering big databases and bigger IT bills. For once, it’s not the IT department’s fault — it’s those of us who are using the data (and, more often, aren’t using it) who are to blame. - Ben Elowitz, CEO, Wetpaint

See on allthingsd.com

Introducing: Project Open Data | The White House

See on www.whitehouse.gov

Last week, President Obama launched the Administration‘s new Open Data Policy and Executive Order aimed at ensuring that data released by the government will be as accessible and useful as possible.

Project Open Data is an online, public repository intended to foster collaboration and promote the continual improvement of the Open Data Policy.

See on www.whitehouse.gov

High Scalability – The Secrets of Scaling at Facebook

See on Scoop.it – Corporate Challenge of Big Data

Aditya Agarwal, Director of Engineering at Facebook, gave an excellent “Scale at Facebook” talk that covers their architecture, but the talk is really more about how to scale an organization by preserving the best parts of its culture.

The Key Points :-

1. Scaling takes Iteration
2. Don’t Over Design
3. Choose the right tool for the job, however your choice comes with overhead.
4. Get the culture right.. Move Fast – break things.
5. Huge Impact – small teams.
6. Be bold – innovate.

See on highscalability.com

High Scalability – High Scalability – Scaling Pinterest – From 0 to 10s of Billions of Page Views a Month in Two Years

See on Scoop.it – Corporate Challenge of Big Data

Pinterest has been riding an exponential growth curve, doubling every month and half. Pinterest’sYashwanth Nelapati and Marty Weiner, tell the dramatic story of Pinterest’s architecture evolution in a talk titled Scaling Pinterest.

 

Two lessons from the talk:

Architecture is doing the right thing when growth can be handled by adding more of the same stuff.Evaluate tool choices with a preference for tools that are: mature; really good and simple; well known and liked; well supported; consistently good performers; failure free as possible; free. Using these criteria they selected: MySQL, Solr, Memcache, and Redis.
See on highscalability.com

Why ‘big data’ is getting bigger every day – timesofmalta.com

See on Scoop.it – Corporate Challenge of Big Data

Machine data is changing the ‘big data’ landscape irrevocably

Correlating that machine data – data at rest, dark data in sensors and human unstructured data – could help you to see opportunities and threats earlier than the competition. 

Machine data is now the fastest growing element of big data and is set to represent 90 per cent of all data.

The ability to analyse and derive insight from all this machine data is where the big prize is in big data, but up until now, its value has been largely ignored by businesses.

See on www.timesofmalta.com

Splice data scientist DNA into your existing team

See on Scoop.it – Corporate Challenge of Big Data

Unfortunately, the job description for a data scientist has become quite lofty. Unless your company is Google-level cool, you’re going to struggle to hire your big data dream team (well, at least right now), and few firms out there could recruit them for you. Ultimately, most organizations will need to enlist the support of existing staff to achieve their data-driven goals, and train them to become data scientists. To accomplish this, you must determine the basic elements of data scientist “DNA” and strategically splice it into the right people. - By Jim Kaskade, CEO, Infochimps

See on www.itworld.com