Database Management
Database management refers to the process of storing, organizing, and managing data efficiently using a Database Management System (DBMS). It involves tasks like data storage, retrieval, updating, security, backup, and recovery. A DBMS ensures data integrity, consistency, and accessibility while allowing multiple users to interact with the data. Common types of databases include relational (SQL-based), NoSQL, and cloud databases. Examples of DBMS include MySQL, PostgreSQL, MongoDB, and Oracle DB.
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Planning and Design
Requirement analysis: Understand what data needs to be stored, its structure, relationships, and the intended use.
Data modeling: Use tools like Entity-Relationship Diagrams (ERD) to map out tables, fields, and relationships between entities.
Database type selection: Choose between relational (e.g., MySQL, PostgreSQL) or non-relational (e.g., MongoDB, Cassandra) databases based on the project’s requirements
Normalization: Apply normalization rules to eliminate redundancy and ensure data integrity, usually up to the 3rd normal form.
enormalization (if needed): In certain scenarios (e.g., performance optimization), you may denormalize for faster reads.
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Database Setup
Database installation: Install the chosen DBMS (Database Management System) on the server (e.g., MySQL, SQL Server, MongoDB).
Database creation: Create the database schema (structure) based on your data model.
Defining tables: Create tables with appropriate data types (e.g., integer, varchar, date) for each column.
Primary keys: Assign primary keys to uniquely identify each record in a table.
Foreign keys and relationships: Define relationships between tables using foreign keys (for relational databases).
Indexes: Create indexes on frequently queried fields to optimize search performance.
Constraints: Define constraints (e.g., NOT NULL, UNIQUE) to maintain data integrity.
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Data Insertion and Managementt
Insert data: Populate the database with initial data either manually or through scripts.
DCRUD operations: Implement functions to Create, Read, Update, and Delete (CRUD) data.
Transactions: Use transactions for critical operations to ensure data integrity (e.g., in banking systems, if one step fails, the entire transaction is rolled back).
Stored procedures and triggers: Write stored procedures to automate complex operations and triggers to automatically respond to database events.
Batch processing: Handle large-scale data processing using batch jobs, if necessary.
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Security
User access control: Define user roles and permissions, ensuring that users have the appropriate access (e.g., admin, read-only).
Encryption: Encrypt sensitive data, both at rest and in transit, to protect against unauthorized access.
TSQL injection prevention: Use prepared statements or parameterized queries to avoid SQL injection attacks.
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Backup and Recovery
Performance Optimization
Monitoring and Maintenance
Scaling and High Availability
Data Migration
Compliance and Auditing
