John Podesta & al. Big Data : Seizing Opportunities, preserving values. Executive Office of the President, May 2014.
 François Bourdoncle. “Peut-on créer un écosystème français du Big Data ?”, Le Journal de l’Ecole de Paris n°108, Juillet/Aout 2014.
 Viktor Mayer-Schönberger, Kenneth Cukier. Big Data – A Revolution That Will Transform How We Live, Work and Think. John Murray, 2013.
 Gilles Babinet. L’ère numérique, un nouvel âge de l’humanité : Cinq mutations qui vont bouleverser votre vie. Le Passeur, 2014.
 Phil Simon. The Age of The Platform – How Amazon, Apple, Facebook and Google have redefined business. Motion Publishing, 2011.
 IBM Global Business Services, « Analytics : Real-world use of big data in telecommunications – How innovative communication service providers are extracting value from uncertain data”. IBM Institute for Business Value, Avril 2013.
 Thomas Dapp. “Big Data – The untamed force”, Deutsche Bank Research, May 5, 2014.
 David Lazer, Ryan Kennedy, Gary King, Alessandro Vespignani. “The Parable of Google Flu: Traps in Big Data Analysis”
 Tim Harford. “Big data: are we making a big mistake?”, Financial Times, March 28th, 2014.
 Octo Technology. Les géants du Web : Culture – Pratiques - Architecture. Octo 2012.
 Tony Hey, Stewart Tansley, Kristin Tolle (eds). The Fourth Paradigm – Data-Intensive Scientific Discovery. Microsoft Research, 2009.
 Max Lin. “Machine Learning on Big Data – Lessons Learned from Google Projects”.
 Michael Kopp. “Top Performance Problems discussed at the Hadoop and Cassandra Summits”, July 17, 2013.
 Eddy Satterly. « Big Data Architecture Patterns ».
 Paul Ohm. “Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization”. UCLA Law Review, Vol. 57, p. 1701, 2010
 CIGREF, « Big Data : La vision des grandes entreprises », 2013
- Massively parallel programming because of the distribution of very large amount of data. The data distribution architecture becomes the software architecture because, as the volume grows, it becomes important to avoid “moving data”.
- Sub-linear algorithms (whose compute time grows slower than the amount of data that they process) play a key role. We heard many great examples about the importance of such algorithms, such as the use of Hyperloglog counters in the computation of Facebook social graph diameter.
- Algorithms need to be adaptive and tuned incrementally from their data. Hence machine learning becomes a key skill when one works on a very large amount of data.
- Big Data is much more than new opportunities to do new things. Fueled by a technology shift that is caused by drastic price drops (storage and computing), Big Data paradigm causes a disruption about how to build information systems.
- Massive parallelism and huge volumes of data are bringing a new way of programming that is urgent to learn, and to teach. This goes for companies as well as universities or engineering schools.
- The old world of cautious “analyze/model/design/run” waterfall linear projects is in competition with a new world of systemic loops “experiment/learn/try/check”. This is true for science  as well as for business. Hence, Big data’s new paradigms needs to be taught in business schools as well as in engineering schools.