# Lecture 9: Oracle Databases at CERN

- Institution: CERN
- Department: CERN openlab Summer Student programme 2013
- Taught by: Eric Grancher, Maaike Limper
- Year: 2013
- Language: English
- Source: [CERN](https://videos.cern.ch/record/3004388)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Teens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

The presentation will describe the usage of Oracle and databases at CERN. It will present some of the key characteristics of the Oracle database management system. Several practical examples will be given and there will be a demonstration of how the wait event interface can be used to identify performance issues. The presentation also includes a description of the investigations being done inside the openlab context on the possibility of performing complex data analysis using database technology.

Maaike is a former ATLAS-physicist with a PhD in Particle Physics completed with the University of Amsterdam. She participated in the analysis of the first LHC data in a variety of ways: she worked on the construction of the ATLAS silicon tracker, wrote new data reconstruction software and developed some of the databases that store information on the ATLAS data-taking conditions. As of January 2012, Maaike joined the CERN IT Databases group as a CERN openlab Fellow funded by Oracle to help investigate the possibility of performing complex data analysis using database technology.

Eric has worked on Oracle technology since 1996 at CERN, especially concentrating on application and database performance as well as Real Application Cluster. He is currently working as senior database administrator and team leader for a team of database administrators. Eric is an OakTable network member since April 2005. He graduated from Telecom ParisTech (computing engineer) and Ecole Normale Supérieure de Lyon (fundamental computing master) in 1996..

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## Lectures

1. [Lecture 9: Oracle Databases at CERN](https://invisiblenetwork.ai/learn/cern/3004388/5b2c9afdfb3f), 1 h 7 min (CERN's player)