The project I delivered
In this section I collected the most important and interesting experiences I learned delivering projects
“Changes are not exceptions; they are part of the normal project live. Fostering change to initiate and maintain worthy initiatives is an art and science.”
Cloud and HAdOop
In the last decade, the adoption of Cloud services and HDFS Data lakes for storing data has been without pace. Today, we can definitively say that almost any IT department in the world is using Data lakes and/or Cloud services.
How to expand DWH using Data Lakes
Hadoop and Clouds are tremendous opportunities for Companies to transform their existing Data Warehouses (DWH) in big data analytic infrastructures. But to fully ...
The 5 golden rules to create an effective Data lake
With the time, Companies observed that querying and extracting data from the lakes was more complicated and costly than expected. Data Lakes were becoming Data Swamps. What happened? ...
Why do you need a semantic layer for your data lakes?
Data lakes is a tremendous opportunity for companies to transform their existing data infrastructure to support digital transformation. But to …
How to create hadoop-friendly data schema
To efficiently use Hadoop and Cloud systems, people must “respectful” of Hadoop internal mechanisms: they must use Hadoop-friendly data schema ...
Analytics
The linkage between the analytics capability and high performance companies is clear: companies using analytics to support decision-making perform twice better than competition
Associative analysis model for network alarm correlation
Machine learning and in particular associative analysis models can be used to automatically correlate network alarms without any knowledge of network or alarm sequence
Network equipment failure prediction
This article presents the project of a predictive analytics models used to predict network equipment failures and to reduce rectification activities associated with these failures.
Automatic root cause analysis of customer experience degradation
Associative analysis models can be used to automatically identify the root cause of a service failure or of a customer experience degradation
Working in progres
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