Building a business intelligence data warehouse sounds straightforward on the surface, but in practice, many nuances determine whether it becomes a fast, scalable analytics foundation or a costly bottleneck. Choices around architecture, data integration, modelling, performance, and security all have a direct impact on report speed, reliability, and long-term maintenance. Getting these details right early can save teams months of rework later.
At Vidi Corp, we have seen this first-hand. We are the #1 BI agency according to G2, and we have designed and implemented BI data warehouses for 400+ organisations, including global companies such as Google and Teleperformance. Across these projects, we have worked with a wide range of tools, data sources, and business requirements, which has allowed us to refine a set of practical, proven best practices for data warehousing in BI environments.
In this article, we break down those practices step by step. You will learn what a BI data warehouse is, why companies build them, how to choose the right technology, how to automate data extraction, and how to model, integrate, and secure your data effectively. Whether you are planning your first data warehouse or looking to improve an existing one, this guide will help you build a solution that is fast, scalable, and ready for real-world business intelligence.
A data warehouse is a central database designed to store, organise, and manage data from across an organisation in a way that supports reporting and analysis. A BI data warehouse combines multiple sources of data, such as CRMs, ERPs, finance systems, and marketing platforms, into a single, consistent structure. This creates a reliable “single source of truth” that analysts and decision-makers can trust when answering business questions.
To keep data organised and scalable, data warehouses usually group tables into schemas. A common approach is to place all tables from a single data source into one schema, which makes the data model easier to understand, maintain, and extend over time.
From a technology perspective, data warehouses can be SQL-based (such as SQL Server) or NoSQL-based (such as MongoDB). However, data warehouses used for BI are always SQL-based. This means the data is stored in relational tables made up of rows and columns, which aligns perfectly with BI tools like Power BI, Tableau, or Looker.
The main reason we create data warehouses is to enable automatic data extraction. Manual exports do not scale well and quickly become unreliable as the number of data sources grows. A data warehouse provides a central place where data can be pulled in automatically and refreshed on a schedule, without ongoing manual effort.
A common example is Power BI reporting on QuickBooks Online data. Power BI does not have a native way to analyse data from 20+ QuickBooks accounts in a clean and scalable way. In these cases, we build an automated process that extracts data from all QuickBooks Online accounts into a SQL Server data warehouse, and then connect Power BI to that database. This creates a stable, scalable reporting setup where data refreshes automatically and new accounts can be added with minimal effort.
In business intelligence, there is a well-known principle: transform data as far upstream as possible and as far downstream as necessary. The goal is to minimise resource usage in BI tools, keep costs low, and ensure reports load quickly and reliably.
In practice, this means avoiding heavy data processing inside tools like Power BI. For example, if you extract data directly from the Shopify API, you will find tables with 200+ columns. Loading these wide, raw tables into Power BI slows down refresh times and makes reports less responsive, especially when you start building more complex analysis.
The better approach is to load Shopify data into a data warehouse first. There, you remove unnecessary columns, filter the data to only the rows you need, and calculate derived columns upfront. The data warehouse, which is designed for heavy processing, does the hard work. Power BI then focuses on what it does best: fast, lightweight analysis and visualisation. The result is faster dashboards, lower compute costs, and a much more scalable BI setup.
Another reason to use a data warehouse is the ability to write SQL for more flexible analysis. BI tools like Looker Studio are easy to use, but they come with limitations. Time intelligence is basic, table visuals are restricted to 10 columns, and more advanced calculations are often not possible directly in the reporting layer.
In practice, teams work around these limits by moving logic into SQL. This requires a SQL-based data warehouse such as BigQuery, where you can write SQL statements to calculate additional columns, handle complex date logic, and reshape the data exactly how you need it. By doing this work in the data warehouse, Looker Studio becomes a lightweight visualisation layer rather than a place where complex logic has to be forced in.
We mostly work with Azure SQL Server and Google BigQuery so we will cover those in detail below. There are definitely other options, but since we know those best, we will talk about them.
Azure SQL Server is a strong data warehousing option, particularly when Power BI is used as the BI tool. Since both are Microsoft technologies, the integration is reliable, which makes it a natural choice for organizations that run on Microsoft.
From a cost perspective, Azure SQL is also budget-friendly. Pricing starts at around $15 per month for a small 10 DTU database, which makes it accessible even for small businesses and early-stage BI projects.
Unlike some modern warehouses, Azure SQL does not scale automatically — if you need more compute power, you manually upgrade the database tier. This gives you predictable costs and full control over performance.
From a data extraction perspective, you would typically use tools like Azure Data Factory to extract data automatically. Alternatively, you can write a custom Python code and insert it into an Azure Cloud Function.
Google BigQuery is the most efficient data warehouse choice when working with Looker Studio. Google provides an in-memory analysis service called BI Engine, which accelerates SQL queries coming from Looker Studio reports. This significantly improves report load times and makes BigQuery the natural backend for Looker Studio dashboards.
Big Query automatically scales resources when you need more compute, so you don’t have to manage this process yourself. However, this does result in less predictability in your costs.
Big Query has native connectors to some data sources like Google Ads, GA4, and Firebase, which minimize the amount of code needed for data extraction from those sources.
Where there is no ready-made connector for a data source, we typically write Python code for automatic data extraction and deploy it to a Google Cloud Function.
2 approaches that we regularly use for data integration into a BI data warehouse is ready-made connectors and python script
This option is ideal if you want the benefits of a data warehouse but have limited technical skills or do not want to maintain custom integration code. Instead of building and hosting pipelines yourself, the connectors handle data extraction, loading, and structure for you.
At Vidi Corp, our data warehouse connectors go beyond simple data extraction. They follow established best practices such as incremental data loading, schema standardisation, error handling, and performance optimisation. They also take care of data modelling, so the data arrives in a reporting-ready format with clean tables, consistent naming, and relationships that work well in BI tools.
There are many good practices that need to be followed to build reliable data warehouse integrations, from efficient loading strategies to scalable schema design. We will cover these practices in more detail later in this article. For teams that want fast results without deep engineering effort, managed connectors provide a low-maintenance and scalable path to a production-ready data warehouse.
When there are no ready-made connectors, we automatically extract data into a data warehouse using custom Python integrations. This approach is reliable, flexible, and works with almost any system that exposes an API.
The process is simple. First, we use Python (typically the requests package) to send API requests and retrieve data from the source system. Next, we deploy this code as a cloud function in Azure or Google Cloud. The function is triggered on a schedule, so data refreshes automatically without manual intervention. Finally, once the function runs, it cleans and inserts the data directly into a SQL-based data warehouse.
This approach gives us full control over data extraction and transformation. Because Python has very few limitations, we can handle complex APIs, pagination, authentication, custom logic, and edge cases that off-the-shelf connectors often struggle with. As a result, we can build robust, scalable data pipelines tailored exactly to the client’s data and reporting needs.
Data modelling inside a data warehouse is the process of shaping data so it is fast, reliable, and easy to use for reporting and analysis. Good modelling decisions have a direct impact on dashboard performance, data accuracy, and long-term maintainability. There are several best practices we consistently follow in BI-focused data warehouses.
Efficient data integration is critical for keeping your data warehouse fast, reliable, and cost-effective. A few practical techniques can make a significant difference when dealing with large data volumes and frequent refreshes.
Strong data security ensures sensitive information is protected while still allowing teams to analyse data efficiently. In BI-focused data warehouses, security is usually implemented at multiple levels to control what users can see and access.
A well-designed data warehouse is the foundation of fast, reliable, and scalable business intelligence. When built correctly, it reduces reporting costs, improves performance, and gives teams confidence in their data. When built poorly, it quickly becomes a source of frustration and technical debt.
If you want help designing or improving your BI data warehouse, contact Vidi Corp. Our team has built production-ready data warehouses for hundreds of organisations and can help you create a solution that fits your data, tools, and business goals.
