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Explained: Building a Functional Data Warehouse

Publication date: 24 February 2021

Having a functional data warehouse is vital for organizations that handle copious amounts of data. A data warehouse helps to run logical queries, build accurate forecasting models, and identify impactful trends throughout the organization.

For those who are not familiar with the concept, a data warehouse represents a dumping ground for data from various systems, such as CRM, marketing stack, or sales stack. A data warehouse typically uses online analytic processing (OLAP) to query all the gathered data for better business insights.

There is one downside to data warehousing – such a large amount of data can be quite overwhelming, especially when building a warehouse for the first time. A data warehouse structure varies from company to company and should be based on the organization’s unique needs.

However, there are a few useful architectural principles that can help companies with building their first data warehouse.

Separate Processes By Their Purpose

The biggest mistake many companies make when building a data warehouse is putting all processes together. It is essential to understand the nature of each process and use the right tools for the job.

Data warehousing can be separated into four fundamental concerns that can be considered parts of a data pipeline: collect, store, process/analyze, and consume. Every system that is part of a company’s data pipeline should encapsulate the responsibility of one of these four concerns.

Enable Functional Data Pipelines

For a data warehouse to work well, data coming from pipelines should be easily reproducible. Every time a company wants to re-run a particular process, it should make sure that the results always stay the same.

This principle can be achieved by enforcing the Functional Data Engineering Paradigm. According to Maxime Beauchemin, “a pure task should be deterministic and idempotent, meaning that it will produce the same result every time it runs or re-runs.” Therefore, any process ranging from extraction to loading should be based on the standards of a pure task.

Create an Immutable Staging Area

All data should be stored in an immutable staging area and then transformed and loaded into the data warehouse. It is recommended that all data is stored in its original form or close to its original form, to ensure data reproducibility.

An immutable staging area ensures that the state of the entire data warehouse can be recomputed from scratch.

So, in order to run a functional data warehouse, it is necessary to have software that can carry out all the previously mentioned tasks. First, the data needs to be collected and then stored in its original form. Then, the information is transformed so that it can be stored in the data warehouse. After this, the data can be consumed by users through different BI tools.

The data presented in this article is meant to help companies get started with data warehousing. However, it is important to point out that all the information presented is a simplification of the entire process. For more information, HICO-Group is always there to answer any questions and lead companies every step of the way.

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