Exploring The Key Concepts Behind Data Modeling In The Warehouse

Companies are having a difficult time managing and reporting on all of their data. Even simple queries like "how many clients do we have in various regions" or "which product do our customers between the ages of 20 and 30 buy the most" can be difficult. These problems were meant to be solved by the data engineering solutions.

Since its inception in the mid-1980s, the data warehouse concept has changed tremendously. To meet the expanding challenges and complexities of the corporate world, it evolved into a unique field. This resulted in improved technology and more stringent corporate processes.

Data warehouses were originally designed to allow businesses to keep an analytical data source from which they could answer questions. This is still crucial, but today's businesses want easier access to information on a broader scale by a wider range of end-users.

From specialized developers to just about everyone who can drag and drop in Tableau, the defined user has exploded.

If you want to develop a data warehouse, you need to know who the end users are. It's simple to bring data into Snowflake or Big query without prioritizing the end-user using modern technologies, but the goal should be to establish a core layer of data that anyone can comprehend. At the end of the day, data is a product of the data team and, like any other feature or product, it must be intelligible, reliable, and simple to deal with.

A Product Is Data

Data is a product and a utility in equal measure. The adage that data is the new oil still holds some water, but data is what it is right now. It is simply supposed to function. We are not interested in crude oil. We need gasoline with a high octane rating. We want to be able to just put gasoline in our car and drive away with no issues. This product must be usable because people will be getting much closer to it. This implies that it should be:

• Simple to comprehend
• Simple to work with
• Robust
• Trustworthy
• Timely

The experience of end-users with data engineering services can be substantially improved by standardizing a company's data processes. Following best practices that help transform data from crude oil to high-octane gasoline is a big component of treating data like a product.

Data Modeling Best Practices

It's critical to follow basic best practices when creating your data warehouse. However, it's equally critical not to be too dogmatic. Many data warehouse systems aren't designed to support (or aren't suited for) some of the most used data modelling strategies. This does not, however, imply that you can just dump data into your data warehouse without regard for standards or modelling. You don't have to be dogmatic, but you should maintain consistency. Be incorrect in the same direction if you're going to be wrong. This implies you'll need to establish guidelines to let developers know what they can expect.

Basic best practices, such as using standardized names, can significantly improve an end-data user's experience.

Standardize names - Having consistent naming standards is essential for analysts to readily understand which columns indicate what. Using consistent naming conventions for data types such as "ts," "date," "is_," and so on guarantees that everyone knows what they're looking at without having to consult the data documentation. This is similar to the classic design principle of the signifier, which defines the column's afferences.

Standardize data structures - Generally, avoiding complex data structures such as arrays and dictionaries in the upper core layers is preferable because it reduces analyst confusion.

As far as possible, standardize IDS - IDs allow analysts to combine data from disparate systems. Looking back on my career, I can see how this one best practice has had a significant impact. I couldn't combine data sets when IDs weren't standardized, no matter how imaginative I tried. When I worked with firms that had mechanisms in place to ensure that system IDs could be traced, I was able to seamlessly integrate highly different data sets.

High-Level Concepts in Data Modeling

Our data engineering solutions will need to spend some time learning about how the data is used, what it represents, and how it appears. As a result, you'll be able to design data sets that your cross-functional partners will want to utilize and use effectively. All of them begin with the same data processing stages.

The primary steps in most firms' data modelling patterns are as follows:

Raw - This layer is usually saved in S3 buckets or is a raw table that is utilized as the first layer of data. After that, teams can do fast data checks to confirm that everything is still in order. It can also be reprocessed if any data is accidentally deleted.

Pretreatment - Some form of data preprocessing is almost always necessary. Data teams, on the other hand, rely on staging layers to do a first pass on their data. Duplicate data, extremely nested data, and data that is just titled inconsistently that becomes normalized in the staging layer are all common occurrences. After the data has been processed, there will usually be additional layer of quality assurance before the data is loaded into the core data layer.

Core - The foundation for a company's data can be found in this tier. It's where you can go back and trace every transaction or occurrence in a company to the smallest detail. This layer can be thought of as a repository for all of the various entities and relationships. It's the foundation upon which everything else is built.

Analytics - The analytical layer typically consists of larger tables that have been pre-joined to limit the number of errors and incorrect logic applications that may arise as analysts work on the core data layer.

Aggregates - On top of the analytical layer, there are usually a number of metrics, KPIs, and aggregate data sets to be built. These measures and KPIs are utilized in dashboards that are distributed to directors, the C-suite, and operational managers who must make decisions based on how the KPIs change over time.

What Are the Benefits of Investing in Best Practices?

Building a strong data storage system, whether it's a data warehouse or a data lakehouse, provides a firm basis on which to develop. A well-defined core data layer helps everyone in the firm to produce data products with complete trust in the data's accuracy, whether they're building a data product or doing research.

Furthermore, even steps such as standardizing IDs make it easier to connect data from disparate systems' data sets. Analysts and data scientists, for example, will be able to create analyses based on more divergent data sets as a result of this.

We haven't even considered all of the advantages users gain from column names that follow a predictable naming pattern, such as not having to spend time evaluating columns before utilizing them because they aren't sure what type of data to expect. I know, it's enthralling. While all of these best practices take time, they ensure that a corporation can make decisions based on a high level of data trust in the long run.

Conclusion

Companies invest in data engineering services because they allow simple access to large volumes of data without the need to manually gather data from multiple sources, enter it into excel, and then munge it. This is why data warehouses, data lakes, and data lake homes are so important to businesses. Any type of data storage system necessitates some amount of data modelling and standardization (one way or another).

The best practices outlined in this article can assist your team in taking their data to the next level. It will deliver more usable data to your analysts and data scientists because they will be able to understand how all of the data should be connected or processed without having to continuously contact engineers. Finally, this ensures that everyone has a better experience, from the data engineer to the C-suite executive looking at dashboards.