Normalization vs Denormalization

 

Azure Cloud Data Engineering Training in Hyderabad – Quality Thoughts

Quality Thoughts offers one of the best Azure Cloud Data Engineering courses in Hyderabad, ideal for graduates, postgraduates, working professionals, or career switchers. The course combines hands-on learning with an internship to make you job-ready in a short time.

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Note: Azure Table and Queue Storage support NoSQL and message handling for scalable cloud apps 

Normalization vs Denormalization

Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller related tables and defining relationships between them using keys. Normalization follows normal forms (1NF, 2NF, 3NF, etc.) to ensure minimal duplication and consistent data. Benefits include reduced storage usage, easier maintenance, and prevention of anomalies (insertion, update, deletion). However, it may require more joins in queries, slightly reducing read performance.

Denormalization is the process of combining normalized tables to improve read performance by reducing the number of joins. It introduces controlled redundancy for faster query results, often used in reporting, analytics, or data warehouses. While it speeds up data retrieval, it increases storage needs, potential inconsistency, and requires more effort for updates.

Key Difference:

Normalization: Focus on efficiency in storage and consistency, better for transactional systems (OLTP).

Denormalization: Focus on query speed and simplicity, better for analytical systems (OLAP).

In short, normalization is about “avoiding duplication,” while denormalization is about “speeding up reads” at the cost of some redundancy.

Read More

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Data Warehouse vs Data Lake

Data Modeling & Management

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