Data Warehouse Design: Process, Architecture & Best Practices

Data warehouse design is the process of designing the model of how data from various sources can be gathered, structured, modeled, stored and then used for analytics and/or reporting. By facilitating consistent data access, efficient analytical queries, and scalable growth with increasing data volume and reporting needs, a well-designed warehouse supports team collaboration and decision-making.

Effective design is more than just picking a database. It calls for business requirements, the appropriate architecture, data model, reliable ETL or ELT pipelines, data governance, and continuous performance optimization.

In this guide, you’ll learn the complete data warehouse design process, understand the core architecture and components, compare star and snowflake schemas, and explore practical design principles and best practices.

Quick Answer 

Data warehouse design involves devising the strategy for gathering and combining data from various sources, structuring the data, storing it in the warehouse, cleaning it for analysis. Business needs are to be defined, the proper architecture to be chosen, the data-model will be developed, the ETL/ELT-pipelines will be built, governance and security controls will be applied and finally performance and scalability testing of the warehouse will take place.

What Is Data Warehouse Design?

A data warehouse is a main repository where data from different sources is consolidated, which allows detailed analysis and reliable reporting. This is a specific architecture built for data storage optimization, creating better reports, and supporting analytical data loads for scalability.

Here are four different goals that a well-structured data warehouse revolves around:

1. Consolidation: Collecting data from ERPs, CRMs, and transactional sources into one place. 
2. Consistency: Includes unique names, different formats, and some business rules in all the sources.
3. Performance: Create tables for writing specific queries that help analyze data efficiently.
4. Governance: It can validate and control data that allows users to get access while staying compliant with the terms and conditions of a company.

In short, data warehouse design refers to the architecture that includes data modeling and processing for building a system that consolidates data from different sources for quick analysis and effective reporting.

Data Warehouse Design Process: 7 Practical Steps

What is the data warehouse design process?

The data warehouse design process is an organized approach to mapping perceived business needs to known and unknown raw data sources and into a robust data warehouse for analytics. Generally includes planning what to report, determining source systems, selecting an architecture, creating a data model, creating data pipelines, setting up governance and data testing, and ongoing optimizations of the data warehouse.

In reality, these decisions do “go hand in hand.” An inadequate schema to match business needs can be created, and even with a good schema, inadequate resolution of the issues encountered with the source data can throw off the desired architecture.

The following is a seven-step process for creating your data warehouse design.

1. First, identify the Business Questions that the Warehouse Needs to answer.

Begin by considering the decisions the warehouse will have to make, not the technology.

Communicate with analysts, business users, reporting teams and anyone who wants to use the data and find out their requirements. Examples of this type of information can be executive reporting, operational reporting, customer reporting, forecasting, financial reporting, or cross-departmental reporting.

Document requirements such as:

• Business KPIs and reporting needs
• Data refresh frequency
• Expected number of users
• Historical data requirements
• Query and dashboard workloads
• Security and access requirements
• Expected growth in data volume

For example, if a particular warehouse is only accessed during management reporting, once a month, it can allow for many more different refresh and performance requirements than a warehouse that is also accessed by other dashboards throughout the day.

If this step is correct, it will provide a purpose to the rest of the design.

2. Identify and Assess the Data Sources

After determining the measurements required in the business, locate where these data now exist.

Common sources can range from CRM/ERP systems, transactional databases, marketing systems, APIs, spreadsheets, SaaS applications, cloud systems, and third-party data.

When creating a list of sources, don’t only do that. Determine the usefulness of their data.

Some problems to watch for include disassociated customer IDs, duplicate records, fields with no data for any particular customer record, varying definitions of the same field, varying dates formats or the systems updating at different times.

With an early assessment we can answer an important design question:

Is the data reliable to be combined as is or does it need extensive cleaning and transformation?

That decision will literally impact how complex your pipelines and data models are later.

3. Select Architecture that is appropriate for the Workload

With the requirements and source systems understood, you can choose an appropriate data warehouse architecture.

It’s essential to design the architecture based on data volume, complexity of queries, latency requirements, scalability, security, cloud resources and budget.

The traditional warehouses frequently have distinct storage, processing, and presentation layers. For workloads on modern platforms, the additional ingestion, transformation, governance or serving layers may be added or removed based on the workload.

In some cases, it is possible to further differentiate raw, refined and analytics-ready data by using concepts like medallion architecture.

Building the most sophisticated architecture is not an objective. It is to design one that can handle today’s workloads and to be able to enhance it on the fly to add new sources of data, users and analytical needs.

The article’s Data Warehouse Architecture: The Three Tiers section below explains the core architectural layers in more detail.

4. Structure the Data Model to match the way people will search the data model

After defining the architecture, make a decision on the organisation of information for analysis.

A helpful way to get started is to list the business events that you would like to measure in fact tables and the descriptive data (e.g., customer, product, location or date) that would belong in dimension tables.

The model should consolidate what is desired in the analytical tools and by the users.

A star schema structure has the following simple structure in many analytical workloads, consisting of dimension tables that are directly related to a central fact table. Different strategies that are more normalised should be considered when there are more complex types of dimensions and/or common dimensions are present for multiple subject areas.

Rather than choosing a schema because it is considered a standard, evaluate it against:

• Common query patterns
• Reporting complexity
• Performance requirements
• Data maintainability
• Governance requirements

That section on Star Schema vs. Snowflake Schema, which will be discussed later in this guide, provides more detail about those models.

5. Build Reliable ETL or ELT Pipelines

The next step is to establish a reliable, trustworthy flow from source systems to the warehouse.

Data could follow an ETL data pipeline (extract, transform, load) or it could follow an ELT (extract, load, transform) data pipeline.

A production-ready pipeline should better make than merely move data from one system to another. Should be a consideration of:

• Validation
• Data standardization
• Deduplication
• Transformation rules
• Error handling
• Monitoring
• Refresh schedules
• Failed-job recovery

For example, if two systems are used for a given customer, and they are defined differently, that should be done before it impacts their dashboards and analytical models.

Dedicated data engineering services can be used to handle this layer for organizations with complex transformations, with multiple systems or automated pipelines.

The objective is simple – analysts need to be able to trust the data that gets to the warehouse.

6. Embed Governance, Security and Data Quality in the Design

Governance should not start when the warehouse is live!

Specify the rules of ownership, access, security and quality during the design of the system.

This involves determining who has access to the sensitive information, who are the respective owners of different data sets, how key metrics are to be defined, and how changes are to be recorded.

Controls vary based on the environment, and can include things like role based access, data classification, lineage, validation rules, audit logging, naming standards and quality monitoring.

This is particularly significant if there are multiple departments working within the same warehouse.

For example, marketing and finance may have different definitions of “customer revenue” and loading both of these data sets into a warehouse will not guarantee there is no issue. There needs to be a common definition and ownership.

With good governance, the warehouse lives up to be more trustworthy, easier to keep up and exceed in size.

7. Do Tests on Warehouse With Realistic Workloads Before Scaling

Check a warehouse with the realistic volume of data, queries, transformations and reporting workloads before deployment.

Validate whether:

• Source records reconcile correctly
• Transformation logic produces expected results
• Queries perform acceptably
• Scheduled refreshes complete reliably
• User permissions work as intended
• Dashboards return consistent numbers
• Failed pipelines can recover safely

The test shouldn’t stop after launch.

Keep an eye on query performance, pipeline failures, growth of storage, compute usage, data quality and evolving business needs. As volumes increase, tuning and/or redesign of some areas of the warehouse may be necessary.

A warehouse that was designed optimally for the ten dashboards and few analysts in your business could require a different strategy when hundreds of users and other data sources are now relying on it.

Thus, data warehouse design should be viewed as an engineering discipline that must be performed on an on-going basis.

Data Warehouse Design Process at a Glance

As a quick reference the seven stages are:

Business requirements → Data-source assessment → Architecture → Data modeling → ETL/ELT pipelines → Governance and security → Testing and optimization

Each of the stages will impact the other. Effective warehouse design is predicated on ensuring these are all considered as one cohesive initiative as opposed to architecture, modelling, pipelines and governance as separate initiatives.

What Should You Decide Before Designing a Data Warehouse?

Before implementation starts, an organization should be able to answer four basic questions:

What types of questions does the warehouse have to answer?

These include the data, dimensions, metrics, and history that must be accessible.

Where will the required data come from?

Source systems define integration complexity, needs for transformation and refresh frequencies.

How quickly does the data need to become available?

Different pipeline/ infrastructural decisions are needed for daily reporting and for near real time analytics.

Who will use and govern the data?

Roles, security, who has ownership, and common metric definitions should be determined in the beginning stages of the project.

Without these answers, selecting a database platform and schema early can lead to costly rework later.

If you’re considering adopting an architecture and are not sure if a warehouse is the right choice, here are the key points you won’t want to miss in our data lakes vs data warehouses guide.

Data warehouse design process at a glance: Define requirements → assess data sources → select the architecture → design the schema → build ETL/ELT pipelines → add governance and security → test and optimize.

Data Warehouse Architecture: The Three Tiers

Most of the modern data warehouses at present use a three-tier architecture approach. Because it can easily maintain performance, quality, and scalability.

Tier	Function	Example Components
Bottom tier	A database server that mainly stores raw and integrated data	Cloud storage and relational database
Middle tier	An OLAP server that is perfect for complex analysis.	BigQuery, Redshift, and Snowflake
Top tier	The front-end layer is used for reporting and creating dashboards.	Dashboards, BI tools, and SQL clients.

Here are two other types of designs for data warehouses, but these are no longer useful nowadays.

• Single-tier architecture removes duplicates and controls redundancy by managing everything within a single design. Though it is quite easy and simple, it becomes difficult to handle a large amount of data.
• Two-tier architecture separates different data sources from analytical tools. However, a direct connection with them can still cap the performance.

According to AWS, data in modern analytics comes into a warehouse from multiple sources and many other databases. The data are sometimes structured, unstructured, or semi-structured, which undergo the process of cleaning, analyzing, and finally reporting in regular modes.

Key Components of a Data Warehouse

Four main pillars handle data warehouses for working together. These are databases, ETL tools, metadata, and access tools.

1. Data warehouse database: It is the central place where integrated data is stored and used to run complex queries.
2. ETL (extract, transform, load) tools: ETL tools access data from different sources, clean the data, process it for creating reports, and load it into the warehouse.
3. Metadata management: It helps with descriptive information about the data that enhances its governance and usefulness.
4. Access tools: People take the help of access tools to query, create, report, and analyze data using Power BI dashboards.

At present, some modern platforms have updated the traditional approach with real-time analytics, high concurrency, scalability, and data sharing limits for warehouse architecture.

Data Warehouse Design Principles

The data warehouse design depends on four key principles. These are query performance, ETL optimization, data modeling, and governance. If you ignore any of these, the results are going to be problematic.

1. Data Modeling

• Store data in fact and dimension tables with either a star or snowflake schema for better query optimization.
• You can choose either denormalization or balanced normalization depending on your analytical requirements. Keep in mind that denormalized approaches are usually quicker to query data but more complex to maintain.

2. Optimising Through The ETL Process 

• Create such pipelines that can easily process large data sets so that the information always remains up to date.
• Make sure to create checks for data quality, like data cleaning and validation, in order to avoid duplicates so that only actual data is used for creating reports.

3. Optimisation and Query Performance

• Use indexing to retrieve data faster from filtered columns occasionally.
• Take the help of partitions to split large tables into smaller ones, which become easier to manage.
• Create views for storing already written complex queries that are time-consuming and need to run multiple times.

4. Security and Data Governance

• Use access controls for higher designations so that only they can access confidential data.
• Build separate compliance categories according to the company’s rules and policies.

Star Schema vs. Snowflake Schema

A star schema allows all types of data and stores it separately in different tables, which have a connection to the main fact table. You can run queries faster and easier with this schema.

A snowflake schema acts out tasks using the main fact table. It converts the dimension table into small and related tables for easy access. Although it helps detect duplicate entries, it leads to more complex structures in a database.

Schema Type	Structure	Best For
Star schema	Denormalised dimensions around one fact table	Faster queries, simpler joins
Snowflake schema	Normalised dimensions across multiple related tables	Reduced redundancy, easier maintenance

Note: It is better to use a star schema if you want to run queries faster and save more time. Use a snowflake schema when you consider data integrity and storage efficiency more important.

Data Warehouse vs. Operational Data Store

An operational data store (ODS) and a data warehouse work for separate business reasons, although these are part of a company’s data strategy. In comparison to Snowflake, a data warehouse mainly pays attention to old and historically stored data to track long-term achievements and growth rates.

Analysts use operational data stores to create everyday reports, take immediate actions for problems before a major loss, and perform real-time analysis to get the latest updates.

• Data type: An operational data store has the most recent transactional data, while a data warehouse stores old and integrated data.
• Data volatility: An operational store’s data is updated frequently, while a warehouse’s data is stable and updates periodically.
• Use case: Experts use the data of a warehouse for monthly, quarterly, and annual comparisons, whereas analysts use the data of ODS for the most recent updates, like 30-minute or hourly reports.

You had better understand the major differences between an operational data store and a data warehouse so that you do not use one system to do both tasks.

Enterprise Data Warehouse Best Practices

The Enterprise Data Warehouse (EDW) wants you to practice as much as possible for managing access, checking scalability, and limiting costs. These are very helpful for using Redshift and BigQuery, as well as Snowflake.

• Build a separate design for MPP wherever you can:  Take the help of AWS notes to create Massively Parallel Processing (MPP) architectures. These let you enhance quality and performance simply by increasing the number of queries.
• Use consumption, staging, and warehousing layers separately: This is a very useful step that separates the raw data from the cleaned and query-run data.
• Use column-type storage for quick analysis: Try using column-type storage as the system returns the result for a specific query that shows results for a column only. It shows the results quicker than the row type method, which saves time.
• Automate schema change management: Always use the latest schema and synchronize it so that you get updates for a new version. However, you must check it in parallel before making changes to the production data.
• Governance cost with performance: Cloud-based warehouses have costs for storage as well as computation separately; therefore, running inappropriate queries generally increases the costs.
• Select Slowly changing dimensions (SCDs): When you update anything in the dimension table, the updates are not reflected in the fact tables.

Do’s and Don’ts of Data Warehouse Design

Dos

• Always make notes of your data so that future analysts can better understand table relationships.
• Check your schema to perform actual query patterns before creating tables.
• Use indexing and partitioning while running queries, not after receiving an error.
• Always work on analytical OLAP and transactional OLTP separately.

Don’ts

• Never ignore the quality of data while using an ETL pipeline to save time.
• Don’t additionally normalize a warehouse, meaning for quick reporting, as it uses irrelevant joins.
• Don’t consider a data warehouse and an operational data store as interchangeable aspects.
• Never take access control lightly because sensitive data needs permission only for specific roles.

Conclusion

Data warehouse design means creating a place where data is stored from multiple sources. This design includes architecture, data modeling, and governance for reliable reporting. No matter if you work on a three-tier architecture, a snowflake schema, or a star schema, it does not change the main target accuracy.

Use tips from AWS, Google Cloud, and Snowflake for data protection when the size of data sets increases.

Next step: Note down your simple queries, and then select a useful architecture tier and schema to get the best results.

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