Data warehouse design is the process of creating a main database that can store data from multiple sources and analyze it effectively. A strong architecture builds reports quickly, handles large datasets, and presents correct results when data flow increases.

Know more about data warehouse design, main concepts, best practices, and the difference between a snowflake and a star schema.

Quick Answer 

Data warehouse design is the process that includes creating data models, pipelines, and architecture that allow a company to store and analyze data from different sources in one system.

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 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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