Key Concepts of Database Management Systems to Know for Principles of Data Science

Database Management Systems (DBMS) are essential for organizing and managing data effectively. They ensure data integrity, support complex queries, and provide security. Understanding DBMS is crucial for data science, as it lays the foundation for data analysis and decision-making.

1. Relational Database Management Systems (RDBMS)

   • RDBMS organizes data into tables (relations) that can be linked by common fields.
   • It uses a structured schema to define data types and relationships, ensuring data consistency.
   • Popular RDBMS examples include MySQL, PostgreSQL, and Oracle Database.

2. SQL (Structured Query Language)

   • SQL is the standard language for querying and manipulating data in RDBMS.
   • It includes commands for data retrieval (SELECT), insertion (INSERT), updating (UPDATE), and deletion (DELETE).
   • SQL supports complex queries through joins, subqueries, and aggregate functions.

3. Database design and normalization

   • Database design involves structuring data to minimize redundancy and improve data integrity.
   • Normalization is the process of organizing data into tables to reduce duplication and dependency.
   • Common normalization forms include 1NF, 2NF, and 3NF, each addressing specific types of anomalies.

4. ACID properties

   • ACID stands for Atomicity, Consistency, Isolation, and Durability, ensuring reliable transactions.
   • Atomicity guarantees that all parts of a transaction are completed or none at all.
   • Consistency ensures that a transaction brings the database from one valid state to another.

5. Indexing and query optimization

   • Indexing improves the speed of data retrieval operations on a database table.
   • It creates a data structure that allows for faster searches, reducing the time complexity of queries.
   • Query optimization involves analyzing and rewriting queries to enhance performance and resource usage.

6. Transactions and concurrency control

   • Transactions are sequences of operations performed as a single logical unit of work.
   • Concurrency control manages simultaneous operations without conflicting, ensuring data integrity.
   • Techniques include locking, timestamp ordering, and optimistic concurrency control.

7. Data integrity and constraints

   • Data integrity ensures accuracy and consistency of data within the database.
   • Constraints like primary keys, foreign keys, and unique constraints enforce rules on data entry.
   • Referential integrity maintains valid relationships between tables, preventing orphaned records.

8. NoSQL databases

   • NoSQL databases are designed for unstructured or semi-structured data, offering flexibility in data models.
   • They include various types such as document stores, key-value stores, column-family stores, and graph databases.
   • NoSQL is often used for big data applications and real-time web applications due to its scalability.

9. Data warehousing

   • Data warehousing involves collecting and managing large volumes of data from different sources for analysis.
   • It supports business intelligence activities, enabling complex queries and reporting.
   • Data is often organized in a star or snowflake schema to facilitate efficient querying.

10. Database security and access control

    • Database security protects data from unauthorized access and breaches.
    • Access control mechanisms determine who can view or manipulate data, using roles and permissions.
    • Encryption and auditing are essential practices to safeguard sensitive information and track access.