The Best Practices On How to Build a Data Warehouse
The ability to collect, process, and analyze the data is a valuable asset that comes as a big competitive advantage. This is why companies need to build a data warehouse. A data warehouse helps keep all the data in one place in an organized manner and helps analyze it in order to extract useful insights.
In this article, we will have a look at the step-by-step process of building a data warehouse and its best practices.
Data warehouse: what is it?
A data warehouse is a system that stores all historical data from multiple sources and analyzes this data to provide valuable insights to all parties involved (company owners, different departments, etc.). This is actually the main difference between a data warehouse and a database since the database stores real-time data related to a particular area of your business.
There are several reasons how a data warehouse can benefit your organization:
- Quick access to the needed data,
- Fewer chances for errors and data inconsistency (since the data is processed automatically),
- Easy data analysis via visualization,
- A high level of security.
Even though a data warehouse seems to be a complex and comprehensive solution, its design process is actually quite simple. Below we listed down the essential steps to follow in order to create an efficient and high-performing data warehouse.
Define your business requirements
Each data warehouse is unique for every company. Why is that? Because data warehouses are built in correspondence with the business requirements.
Hence, before starting to plan the data warehouse design, the first thing you need to do is to define the business requirements for your specific company. Why do you really need a data warehouse, what kind of information do you need, and who will be using the data warehouse? These questions must be answered in order to understand the architecture of your future data warehouse and to manage the permission rights properly.
Some of the tips to help you during the requirement gathering stage:
- Identify the departments that will work with the data and their goals,
- Create a disaster recovery plan,
- Think about all needed security layers,
- Analyze your data needs and your current tech stack.
Set up separate physical environments
It’s a common thing for data warehouses to have three separate physical environments: for development, testing, and production. And there are several reasons to keep these environments separated:
- With testing and production environments separated, it’s easier to test any changes.
- Limited access to production data contributes to better security.
- The workload on the production environment is usually bigger than in other environments.
- It’s easier to track data integrity over three different environments.
- Running tests can interfere with the environment performance so you need separate environments to avoid performance breakdowns in the production environment.
Remember that some companies prefer to have more than three environments and it’s completely okay - but these three are considered the core ones and should definitely be implemented.
Choose the right data modeling approach
When designing a data warehouse, you can go with the following approaches:
- Top-down: a central repository is designed first and the data marts are created next.
- Bottom-up: data marts are built first and are then combined to create a warehouse.
- Hybrid: this approach recommends first creating a central repository in third normal form and then create several data marts in third normal form. In this way, dual modeling takes place.
Each approach has its pros and cons. For example, the top-down approach is recommended for those companies that wish to obtain a general picture of the business. As well, this approach provides flexible and integrated architecture. The bottom-up approach, in turn, is more suitable for those companies that wish to retrieve specific data first. This approach also puts emphasis on creating very user-friendly data structures.
Hence, we highly recommend studying the possible options and its pros and cons in order to choose the most suitable option.
Choose the proper ETL solution
ETL stands for Extract, Transfer, Load and is basically the main concept of data warehousing. First, the data needs to be extracted from the source, then it has to be transferred into the right format, and finally the data is loaded into the system.
The good news is that there are plenty of available ETL tools out there that will significantly speed up the data processing and management. The bad news - some ETL tools deliver poor performance and can negatively impact the whole data warehouse performance.
When choosing an ETL solution, look for high speed, good visualization, and the creation of consistent data pipelines. In this way, you will ensure that the tool will contribute to efficient performance and not the other way around.
Data warehouse design: best practices
Opt for custom-made solutions
Even though there is a great number of one-stop solutions that promise a quick start and a rich variety of features, it’s always best to choose a custom solution. Why is that?
First, a custom-made solution guarantees to cover all your business needs - while a generic solution is aimed at the most common needs that may not necessarily match your business goals.
Second, as your business grows, your needs will expand and change too. However, not all one-stop solutions are scalable enough so it will result in critical issues in the future.
And finally, custom-made solutions guarantee 100% data ownership for you which may not be the case for some of the one-stop solutions.
Use specialized tools
While we recommend using custom solutions, it does not necessarily mean that you have to create everything from scratch. Specialized tools such as ETL tools do not only save a significant amount of time but also help produce faster and better results.
Mind though that such specialized tools should be used for certain purposes only. For example, if you have a classic three-layer data warehouse architecture, it is recommended to deploy a tool per layer. It is done in order to minimize further risks or issues. For example, if you have a tool that works on several different tasks (i.e. BI and data transformation) and if for some reason you decide to change your BO vendor, you will need to find a reliable and efficient alternative to the previous tool.
Pay attention to the reporting tools
The main purpose of creating a data warehouse is to provide data to different departments and this data should be presented in an understandable format. The thing is, most marketers or sales specialists are non-technical professionals so the data should be presented to them in a clear and useful manner.
Hence, you need to pay utter attention to the BI or reporting tools that you plan to use. Here are a few tips:
- Think about who will be using the solution and how?
- What kind of reporting format will you need?
- What is the best solution you can get within the determined budget?
Thinking about data visualization is really important as data reporting is the whole point of establishing a robust data warehouse.
Don’t forget the after-deployment support
After-deployment support and maintenance are incredibly important so make sure you don’t forget about them! When your data warehouse is built and launched, take care of the following aspects:
- Change management: educate and train your employees on how to use the data warehouse properly,
- Establish clear data administration procedures,
- Establish performance monitoring procedures,
- Test the data warehouse performance in order to identify any weak or problem areas and prevent any issues.
A data warehouse is a must for any company that wants to make data-driven decisions that will lead to further company growth and development. As already mentioned, a custom-made solution is a preferred option as it precisely covers all business needs of a company and is built with scalability and security in mind. As well, such a solution should be easily integrated with the needed third-party tools in order to achieve maximum efficiency.
Irina LinnikView all articles by this author.
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