If you want to know how to build a data warehouse, you’ve come to the right place. In this blog post, we’ll give you a step-by-step guide on how to get started.
Data warehouses are an essential part of any business that wants to make data-driven decisions. A warehouse provides a central repository of your organization’s data for analysis.
How to build a data warehouse? Building a warehouse can sound like a difficult task, but with our assistance, you’ll be up and running in no time at all!
What’s A Data Warehouse?
A data warehouse is a central location where data is aggregated and where analysis can be performed. This data can be sourced from several sources, including transaction systems. This data is constantly updated so that decisions can be made based on the latest information.
The data sources are the first part of the data flow. These databases can be from SQL servers or other relational ones. They can also come from other sources such as CSV files and XML files.
The staging database is where you combine the data from different sources. You can implement this with an SQL server but you can also use Excel.
The warehouse is then created using a relational database that has a multidimensional structure. Later on, we will demonstrate how to create one.
Dimensional Model
A warehouse is different from an operational database, which stores data to be retrieved for analytics and business intelligence. Before we dive into the specifics of the schema and structure of a warehouse, let’s review some terminology.
Fact Table
A fact table is a central location for all data about a business entity. It is surrounded by a dimension and a measure. The columns contain IDs for each of these elements.
The details of the fact tables are dependent on the records that populate them. Different facts can be sourced from multiple databases that share common data.
Dimensions
Fact table dimensions are used to describe the categories and attributes of facts. Examples of common dimensions are customer, product, or location. These can be used to answer specific questions, such as “How much money did we make from selling Products A, B, and C?”
A dimension is a separate entity that is referenced from a fact table. The dimensions include a unique identifier, such as the product name or ID number, and additional information to further describe them. Product categories and subcategories can be used to describe products.
Dimensions can have different keys than the tables that contain them. This can happen when combining data from multiple databases.
Measures
The measure is a property of the fact table that allows the user to calculate a numeric value. This can be the sum of all values, the average of all values, the count of all values in the measure, the minimum of all values, and the maximum of all values of the measure.
Additive measures are quantities that can be summed. The sum of a unit price and quantity is an additive measure of the total value.
A summarization may not make complete sense. A calculation or computation is very similar to it. For instance, total sales are calculated by the product’s per-unit price and taxes.
Star Schema
A star schema is one of the simplest and most common ways of structuring data. It places the dimension tables around a central fact table and is sometimes referred to as a parent and child relationship. There are no grandchildren.
Characteristics of star schema model:
- The center contains the fact table, which comprises dimension keys (foreign keys) and measures.
- The foreign keys in the fact table are the primary keys to the corresponding dimension tables.
- A dimension table does not refer to another dimension table. They are denormalized.
Its advantages:
- The simple design allows for simpler queries.
- Easy to maintain
- The denormalized dimension table design allows for faster access to records.
Snowflake Schema
In a snowflake schema, the dimension tables have physical structures which resemble snowflakes.
Unlike the parent-child model, snowflake schemas can have grandchildren.
Characteristics of the snowflake schema:
- The center is also where you will see the fact table, similar to the star schema.
- The fact table references first-level dimension tables.
- A dimension table can be used to refer to another dimension table. This design is called normalization.
Its advantages:
- Structure changes are easier to make.
- Normalized dimension tables take up less disk space.
How to Build A Data Warehouse
Let’s look at these concepts in action. We will look at a fictitious company, XYZ Insurance, that sells fire insurance for residential buildings, apartment buildings, and commercial buildings.
These are the basic characteristics of a data warehouse example:
- One (1) transactional database
- The staging environment will have a copy of our transaction data, with the tables and columns that are to be updated.
- The insurance company will use the star schema to focus on the sale of its insurance services.
Step 1: Get Business Requirements
Receive Business Questions
- Questions and objectives for the business.
- Reports and their formats give answers to business questions.
Your job is to answer questions for your clients and to help them make informed choices.
In our example above, we only answered the question of how many sales were made in a period. There are many others, but we’ll only show you the ones we’ve covered. The rest is up to you.
For the best results, make sure to pay attention to both the current state of the system as well as the desired end result. Also, be sure to ask which report format they prefer.
Inspect The Source Transactional Database
- Database of staging areas.
- Make sure to extract data from your data source to the staging area.
The transactional database includes all the information currently available. In this example, we assume all the information we require can be found in our source database.
If you find missing information, you should contact your stakeholders and resolve the issue separately. Then return to the previous step.
Once you have reviewed your source, determine what tables, fields, and columns are needed. You don’t have to have them all. If you must clean the data, you should clarify which steps need to be taken.
Let’s suppose that you already have all the information you need.
So, now you need to decide where your data will be staged. Before you do this, though, I have a question for you. Why would you want to manage a completely separate copy of the data?
In this example, we only use a single data source. However, you don’t have to limit yourself to only using a single source. You can pull data from multiple databases.
Other software can be used to purchase items, manage a petty cash fund, pay employees, and for other tasks. If you have multiple databases that contain information, you can link them all here and analyze them all at once.
How do you find this information? If they can share information, consolidate it into one area. Employee lists, for example, are something you can share.
Cleaning up data in the staging zone is another crucial step. Also, pre-calculating aggregate values in the staging area is also important.
Step 2: Create Your SQL Data Warehouse
We have finally reached the core of this article. It’s time to build a new database for the data warehouse.
- SQL Server database for the data warehouse.
- Move the data from the staging area to the data warehouse.
Launch SQL Server Management Studio to create a new database for the data warehouse. Next, open the Object Explorer and right-click on the Databases folder. Select New Database. Give your database a name and select the database options.
Create a Fact Table
The empty database now needs new tables. The fact table is the first table that you create.
Pay close attention to the foreign keys client_id, building_city_id, product_id, and statement_date.
Make The Dimensions
Now it’s time to create the table that will store the data. Each column in the table represents a different dimension of our data.
Once you have created a data repository, what do you do next?
Step 3: Transfer Data from the Transactional Database to the Data Warehouse
Extracting data from one system to another involves creating mapping fields between the systems. Before beginning this process, make sure you create these mappings.
A query is required to load data into the date column.
You can change the date range for this script.
An Extract, Transform, and Load (ETL) tool will be needed to automate the extraction of data. An SQL-based system like Microsoft SQL Server or Azure can be used, or a cloud-based service such as Skyvia.
Step 4: Create a Sample Report
Lastly, you can generate the reports and dashboard that your stakeholders asked for. Since they are already familiar with Microsoft Excel, this is probably the best option. However, you can also use Microsoft Power BI and Microsoft SQL Server reporting services.
Conclusion
Thanks for reading our guide on how to build a data warehouse! We hope it has helped get you started on building your data warehouse.
