What role does normalization play in data warehousing?

I have seen that this is a key basic that is often missed, people not familiar with the concept tend to mix up data warehouses and data marts, so they speak about the level of denormalization they applied to their data warehouse. The data warehouse layer is most of the time normalized (I used to work in 3rd Normal Form) to ensure consistency, ease/speed of insert/updates, and the Data Marts are denormalized to ease speed of querying.

There are different levels of normalization (first, second, third normal form etc.) that offer increasing degrees of data organization. However, it's important to find a balance. While high normalization minimizes redundancy, it can also lead to complex joins and slower query performance in data warehouses designed primarily for analysis. Consider the specific needs of your data warehouse and prioritize data integrity while optimizing for efficient querying and retrieval of insights.

Normalization involves organizing data to minimize redundancy and dependency by dividing larger tables into smaller, related tables. This process is guided by a series of rules called normal forms, which include: First Normal Form (1NF): Ensures each column contains atomic (indivisible) values and each record is unique. Second Normal Form (2NF): Builds on 1NF, ensuring all non-key attributes are fully functional dependent on the primary key. Third Normal Form (3NF): Ensures that all non-key attributes are not only dependent on the primary key but also independent of each other.

Normalization also links tables using keys, preventing inconsistencies. For example, an address table linked to a customer table by ID ensures a customer has one correct address. Updates are also simplified: update the address once in the address table, not multiple times across orders.

Normalization promotes data integrity by: Eliminating Redundancies: Reducing data duplication, which decreases the risk of inconsistencies. Enforcing Constraints: Primary keys, foreign keys, and unique constraints ensure data accuracy and consistency across related tables.

From what I have seen, one of the main difference between a data warehouse and a lake house resides in this area. While in a database you have some of the consistencies/integrity checks that are ensured by the very fact that you are in a database (primary keys, field format, etc), in a Lake House you would want to have some of these included in your pipeline, as as you are working with files, keys are just text. You can do it, just do not forget it, I know, I forgot, I suffered.

Data warehouses prioritize query speed by strategically denormalizing data in dimensional modeling. This means some redundancy, like copying descriptive attributes, to avoid complex joins during analysis. This balances fast querying of large datasets with data consistency, but requires careful management to avoid bloat.

Normalized databases can influence query performance in the following ways: Pros: Efficient Updates: Smaller, normalized tables allow quicker updates and inserts. Indexing: Easier to create and manage indexes on smaller tables, improving read performance. Cons: Complex Joins: Queries often require multiple joins across tables, which can slow down read operations if not optimized properly.

Normalization prioritizes storage efficiency, but complex joins can slow queries. Denormalization strategically adds redundancy to speed up specific analyses. Find the balance between efficient storage and fast retrieval for your data warehouse needs.

Normalization enhances storage efficiency by: Reducing Redundancy: By eliminating duplicate data, normalized databases require less storage space. Efficient Schema: Structuring data into related tables avoids unnecessary storage of repeated data values.

Data warehouses benefit from both normalization (reduced redundancy) and denormalization (faster queries). Dimensional modeling techniques prioritize storing related data together to optimize query speed, but this can introduce redundancy. Carefully balance these factors for each dimension table.

In data warehousing, normalization is applied differently compared to OLTP (Online Transaction Processing) systems: Staging Area: Data is typically normalized in the staging area to cleanse and prepare it. Dimensional Models: Data warehouses often use denormalized structures like star schemas or snowflake schemas for better query performance and easier reporting. Hybrid Approach: Some data warehouse designs use a mix of normalized and denormalized structures to balance storage efficiency and query performance.

While denormalization can improve query performance in data warehouses, it comes with trade-offs: Pros: Simplified Queries: Fewer joins needed, speeding up read operations. Faster Reporting: Aggregated data ready for reporting and analysis. Cons: Increased Redundancy: More storage space required due to duplicate data. Complexity in Updates: Updates become more complex and time-consuming as data redundancy increases.

The normalization techniques help understand better a soluction to the dependency problems associated with attributes in records when your write data to reflect the reality and modify without problems in the integrity, performance and redundancy aspects of the data.