Bases de données
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Cours, examens, TD, TP et exercices de bases de données. Thèmes couverts : SQL, MySQL, Oracle, modèle relationnel, SGBD, MERISE.
Ce document est un TD sur l'architecture d'Oracle Database. Les étudiants doivent compléter des phrases en se basant sur leurs connaissances des composants et processus d'un SGBD Oracle. Cela inclut des éléments comme la mémoire SGA, les processus en arrière-plan, et les stratégies de journalisation.
This document outlines the structure of user accounts in a database, focusing on the DBA_USERS table. It explains the process of creating a database user and the associated authentication methods, including database, operating system, and network authentication. Important considerations for user creation, such as naming conventions and default tablespaces, are also discussed.
This document contains a series of exercises on database dependencies and normalization. It includes problems related to functional dependencies, key identification, and normalization forms for various relations. Each exercise presents distinct scenarios and challenges within the field of database management.
Ce document traite des bases de données Oracle et de l'importance du dictionnaire de données pour leur administration. Il vise à familiariser les étudiants avec la structure du dictionnaire et les requêtes SQL pour son interrogation. À travers des activités pratiques, les étudiants apprendront à utiliser les fonctionnalités de consultation disponibles dans Oracle.
This document provides answers to questions related to database administration, transaction management, and SQL view creation, as part of a database examination from the academic year 2014-2015. It includes a range of topics such as transaction control, system views, and data recovery methods.
This document provides a solution and analysis for various PL/SQL exercises related to triggers and queries. It includes explanations of trigger functionality and sample SQL scripts for calculating averages and processing data. Additionally, it addresses updates and inserts in database tables.
This document provides an overview of ADO.Net, a Microsoft library for data access in Windows environments. It covers the functionalities, objects, and operating modes of ADO.Net, including connected and disconnected modes. Additionally, it discusses the necessary steps to establish a connection with databases using ADO.Net, particularly with the OLEDB provider for Access databases.
The document outlines comprehensive curriculums for SQL Server and NoSQL training programs. It covers foundational, intermediate, and advanced topics such as relational algebra, Transact-SQL, database management, and high availability solutions. For SQL Server, it includes specific training on implementation, administration, optimization, high availability, diagnostics, troubleshooting, and .NET integration. The NoSQL section focuses on key differences with relational systems, available solutions, deployment strategies, and future trends.
The document outlines a database schema and queries related to a real-world scenario using Object Database Language (ODL). It describes relationships and attributes of several classes, such as 'Person', 'Employee', 'Drinker', 'Car', 'Apartment', and 'Wine', encapsulating behaviors like drinking or driving. It also details enumerated and multi-valued attributes while providing a set of queries using Object Query Language (OQL) to explore the data. The activity aims to create an effective database schema, run advanced queries, and define views for analytical insights.
This document focuses on utilizing SQL queries to manage and analyze data related to medications, their formats, prices, and associated diseases using Oracle SQL+ environment. The outlined tasks involve creating structured relational database tables with primary and foreign keys, as well as executing complex queries such as ordering medications by treated diseases, identifying the cheapest medication format for various diseases, and adjusting price ranges dynamically. Additionally, it includes elimination of expensive medication formats, identifying medications treating more than two diseas...
This document introduces the key concepts of relational databases (SGBDR), their strengths such as ACID properties and complex querying, and their limitations in handling massive datasets and high-speed operations. It contrasts these with the advantages of NOSQL databases, including scalability, flexibility, and performance suited for Big Data and unstructured data. The different types of NOSQL databases—key-value, document-based, column-oriented, and graph-oriented—are detailed, along with specific examples like Cassandra's architecture and replication strategies. It concludes with a compa...
This document outlines the challenges faced by database administrators, such as resource management, database recovery, and user privileges. It discusses various scenarios, including troubleshooting errors, handling profiles, optimizing tablespaces, and configuring system parameters. It provides structured recommendations to ensure efficient database management while highlighting key functionalities and commands useful in addressing common administrative tasks.
This document explores practical examples of querying MongoDB collections using common NoSQL commands. It provides detailed query examples on filtering data, applying comparison operators, and using projections to refine results. The Cinema collection demonstrates filtering films based on release dates and directors, while the Collaborators collection focuses on querying employee attributes like salary and location. The document emphasizes practical scenarios to understand MongoDB's flexibility and syntax in managing database collections.
This document provides an in-depth overview of MongoDB, focusing on its document-based data storage model and its schema flexibility achieved through BSON types. It elaborates on document structure, unique identifiers, and collections, highlighting differences from relational databases. Key CRUD operations such as creation, querying, updating, deletion, and batch processing are detailed with examples, emphasizing MongoDB's use in distributed environments with ObjectId for ensuring unique identification in scalable systems.
This document provides an overview of MongoDB, detailing its structure, including documents and collections. It explains the data types available in BSON, the creation and management of documents, and the unique identification through ObjectId. Additionally, it covers basic CRUD operations for document handling.
This document outlines the step-by-step process for implementing incremental data extraction and change data capture (CDC) using SQL Server and SSIS. The lab first establishes a setup with predefined tasks, guides the user through configuring SSIS packages for tracking modified data using high water marks, and verifies successful execution. It also introduces CDC in SQL, demonstrating change tracking with timestamp ranges and stored procedures for extracting modifications in datasets efficiently.
This document outlines the tasks required to implement a data warehouse and an ETL solution as part of a supervised assignment. It includes steps for creating a data warehouse schema, a time dimension table, and extracting and profiling data from a source database. The assignment emphasizes the use of SQL Server Management Studio and various SQL scripts.
This document focuses on optimizing data extraction processes by configuring SSIS packages to perform incremental data extraction, thereby reducing network and system resource usage. It explores leveraging datetime columns, Change Data Capture (CDC), and Change Tracking to identify and extract only modified rows. Practical exercises include configuring SSIS packages, utilizing expressions and variables for incremental extraction, CDC setup and usage, stored procedure creation to identify data changes, and integrating these techniques into ETL workflows via SSIS and Visual Studio debugging....
This lab focuses on modifying SSIS packages to address the inefficiencies of extracting unnecessary data in ETL processes. It guides participants through using datetime columns and Change Data Capture to ensure only modified records are extracted. By the end of the lab, participants will have practical experience in implementing incremental data extraction techniques.
This document provides an in-depth look at extracting modified data in an ETL process. It covers various techniques such as utilizing DateTime columns, Change Data Capture, and storing primary keys with checksums. The goal is to identify and efficiently extract only the records that have been modified since the last refresh cycle.
This document provides a detailed overview of extracting modified data through various ETL techniques. It highlights methods such as using a DateTime column, Change Data Capture, and checksums for efficient data management. Furthermore, it outlines the objectives of implementing these techniques effectively in SQL Server.
This document outlines a practical activity focused on implementing a conceptual UML schema for managing room schedules, using the object-oriented features of SQL3. Key tasks include defining classes and associations based on a UML diagram, creating object tables, and ensuring referential integrity with primary key constraints managed through triggers. The activity further involves inserting data instances into classes, performing SQL queries to analyze scheduling and room use, and leveraging advanced SQL features like TREAT(), DEREF(), and IS OF() for hierarchical and polymorphic operation...
The document outlines a programming assignment requiring the use of the Map-Reduce and MongoDB Aggregation frameworks for data processing. It involves importing a dataset, performing counting operations using Map-Reduce, analyzing ordering issues, and generating derived collections based on specific keys. The assignment also requires joining the resulting collections into a final document format. Detailed references are provided to guide the implementation of the tasks.
This document outlines a lab assignment focused on Map-Reduce and aggregation frameworks in MongoDB. Students are required to import JSON data, perform data counting using Map-Reduce, generate new collections based on specified criteria, and execute a join operation. The tasks include analyzing data structure and problem-solving in a practical context.