Overview

Effective data management turns raw facts into strategic insight. This topic covers core database principles—data types, file organization, relational models—and modern practices such as data warehousing, big‑data analytics, and cloud storage lifecycle policies.

Key Concepts and Structures

• Data Hierarchy: Bit → Byte → Field → Record → File → Database → Data Warehouse.
• File Organization: Sequential, indexed‑sequential, direct (hash), and the role of memory management.
• DBMS Models: Relational (SQL), hierarchical, network, NoSQL (document, key‑value, wide‑column), and object‑oriented.
• Relational Concepts: Tables, primary keys, foreign keys, normalization (1NF–3NF) to reduce redundancy.
• Data Warehousing & BI: ETL processes, star/snowflake schemas, OLAP cubes, and dashboards.
• Big Data & Storage: Hadoop, distributed file systems, cloud object storage (S3), and lifecycle rules for cost optimization.
• Data Governance: Validation, migration, privacy regulations, and retention/obsolescence policies.

Step-by-Step Example

Problem: A retail chain stores sales data in multiple spreadsheets. They need consolidated reporting and faster queries for regional managers. Outline a database solution.

Step 1: Import spreadsheets into a relational DBMS (e.g., MySQL) with separate tables for Stores, Products, and Sales.

Step 2: Define store_id and product_id as primary keys; reference them as foreign keys in Sales for referential integrity.

Step 3: Normalize to 3NF to eliminate duplicate store or product details.

Step 4: Build a nightly ETL job that loads aggregated data into a star‑schema data mart for quick OLAP queries.

Final Answer: Migrating to a normalized relational database with a downstream star‑schema warehouse provides single‑source accuracy and sub‑second regional sales queries.

Quick Tip