Big Data: Principles, Platforms, and Applications

Join us in the next session of the Cairo Data Science Meet-Up where Alberto Méndez, CTO of Stratio (a revolutionary big data platform with more than 120 data scientists and engineers in Silicon Valley and Europe) will share with us his experience building Stratio and working on numerous high impact big data projects. Alberto is in Egypt for less than a week and generously offered to make time to speak to our community. If there is one session you don't want to miss, make it this one!

Biography:

Alberto is a telecommunications engineer with a passion for technology, programming and Open Source. Alberto has been involved as CTO, for more than 15 years, in various companies and many large projects spanning various industries from insurance, to banking, to telco, always leveraging the latest technologies. This technological tour included 10 years to implement early Hadoop projects in Europe. 4 years ago, Alberto helped found Stratio whose aim is to create the first Big Data platform that is 100% Spark and with native integration with the best NoSQL databases. Headquartered in Silicon Valley and in Spain, Stratio has the most Big Data engineers and Spark projects in Europe.

Outline:

1. Why Spark vs Hadoop?

2. The next Big Data Platform generation: Spark + NoSQL + Relation DDBB distributed.

3. Stratio the End to End Platform, from ingestion to visualization all via drag & drop.

4. Real Big Data projects (videos) and what is in demand in today's market.

5. What MLlib has and what it doesn't have.

6. New Maching Learning algorithm Stratio is offering (developed in Spark):

6.1 Customer Segmentation: MLLib algorithms: K-Means, Streaming K-means, Gaussians mixture models, coming soon Hierarchical clustering. New algoritms developed by us: K-Prototype, Pattern Clustering, Dimension reduction: PCA

6.2 Prediction models: MLlibs: Bayesian models, Support Vector Machines (SVMs), Decision trees, Random forests

6.3 Time-series forecasting (Cloudera initiative under Spark)

6.4 Recommendation engine & User profiling