The Impact of a Good Data Warehouse Structure on Data Science Projects

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In the current era of constant data exploitation and information retrieval, data science has become an essential pillar for informed and valuable decision-making. However, for data science projects to thrive, the underlying infrastructure, particularly the structure of the data warehouse, plays a crucial role. This article will explore the impact that a robust data warehouse structure can have on the success of data science projects, detailing examples, advantages, and disadvantages, and sharing techniques and tips that I have implemented with favorable results throughout my journey in business intelligence.

But what is a Data Warehouse?

A Data Warehouse is not simply a database; it does not even come close to the concept of a transactional database. Instead, it is a centralized infrastructure that enables the collection, storage, and management of data from various sources. Its main objective is to provide an organized and optimized foundation for data analysis. Thus, a Data Warehouse maintains data governance and helps centralize information, giving it order and structure.

And Regarding Data Science

As a master’s student in data science, I can offer a concept of what I have interpreted so far in these early steps in this fascinating field. Data science involves extracting meaningful knowledge and patterns from large datasets, including processes of data cleaning, transformation, and analysis to obtain valuable information. This, in turn, generates value for the resolution, optimization, transformation, or understanding of any process or problem that may arise in daily life in any field.

A Data Warehouse within Data Science

As mentioned earlier, the definition of data science for any project involves extracting information, cleaning it, and, to obtain valuable information, one needs order and data governance to make the most of it. A significant support for this is to have your data within a good database structure — not just a simple database but rather a robust and scalable Data Warehouse project.

Let’s look at some examples to better understand the incredible relationship between these two concepts.

Sales in E-commerce

Imagine a data science project that aims to analyze purchasing patterns on an e-commerce site. A good Data Warehouse structure would allow the consolidation of transaction data, inventory, and customer profiles, facilitating comprehensive analysis to optimize marketing strategies and inventory management.

Public Health

In the health sector, a well-structured Data Warehouse could consolidate patient data, medical histories, and test results. This would be crucial for data science projects aiming to predict disease outbreaks, improve healthcare efficiency, and personalize treatments.

Advantages of a Good Structure in a Data Warehouse

1. Fast and Efficient Access:

An optimized structure facilitates the quick retrieval of data, allowing data scientists to work more efficiently.

2. Data Integration:

It facilitates the integration of data from various sources, providing a unified and consistent view for analysis.

3. Scalability:

A well-designed Data Warehouse can scale to handle growing data volumes without compromising performance.

Disadvantages and Challenges

1. Initial Costs:

Implementing a Data Warehouse can be expensive in terms of hardware, software, and human resources.

2. Maintenance Complexity:

The ongoing management and update of a Data Warehouse can pose challenges, especially as it grows in complexity. However, proper initial analysis for the design of your data warehouse can help maintain a consistent standard of quality.

Concepts and Tips

Understanding the idea of a Data Warehouse as more than just a differently designed database, I like to think of it as an ideology that helps achieve the right design for data analysis and exploitation.

Here are some concepts and tips that can serve as a guide for developing a good Data Warehouse structure, remembering that it’s more of an ideology, a way of thinking adapted to a database design. Certain rules must be respected for it to fulfill its ultimate goal.

Models

There are few models used for designing the structure of a Data Warehouse, with the most important being, the star model and the snowflake model (the latter behaving more like a transactional database in structure but maintaining the ideology of the Data Warehouse).

Image is taken from this website to observe the difference between the two main models

The only noticeable difference between the two models is an additional hierarchy in the relationships between tables. The star model is simple without complexity, with a main table relating to its dimensions. On the other hand, the snowflake model allows a dimension table to have a relationship with another table, forming a hierarchy.

Personally, I prefer and find the star model more suitable for use. While the hierarchy between tables helps, the basic concept here is the best.

Tip: In the star model, if you need to have a hierarchy between the data, add an extra column to the dimension with the feature of its higher level. For example, if you have data for city and state, even up to the country, instead of having three related tables, add two more columns to the city dimension specifying the state and country.

What Are Fact Tables?

Fact tables are one of the two types of tables involved in a Data Warehouse and are considered the main table around which all facts revolve. This is where everything you want to measure or analyze is stored.

Some characteristics include:

• Only numeric data is involved, although other data types can be considered. There is no technical restriction, but it breaks with the ideology of the Data Warehouse, so respect this point.
• Contains key fields that link to dimension tables.
• Contains metrics or measures, representing everything measurable and analyzable that forms the basis for reports.

What Are Dimension Tables?

Dimension tables store information that gives a characteristic to a fact or observation within a fact table through their attributes. Unlike fact tables, these tables can contain various data types, including numeric, text, or logical.

Some characteristics include:

• Simple denormalized tables.
• Linked to fact tables through a key field.
• Can contain one or more hierarchical relationships (once again, this is under the concept of the snowflake model and can be used as long as the project’s characteristics merit it).
• Usually, they have few records.

Just by understanding these two concepts that make up a Data Warehouse — fact table and dimension table — we could say that we can create a solid database structure. However, practice and constant work in the context of a Data Warehouse are necessary for a full understanding.

Tip:

• Work under the concept of schemas; identify where each table is headed and what information it contains. Create the necessary schemas to relate them to your tables.
• Add a simplified and standardized nomenclature to name your different tables. A good practice is to start the names of dimension tables with DIM_(Name indicating the information the table contains) and fact tables with FACT_(Name indicating the information the table contains).
• Use singular names for your dimension tables (instead of naming a table DIM_countries, use its singular form DIM_country). While it can be understood that this table will store countries, consider it more as an object, and the object being stored is a country.

It’s a simple but nice Data Base structure

Now, a topic of debate is always the date dimension tables. This adds to best practices, having a date dimension table facilitates the way we break down each date record. This table has the ability to contain the date in its original format, along with other columns with the date in different formats or parts. This dimension table should have records of dates related to any transaction in the original table or source, meaning the capture date, registration date, the date the event occurred, etc. — any record containing a date and indicating a moment can be placed in this type of dimension table.

See how a simple date record can be broken down into smaller elements

Let’s Talk about Indexing and Optimization

Applying indexing and optimization techniques to improve query performance is essential. It is crucial to maintain a culture of indexing certain fields in our database, in this case, in our various Data Warehouse tables.

• Ensure indexing in case it is not done automatically by the database manager you are using, or if you are manually creating tables and their fields, make sure every primary key is indexed.
• Ensure that every field in your fact table related to a dimension is indexed.
• Never index all fields in your tables — only those where there may be a relationship and potential data search with these relationships.

Generate Views and Optimize Your Reports and Models

One advantage of working with a database and especially with a Data Warehouse is that you can extract information easily and it is available when you need it. However, there are reasons to want to optimize your queries and have a set of data ready to be used quickly and without repeating code or work, obtaining summarized information and only what you need.

Views are nothing more than queries that generate a set of data and do not require knowledge of the relationships or structure between the involved tables to be used.

• Try to create a nomenclature for naming them.
• Create the views you consider necessary and try to make one view usable for different purposes. While there are reports, models, or graphs that need specific information, do not overuse them.
• Do not make them too complex. A poorly constructed view without due care can make your peace go far away due to inefficiency and poor performance.

General overview of a BI project implementing Data Warehouse

Final Conclusion

In summary, the impact of a good Data Warehouse structure on data science projects is substantial. From agility in accessing data to the ability to scale with growing datasets, a solid infrastructure lays the foundation for success in extracting valuable knowledge. Although there are challenges, the advantages far outweigh the obstacles, making the investment in a well-designed Data Warehouse essential for progress in data science.

Now the handling of so much data and information within a Data Warehouse for the use of data science is something that implies working at certain times with sensitive information, so ethics and neutrality in data management are fundamental pillars, but that is a topic for another time.

Finally

I thank you very much for your time invested in reading this article, I thank every teacher in this first semester of the master's that has expanded my panorama in this world of data.

If you have any questions regarding techniques or suggestions for the creation and maintenance of a Data Warehouse or database in general, let me know, the best thing that humanity can have is to share knowledge.

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