This document contains the resume of Franck Victoria, an Oracle and MS SQL Database Administrator and Data Warehouse Performance Tuning Specialist. It outlines his extensive experience as a DBA working on various projects at companies like BNP Paribas, Rolex SA, and Alcan Aluminium Valais SA. It also lists his technical skills and certifications in databases like Oracle, MS SQL Server, and IBM DB2.
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
Business Intelligence: Multidimensional AnalysisMichael Lamont
An introduction to multidimensional business intelligence and OnLine Analytical Processing (OLAP) suitable for both a technical and non-technical audience. Covers dimensions, attributes, measures, Key Performance Indicators (KPIs), aggregates, hierarchies, and data cubes.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
MLOps with serverless architectures (October 2018)Julien SIMON
Talk @ AWS Loft Stockholm, 23/10/2018
But why?
A quick recap on Amazon SageMaker
A quick recap on serverless architectures
Open Source tools: AWS Chalice, Serverless Framework
Demos
Resources
OpenBOM: Neo4j and Bill of Materials meetup, BostonOleg Shilovitsky
OpenBOM is using Neo4j to build global graph relationships, to provide structural queries and glean intelligence for decision making in engineering, manufacturing and supply chain.
Check more information on our website - www.openbom.com
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
Business Intelligence: Multidimensional AnalysisMichael Lamont
An introduction to multidimensional business intelligence and OnLine Analytical Processing (OLAP) suitable for both a technical and non-technical audience. Covers dimensions, attributes, measures, Key Performance Indicators (KPIs), aggregates, hierarchies, and data cubes.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
MLOps with serverless architectures (October 2018)Julien SIMON
Talk @ AWS Loft Stockholm, 23/10/2018
But why?
A quick recap on Amazon SageMaker
A quick recap on serverless architectures
Open Source tools: AWS Chalice, Serverless Framework
Demos
Resources
OpenBOM: Neo4j and Bill of Materials meetup, BostonOleg Shilovitsky
OpenBOM is using Neo4j to build global graph relationships, to provide structural queries and glean intelligence for decision making in engineering, manufacturing and supply chain.
Check more information on our website - www.openbom.com
Making the move to a document database can be intimidating. Yes, its flexible data model gives you a lot of choices, but it also raises questions: Which way is the right way? Is a document database even the right tool? Join this live session on the basics of data modeling with JSON to learn:
- How a document database compares to a traditional RDBMS
- What JSON data modeling means for your application code
- Which tools might be helpful along the way
The examples in this session use the free, open-source Couchbase Server document database, but the principles you’ll learn can also be applied to Cosmos DB, MongoDB, RavenDB, and others.
Data-Ed Slides: Best Practices in Data Stewardship (Technical)DATAVERSITY
In order to find value in your organization's data assets, heroic data stewards are tasked with saving the day- every single day! These heroes adhere to a data governance framework and work to ensure that data is: captured right the first time, validated through automated means, and integrated into business processes. Whether its data profiling or in depth root cause analysis, data stewards can be counted on to ensure the organization's mission critical data is reliable. In this webinar we will approach this framework, and punctuate important facets of a data steward’s role.
Learning Objectives:
- Understand the business need for a data governance framework
- Learn why embedded data quality principles are an important part of system/process design
- Identify opportunities to help drive your organization to a data driven culture
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Practicing Data Science: A Collection of Case StudiesKNIMESlides
This is a review of case studies: from easy to complex, from standard to creative. We'll start with a classic churn prediction classification problem and move to another classic - demand prediction. Then we'll determine if and how much easy, standard model training can be automatized. Finally, we'll review a few more creative case studies like AI generated rap songs or neuro-styling of passport images.
First presented by Rosaria Silipo (KNIME) at Strata New York, September 2019.
The business dimensional life cycle. Summarized from the second chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
Informatica Cloud 101: Fast Track to Integration with IntuitInformatica Cloud
Data Integration on the Cloud is a not just a trend... it is a proven, secure, efficient way to get you from fragmented to integrated in no time. 92% of IT executives say adoption of Cloud technologies is good value for business. In this session, learn more about Informatica's industry leading Cloud integration solution and how easily you may increase the ROI on your Cloud investments
In this session, you will learn:
* How to use Informatica Cloud to build an end-to-end data integration in less than 5 minutes
* Informatica's Cloud offering for data integration, data replication, data quality and MDM
* How Intuit is leveraging Informatica Cloud for Salesforce integration
* Best practices on Hybrid IT and Cloud adoption
How to Build Data Governance Programs That Last: A Business-First ApproachPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves governance leaders and data stewards having to continually make the case for data governance to secure business adoption.
In this introductory session, we will share the core components of a business-first data governance approach that promotes organizational adoption, lays the foundation for data integrity, and consistently delivers business value for the long term.
RWDG Slides: A Complete Set of Data Governance Roles & ResponsibilitiesDATAVERSITY
Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From Data Stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role and how the role fits in with their existing job.
Join Bob Seiner for this RWDG webinar where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an Operating Model of Roles and Responsibilities that can be customized to address the specific needs of your organization.
In this webinar, Bob will discuss:
- Executive, Strategic, Tactical, Operational, and Support Level Roles
- How to customize an Operating Model to fit your organization
- Detailed Responsibilities for each level
- Defining who participates at each level
- Using working teams to implement tactical solutions
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...Alan McSweeney
These notes describe a generalised data integration architecture framework and set of capabilities.
With many organisations, data integration tends to have evolved over time with many solution-specific tactical approaches implemented. The consequence of this is that there is frequently a mixed, inconsistent data integration topography. Data integrations are often poorly understood, undocumented and difficult to support, maintain and enhance.
Data interoperability and solution interoperability are closely related – you cannot have effective solution interoperability without data interoperability.
Data integration has multiple meanings and multiple ways of being used such as:
- Integration in terms of handling data transfers, exchanges, requests for information using a variety of information movement technologies
- Integration in terms of migrating data from a source to a target system and/or loading data into a target system
- Integration in terms of aggregating data from multiple sources and creating one source, with possibly date and time dimensions added to the integrated data, for reporting and analytics
- Integration in terms of synchronising two data sources or regularly extracting data from one data sources to update a target
- Integration in terms of service orientation and API management to provide access to raw data or the results of processing
There are two aspects to data integration:
1. Operational Integration – allow data to move from one operational system and its data store to another
2. Analytic Integration – move data from operational systems and their data stores into a common structure for analysis
Making the move to a document database can be intimidating. Yes, its flexible data model gives you a lot of choices, but it also raises questions: Which way is the right way? Is a document database even the right tool? Join this live session on the basics of data modeling with JSON to learn:
- How a document database compares to a traditional RDBMS
- What JSON data modeling means for your application code
- Which tools might be helpful along the way
The examples in this session use the free, open-source Couchbase Server document database, but the principles you’ll learn can also be applied to Cosmos DB, MongoDB, RavenDB, and others.
Data-Ed Slides: Best Practices in Data Stewardship (Technical)DATAVERSITY
In order to find value in your organization's data assets, heroic data stewards are tasked with saving the day- every single day! These heroes adhere to a data governance framework and work to ensure that data is: captured right the first time, validated through automated means, and integrated into business processes. Whether its data profiling or in depth root cause analysis, data stewards can be counted on to ensure the organization's mission critical data is reliable. In this webinar we will approach this framework, and punctuate important facets of a data steward’s role.
Learning Objectives:
- Understand the business need for a data governance framework
- Learn why embedded data quality principles are an important part of system/process design
- Identify opportunities to help drive your organization to a data driven culture
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Practicing Data Science: A Collection of Case StudiesKNIMESlides
This is a review of case studies: from easy to complex, from standard to creative. We'll start with a classic churn prediction classification problem and move to another classic - demand prediction. Then we'll determine if and how much easy, standard model training can be automatized. Finally, we'll review a few more creative case studies like AI generated rap songs or neuro-styling of passport images.
First presented by Rosaria Silipo (KNIME) at Strata New York, September 2019.
The business dimensional life cycle. Summarized from the second chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
Informatica Cloud 101: Fast Track to Integration with IntuitInformatica Cloud
Data Integration on the Cloud is a not just a trend... it is a proven, secure, efficient way to get you from fragmented to integrated in no time. 92% of IT executives say adoption of Cloud technologies is good value for business. In this session, learn more about Informatica's industry leading Cloud integration solution and how easily you may increase the ROI on your Cloud investments
In this session, you will learn:
* How to use Informatica Cloud to build an end-to-end data integration in less than 5 minutes
* Informatica's Cloud offering for data integration, data replication, data quality and MDM
* How Intuit is leveraging Informatica Cloud for Salesforce integration
* Best practices on Hybrid IT and Cloud adoption
How to Build Data Governance Programs That Last: A Business-First ApproachPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves governance leaders and data stewards having to continually make the case for data governance to secure business adoption.
In this introductory session, we will share the core components of a business-first data governance approach that promotes organizational adoption, lays the foundation for data integrity, and consistently delivers business value for the long term.
RWDG Slides: A Complete Set of Data Governance Roles & ResponsibilitiesDATAVERSITY
Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From Data Stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role and how the role fits in with their existing job.
Join Bob Seiner for this RWDG webinar where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an Operating Model of Roles and Responsibilities that can be customized to address the specific needs of your organization.
In this webinar, Bob will discuss:
- Executive, Strategic, Tactical, Operational, and Support Level Roles
- How to customize an Operating Model to fit your organization
- Detailed Responsibilities for each level
- Defining who participates at each level
- Using working teams to implement tactical solutions
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...Alan McSweeney
These notes describe a generalised data integration architecture framework and set of capabilities.
With many organisations, data integration tends to have evolved over time with many solution-specific tactical approaches implemented. The consequence of this is that there is frequently a mixed, inconsistent data integration topography. Data integrations are often poorly understood, undocumented and difficult to support, maintain and enhance.
Data interoperability and solution interoperability are closely related – you cannot have effective solution interoperability without data interoperability.
Data integration has multiple meanings and multiple ways of being used such as:
- Integration in terms of handling data transfers, exchanges, requests for information using a variety of information movement technologies
- Integration in terms of migrating data from a source to a target system and/or loading data into a target system
- Integration in terms of aggregating data from multiple sources and creating one source, with possibly date and time dimensions added to the integrated data, for reporting and analytics
- Integration in terms of synchronising two data sources or regularly extracting data from one data sources to update a target
- Integration in terms of service orientation and API management to provide access to raw data or the results of processing
There are two aspects to data integration:
1. Operational Integration – allow data to move from one operational system and its data store to another
2. Analytic Integration – move data from operational systems and their data stores into a common structure for analysis
The AMIS Report from Oracle Open World and JavaOne 2011 - Part OneLucas Jellema
The first part of the report from the AMIS team on their findings of Oracle Open World 2011 and JavaOne 2011. With the major announcements, the roadmaps, highlights and disappointments, some gold nuggets and personal bests and a general impression of where Oracle, the industry trends and the technology are going.
Oracle DBA - Oracle Apps DBA - Technical Architect - IT Infrastructure Management - IT Application Management
Snap Information:
Title: Oracle DBA / Oracle APPS DBA Experience
· 17+ years of experience in IT Infrastructure as Oracle Apps DBA, Oracle DBA, Oracle RAC DBA, ODA, DBA & SQL DBA. Exadata x2, Exadata x 5 & Exadata x 8 administration, Technical Architect, Project management, IT Infra Lead. 10 years exclusively in UAE.Supported on different versions and platforms of database and ERP up to 19c database and 12.2.x ERP version……….
· Designed and implemented ODA 5x servers for database.
· Designed and implemented Exadata servers for ERP database.
· Oracle Database production support, Oracle RAC support, Cloning, Disaster Recovery.
· 40+ upgrades, migration and Implementation projects on different versions and platforms of Oracle ERP and database.
· Certified Six Sigma Green Belt and OCP. Completed trainings on PMP and Togaf9.2
Hi, I have made a [successful] habit of reaching. I have done boutique and enterprise ERP consulting. I have delivered code for networks, CRM, and advertising data warehouse development teams. I have architected and moved systems. I have led data migrations, administered production support, and led integrations across varied platforms. Now, I want to stretch your team's production.
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CV - Database Administrator ( English )
1. FRANCK VICTORIA
Oracle & MS SQL Database Administrator
Data Warehouse Performance Tuning Specialist
PERSONAL PROFILE
Animated by the passion and the technical challenges related to my profession, it is with rigor, method and objectivity that I apprehend
the different missions entrusted to me. Pragmatic and undertaker, I make my choices of technical solutions always in favor of the
objectives and interests of my company.
EXPERIENCES
Database Administrator BNP PARIBAS Partner for innovation
Data Warehouse Performance Team Partners SA
Tuning Specialist
03/2010 to 12/2016
Database Administrator ROLEX SA
Production and Development Alpha Sarl
04/2006 to 02/2010
Database Administrator ALCAN ALUMINIUM VALAIS SA
System Manager
03/2005 to 07/2005
Database Administrator WEBTECH SOLUTIONS
CEO
01/2000 to 06/2004
FORMATIONS
Oracle Database 12c ORACLE UNIVERSITY
Managing Multitenant Architecture Ed 1
06/2016
Oracle Database 12c ORACLE UNIVERSITY
Install And Upgrade Workshop Ed 1
06/2016
IBM Database DB2 10.1 SATOM IT
Administration for Linux and Windows
03/2015
Oracle Database 11g ORACLE UNIVERSITY
RAC Administration Ed 4
10/2014
Oracle Database 11g ORACLE UNIVERSITY
Data Guard Administration Ed 3
09/2014
Microsoft SQL Server 2012 DIGICOMP
Administering MS SQL 2012 Databases
03/2013
Oracle Database 11g ORACLE UNIVERSITY
Administration Workshop Ed 2
11/ 2012
Oracle Database 10g TRIVADIS
New Features
03/ 2007
PROFESSIONAL SKILLS
Administration RDBMS
OLTP & Data Warehouse
High Availability
Performance Tuning
Architecture
Design & Modeling
Development
Project Management
LANGUAGES
English
TECHNICAL SKILLS
Oracle
Microsoft SQL Server
IBM DB2
Java ( Servlet )
Unix
Linux
Windows
CONTACT INFO
Address : Rue de Genève 129
1226 THONEX
Home phone : +41 22 348 78 70
Cell phone : +41 78 906 08 28
E-mail : f.victoria@bluewin.ch
C licence valid until 31.05.2020
FOLLOW ME
Linked in : https://www.linkedin.com/in/franck-victoria-b2453858
2. FRANCK VICTORIA
Oracle & MS SQL Database Administrator
Data Warehouse Performance Tuning Specialist
EXPERIENCES
Database Administrator
Data Warehouse Performance
Tuning Specialist
03/2010 to 12/2016
BNP PARIBAS Partner for innovation
Team Partners SA
Performance / Tuning :
Audit , monitoring and load testing.
Optimization of SQL queries, PL / SQL and batch processes.
Optimization of the data model.
Optimization of the instance.
System and storage optimization ( Disk, VG, FS...).
Development and automation of PL/SQL administration and optimization packages.
Installations / Patchs :
Oracle RDBMS 11.2.0.4, Data Guard, RMAN, Oracle Grid Infrastructure, OAE, Clients, Oracle Transparent Gateway
Microsoft SQL Server 2008 R2, 2012 Standalone / Always On
IBM DB2 9.7, 10.5
Migrations :
Oracle 8i, 9i et 10g => Oracle 11.2.0.4
MSSQL 2000, 2003, 2005, 2008 R2 => MSSQL 2012
Task Force / Project : CIB DWH Europe et Gulf – Oracle 11.2.0.3 & Data Guard ( role : DWH Performance Tuning Specialist )
Definition of the technical base specific to the CIB infocenters ( VIO, OS, Disks, VG, FS , Oracle instance ).
Migration of the 17 European infocenters and the 2 infocentres of the Gulf ( Oracle 10g => Oracle 11.2.0.3 ).
Optimization of instances, SQL queries (BO), PL/SQL and batch processing.
Development and putting into production of a set of PL/SQL packages (EzDBA - Automatic reorganization, statistics ...).
Optimization of the Data Guard parameters of the 19 infocentres.
Project : EQUITRAC Oracle 11.2.0.4 & Data Guard ( role : Dba )
Project : SHAREPOINT Microsoft SQL Server 2012 & Always On ( role : Dba )
Project : CITRIX AZUL Microsoft SQL Server 2012 & Always On ( role : Dba )
Project : NIMSOFT Microsoft SQL Server 2012 & Always On ( role : Dba )
Project : EzTOOLS – EzREF Oracle 11.2.0.4 & Data Guard ( role : Project Manager & Dba )
Study, realization and implementation of a tool (CMDB) allowing the management of the database of the Swiss park (1000
Instances) and the French park (8000 Instances) in terms of Scoring, Capacity Management, Options and Licenses...
Developed in PL/SQL, Java, Perl, Shell and Oracle Application Express 5.0.4.
Project : EzTOOLS – EzMON Oracle 11.2.0.4 & Data Guard ( role : Project Manager & Dba )
Design, realization and implementation of a multi-engine database monitoring tool (Oracle, MSSQL, Sybase, MySQL.) Without
agent. Powered by EzREF.
Perimeter Switzerland : + 300 Multi-RDBMS Production Instances.
Perimeter France : + 2800 Oracle instances of backup and production ( Standalone, HACMP, RAC, Data Guard, Always On ).
Developed in PL/SQL, Java, Perl, Shell and Oracle Application Express 5.0.4.
Project : EzTOOLS – EzNSM Oracle 11.2.0.4 & Data Guard ( role : Project Manager & Dba )
Study, realization and implementation of a tool allowing the monitoring of the configuration of the monitoring of the hosts of the
Swiss park. Powered by EzREF and collected from agents of the NSM CA product of each supervised server.
Developed in PL/SQL, Shell and Oracle Application Express 5.0.4.
Project : EzTOOLS – EzCRM Oracle 11.2.0.4 & Data Guard ( role : Dba )
Realization and implementation of an application allowing to manage the service catalog of BNP PARIBAS Partner for Innovation,
as well as the costing of a resource request of the type Hardware, Software, Database or Human Day carried out by the business
teams.
Powered by EzREF and LIPS.
Developed in PL/SQL, Shell and Oracle Application Express 5.0.4.
Technical environment :
RDBMS : Oracle 9.2.0.8, 10.2.0.4, 11.2.0.4, 12.1.0.1
Microsoft SQL Server 2008 R2, 2012
IBM DB2 9.x, 10.5
OS : AIX 5.3, 6.1, 6.2, 7.1
Redhat Entreprise Linux 5.x, 6.x, 7.1, 7.2
Microsoft Windows Server 2003, 2008, 2012
3. FRANCK VICTORIA
Oracle & MS SQL Database Administrator
Data Warehouse Performance Tuning Specialist
EXPERIENCES
Database Administrator
Production et Development
04/2006 to 02/2010
ROLEX SA
Alpha Sarl
Day to day management ( +- 100 Oracle instances ) :
Control of daily batch processes ordered by dollar universe. Control of Oracle instances with Grid Control and Patrol
Tracking of backups (Offline / Online & RMAN for db 10g). Adjusting the storage space for Oracle instances.
Troubleshooting. Processing user requests in terms of creating, modifying and deleting application Oracle accounts.
Installations / Patchs :
Clients Oracle 9i / 10g.
RDBMS 9.2.0.1 + patchs ( 9.2.0.6 / 9.2.0.8 ).
RDBMS 10.2.0.1 + patch 10.2.0.4 + DbConsole.
Migrations :
Oracle 8i et 9i => Oracle 10.2.0.4.
Studies / Projects :
Development of Oracle development standards. Advice in the development of SQL queries and PL/SQL code.
Validation of data models. Managing Oracle schema and object accounts.
Realization of data flows. Creation and deployment of DDL and DML script generation solution ( Schema + PL/SQL ) of a given
Schema.
Change Management / Deliveries :
Implementation of a delivery method on quality and production environments.
Implementing Microsoft Visual Source Safe as a release / Oracle scripts management tool. Validation and delivery of DDL and DML
scripts on quality and production environments.
Performance Reviews / Tuning :
Audit - Monitoring / load testing (AWR Grid control / Dbconsole, Statpack snapshot). Realization and deployment of an emergency
account audit solution (PL/SQL). Creating and deploying a PL/SQL package to reorganize indexes.
Optimization of batch processing, SQL queries and PL/SQL code.
Technical environment :
RDBMS : Oracle 8.1.x, 9.2.0.8, 10.2.0.4
OS : HP UX 11.11, 11.23 - AIX 5.3 - Microsoft Windows Server 2003
Database Administrator ALCAN ALUMINIUM VALAIS SA
02/2005 to 07/2005
Database Administrator WEBTECH SOLUTIONS Sarl
CEO 07/2003 - 06/2004 - Oracle Database Administrator - EUROPCAR
01/2000 to 06/2004 06/2003 - 07/2003 - Oracle Database Administrator - LDCOM
11/2002 - 05/2003 - Oracle Database Administrator - ILIUS ( Meetic.fr )
07/2002 - 08/2002 - Oracle Database Administrator - SNPI ( France Telecom )
01/2002 - 06/2002 - Oracle Database Administrator - EDF
05/2001 - 12/2001 - Oracle Database Administrator - WORLDCOM
01/2000 - 02/2001 - Oracle Database Administrator - CARREFOUR France
Database Administrator WEBTECH Sarl
07/1999 to 12/1999
Database Administrator NEW TECHNOLOGY SOFT
01/1997 to 06/1999
Analyst Programmer OBIMD International
01/1996 to 12/1996
Analyst Programmer CARREFOUR France
09/1990 to 12/1995 Sailing Software
Programmer Others Positions
06/1988 to 07/1990 06/1990 - 07/1990 - 3M France
06/1989 - 05/1990 - Service National - DICAT - Direction du Commissariat de l'Armée de Terre
03/1989 - 05/1989 - Alcatel Radiotéléphone
10/1988 - 12/1988 - Télic Alcatel
06/1988 - 07/1988 - Société Générale - Delta Informatique
4. FRANCK VICTORIA
Oracle & MS SQL Database Administrator
Data Warehouse Performance Tuning Specialist
TECHNICAL SKILLS
Administration RDBMS
Oracle 8i, 9i, 10g, 11g, 12.1.0.2
Microsoft SQL Server 2000, 2003, 2005, 2008, 2012
IBM DB2 9.7, 10.,5
MySQL
High Availability
Oracle Data Guard
Oracle Real Application Cluster
Microsoft Always On
Backup and Recovery
Oracle Recovery Manager ( RMAN )
IBM Tivoli Storage Manager
HP OpenView Data Protector
Architecture, Design et Modeling
Oracle SQL Developer Data Modeler
SAP Power AMC Designer
Development / Workspace
SQL, PL/SQL
Oracle Application Express ( OAE )
Java ( Servlet )
Eclipse Java EE IDE for Web Developers
Perl, Shell
PHP, HTML, CSS
Service Oriented Architecture ( Web Services SOAP and RESTful )
C++ Builder, Delphi, Microsoft Visual Basic
Application Servers
Oracle WebLogic Server
Apache Tomcat
Supervision / Scheduling
Oracle Grid Control
Oracle SQL Developer
CA IT Client Manager ( ITCM )
CA Network and Systems Management ( NSM )
BMC Patrol Central Operator
CA Workload Automation ( AUTOSYS )
Operating Systems
IBM AIX 5.x, 6.x, 7.x
HP UX
SUN Solaris
Redhat Entreprise Linux
Oracle Entreprise Linux
Microsoft Windows Server 2003, 2008, 2012
Virtualization
VMware ESXi 6
VMware vCENTER Server 6
VMware Workstation
Oracle VM VirtualBox
Use
+15 years
+6 years
1 year
1 year
+2 years
-1 year
+3 years
+10 years
+6 years
1 year
+6 years
5 years
+15 years
+6 years
+6 years
+6 years
+6 years
5 years
+6 years
5 years
1 year
+10 years
+10 years
+6 years
+6 years
+6 years
4 years
+6 years
+10 years
5 years
2 years
10 years
4 years
+10 years
1 year
1 year
3 years
2 years
Last Use
2016
2016
2015
2015
2016
2014
2016
2016
2016
2005
2016
2002
2016
2016
2016
2016
2016
2016
2016
2002
2015
2016
2016
2016
2016
2016
2010
2016
2016
2010
2016
2016
2016
2016
2016
2016
2016
2015
Level