A data warehouse is a centralized system that efficiently stores and manages company data. Current and historical data from all sources can be collected, linked, and analyzed in one place. This helps companies gain better or new insights into their business and make informed decisions.
Compared to the data warehouse, which is primarily used to collect raw data that can be combined and analyzed by data scientists as they see fit, data in the data warehouse is filtered, processed, and structured to make it immediately available for reporting and analysis.
How Does a Data Warehouse Work?
The data available in a data warehouse is first assessed, processed, edited, and prepared for further structured analysis. It is then made available to the users of the data warehouse.
The data warehouse architecture consists of three layers:
- A bottom layer with the actual database server where the various data are stored and loaded.
- A middle layer where the data is accessed and analyzed.
- A top layer with reports and data mining tools that display the results.
A Typical Data Warehouse Also Has the Following Main Elements:
Central Database: A simple relational database management system, such as MySQL or MariaDB, is used to collect, store and manage the underlying data.
ETL tools – The word ETL stands for “extract, transform, and load.” These tools extract data from the underlying database, transform it so that it can be linked to other databases, and prepare it for further analysis.
Access tools – These tools provide the end-user with the functionality needed actually to access the data. They can be used for analysis, query abstraction, reporting, and data mining. In addition, access tools are also used to visualize and present data. Of course, sophisticated tools based on artificial intelligence can also be used here.
Metadata – These are data that describe other data – for example, to record their origin or to determine their structure. All of this data is collected in a system designed for fast access and efficient and thorough analysis.
What Data Does the Data Warehouse Contain?
In principle, a data warehouse can store, process, and retrieve data from almost any source – it is designed to do so. However, for cost reasons, it is advisable to plan in advance what data you want to store in the data warehouse and for how long rather than taking the often tempting but unnecessary approaches.
The data that goes into a data warehouse can be structured, semi-structured, or unstructured. It can come from sources such as internal applications, third-party systems such as ERP, CRP, Logistics systems, or the e-commerce platform itself. Here are just a few examples of data sources whose data records can be merged into a data warehouse:
- Raw data from analytics platforms such as Google Analytics. Campaign data from advertising networks such as Google Ads or Facebook Ads.
- Marketing data from tools like Mailchimp or HubSpot. Order data from e-commerce systems such as Adobe Commerce powered by Magento and Shopware.
- Customer data from CRM systems such as Salesforce or MS Dynamics CE
- Inventory information from ERP systems such as SAP Hana or Microsoft Dynamics 365 F&O
Providers of Data Warehouse?
In the past, companies had to build complex infrastructures themselves to build a data warehouse. Fortunately, with the increasing speed of cloud technologies and automated tools, some data warehouse service providers have established themselves on the market that, significantly reduce both the effort and the cost of a data warehouse.
Cloud-based platforms such as Snowflake, Google BigQuery, Microsoft Azure Synapse, or Amazon Redshift are flexible, fast, cost-effective, and highly scalable. Companies who already use a range of Google services such as Analytics, Ads, or Data Studio should definitely include BigQuery in the shortlist because integrating these services can be faster and more seamless than with most other providers.
