The Straightforward Guide to Data Warehouse Design

Do you need assistance with your data warehouse design? Zuar provides a few tips to help with this process.

The Straightforward Guide to Data Warehouse Design

Over the past decade, data warehouses have been the pillar of data ecosystems, especially for corporate businesses. You cannot ignore the importance of data warehouses. Today, there are more possibilities than ever for storing, indexing, and analyzing data. Take a look at this straightforward guide to see some great data warehouse design concepts.

Do you need robust data pipeline strategies and staging services for your business? At Zuar, we can help you design an efficient data warehouse for your organization.

What Is a Data Warehouse?

Before you try to design a data warehouse, make sure you understand the basics. A data warehouse is a central repository that includes commutative and past information from single or multiple sources. Companies can collect and import to a data warehouse from numerous data sources. A data warehouse assists with evaluating data and preserving records.

The end users can then use the data to draw insights, forecast, and perform other types of analysis. ETL (extract, transform, and load) or ELT processes are fundamental to a data warehouse.

Best Practices for Data Warehouse Design

If you want to ensure the efficiency of your enterprise data warehouse design, be sure to take advantage of these tips.

Define the Scope of Your Data Warehouse

Before you start designing your data warehouse, consider all the priorities of your end-users. You can outline the project's scope while noting all the limitations and risks of the system. It is vital to set the right expectations for your project by estimating all costs and other expenses. In some cases, you may have to adjust those requirements to ensure the project can meet the completion date. Don't forget to list the deliverables and allocate all duties to the rest of your development team.

Think About Similar Data Warehouse Models

Your data warehouse should have an intangible, theoretical design that can organize your company's data into specific relations, such as attributes and entities. Your data items are known as entities, while the attributes can help you define these entities' exclusivity. Select a model that focuses on your business needs.

The map source data can provide subject-oriented information to the data warehouse schema. You can take those end-user requirements and the source data model to design a data warehouse. Some of those physical implementations of your data model should be based on parameters such as the number of operators, network types, software, disk storage, and computer size.

Identity Your Relevant Data

Based on your business requirements, you should identify crucial data elements. In many situations, make sure to look beyond just end-user requests and needs. Choose those business factors that are crucial to your specific department and relevant to your subject areas. For example, if you want to design a data warehouse for your marketing and sales department, focus on the locations, sales, promotions, clients, and products.

After that, list all the required data fields that should be front and center for the data warehouse operators. For instance, marketing data could yield promotion characteristics, specific regions, and product names. Divide that data into descriptive records (dimensions) and numeric metrics (facts).

What Is a Data Pipeline? Learn | Zuar
Data pipelines enable the smooth flow of information. They automate the process of extracting, validating, and loading the data for analysis. This helps eliminates errors and combats latency. Effective data pipelines are critical for data-driven enterprises.

Always Narrow Down Your Data Sources

Once all the facts and dimensions are listed, take time to identify those data sources that will feed into the repository. These sources can include delimited files, Excel files, databases, API-based connectors, etc. Use any map dimensions to lookup tables in the operating system. With that information, you can map the facts to transaction tables.

Unfortunately, you cannot map some required data. When that happens, it is due to fields in the source that don't have similar parameters as those in the required data group. For example, the telecom industry groups phone calls by area code. If your data warehouse requires data with a postal code, it can be challenging to map these dimensions, primarily because postal codes can contain several area codes. When you need to translate data, like the above example, it can involve more money and time on your end.

Design the Star Schema

Before creating a star schema (the most widely used schema for data warehouses), think about the relationship between dimension tables and facts. You can use keys that include either multiple or single columns to make a row within an exclusive table. Several columns are known as concatenated or composite keys within the primary key.

Use the surrogate keys to line the facts with the dimension. These keys allow the data warehouse manager to control all the keys in the environment, and even change those keys in the operating system. The surrogate keys also will enable a series of integrators to be included in the dimension table. There are more benefits to using these surrogate keys than just using the primary key.

Scaling Considerations

Over time, your data volumes are highly likely to increase. For that reason, you want to think about scalability when designing your data warehouse. It is essential to minimize any restrictions on your software capacity, system bandwidths, and hardware size.

Expert Help

This stuff is complex. But that's why Zuar was founded. We work with organizations of all sizes to help them get set up with data pipelines and warehouses that utilize up-to-date yet proven technologies.

  • Get the most out of your data without hiring an entire team to make it happen. Learn about Zuar's data staging services to build data integrations, pipelines, infrastructure, and models.
  • Pulling data into a single destination and normalizing that data, whether in the cloud or OnPrem, can be difficult for any organization. Zuar's Runner solution provides comprehensive ETL and automated pipeline functionality without the learning curve and cost of many other solutions.  You can learn more here.
Data Warehouse Strategy: What Questions Should You Ask? l Zuar
What questions should you ask before implementing a data warehouse strategy? Read our informative blog and contact Zuar today to learn even more!
Everything You Need To Know About Data Mapping | Zuar
Employ data mapping to combine/migrate all of your desperate data into a single destination. Learn how!
Data Mart vs Data Warehouse vs Data Base vs Data Lake | Zuar
Not sure whether to invest in a data mart, data warehouse, database or data lake? Let’s examine the key differences.