0.1 Quizz 3 0.2 Quizz 4

[SCIA] EPITA 2009
Data Warehouse
0.1
Quizz 3
– Performs Data loading into the databases where some transformations occur
using sql and the database itself : ELT
– Contains detailled and historical data. its model must be independant from
the application (neutral model). It allows multi domain requests and bring
the relevance to a customer relationship : Datawarehouse
– Based on a data referential. allows bulk data load. all transformation occur
prior to being loaded to database : ETL
– Interfaces strongly coupled with the applications and the data warehouse. it
must be able to manage almost real time events : ODS
– Refers to data streams between one place to another place (file typem, server, location) and/or from one system to another : EAI
0.2
Quizz 4
– Model the Business implies a normalized data model -> Aujourd’hui on a
besoin d’un schéma qui s’adapte aux évolutions des métiers et des entreprises -> 3 ème forme normale -> VRAI
– Denormalized data model minimize the number of joins between tables and
may increase query performances -> VRAI
– In a Data Mart environment we use very often denormalized data model
because we are querying very often on small or medium data tables ->
FAUX (Oui on utilise des modèles dénormalisés, mais uniquement parcequ’on connait les requettes qui vont être faites)
– Data Warehousing is a Coninually Evolving Process consisting in four main
phases -> FAUX (Evolving process -> vrai, mais juste 3 etapes)
– OLTP and Data Warehouse Workloads are very close ; consequently the data
warehouse DBMS choice can rely on the OLTP DBMS choice -> FAUX
(Module 3 pages 33/34), la vie entre la prod et le décisionnel est très différente.
– In a Data Warehouse environment we may have a mixt of simple or complex pre-defined queries and simple or complex ad-hoc queries -> VRAI
(Datawarehouse -> on répond a toutes les demandes de l’entreprise !)
– The amount of detailed data volume as well as the number of concurrent
users are the only parameters for choosing a Data Warehouse solution ->
FAUX (Il faut aussi prendre en compte la complexité des requettes, ainsi
que la complexité des modèles)
– Scalability of the DW solution (Hardware and Software) is one of the central
issues for DW project success -> VRAI -> 3 types d’architecture :
1
[SCIA] EPITA 2009
Data Warehouse
– PC domestiques
– Clusters (pour backups, questions de sécurité)
– Archi massivement parallèle
– Data Volume size, Data Model complexity are the two elements to consider
when selecting a Data Warehouse Solution -> FAUX (“the” est de trop)
0.3
Quizz 6
– Performs data loading into the databases where some transformations occur
using sql and the database itself : ELT
– Oltp process with multidimentional + relational databases, good solution
for high scalability : HOLAP
– Interface strongly coupled with the applications and the DataWarehouse.
must be able to manage almost real time events : ODS
– Dynamic analysis of multidimensional data. use a multidimensional denormalized data model determined by logical requirements : OLAP
– Based on a data referential. allows a bulk data load. all transformation occur
prior to being loaded to database : ETL
– OLAP system based on relational tables : ROLAP
– Traditional DataWarehouse plus ... very current detailed data integrated with
historical data for strategic tactital and event driven business decision making. Allows timely updates - close to real time - as well as short, tactical
queries that return in seconds : Activte DataWarehouse
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