Data warehouse is a term you may have seen used and be wondering what it is. Data warehouses are not a physical space but a system with data drawn from different sources to a single, centralised store.
Data warehouses are not new, having been in use for around three decades, but as with all aspects of IT, they have developed. Once stored on a mainframe computer, today they have a dedicated data appliance or are stored in the cloud.
What a data warehouse actually does
Who uses them?
Data warehouses have a variety of uses including machine learning, artificial intelligence and data mining. Data analysts or a data collection company will also use the data for purposes such as market research or to aid businesses in their decision-making.
A Three-Tier Structure
Data warehouses have a three-tier structure with the bottom tier being a data warehouse server that gathers, cleans and transforms the data. The middle tier has an online analytical processing server to enable rapid query speeds. On the top tier is a reporting tool or a user interface to aid ad-hoc data analysis.
Take a look at Geeks for Geeks for more information on data warehouse architecture.
Weighing the benefits against the drawbacks
Benefits of Data Warehouses
Data warehouses offer better data quality with faster insights for businesses, resulting in smarter decision-making. By gaining information from a data warehouse rather than from many different data stores, the data analysis process is more streamlined and competitive.
Using skilled data analysts or a data collection company can help businesses thrive in an increasingly competitive world.
Drawbacks of Data Warehouses
The vast amounts of data stored and the increasingly sophisticated analysis of that data can result in data warehouses becoming expensive and inflexible. Unstructured data can be a particular challenge for data warehouses.
However, already the system is evolving with data lake houses becoming the next stage of data management solutions. These have the ability to store a greater variety of data types including structured, semi-structured and unstructured data. Ultimately, it is up to individual businesses to decide which data management system is likely to be the most effective for them.
Cloud or in-house: where the data is kept
A warehouse has to live somewhere, and the choice between hosting it yourself and using the cloud is a decision many smaller organisations face. Virtualisation lets one piece of hardware run several server systems, which usually costs less than a separate machine for each job. It also gives a business hands-on control, and it suits those with skilled IT staff and very specific needs.
Cloud computing takes the same idea across many machines and runs it over the internet. It is easy to scale up as needs grow, which matters if today’s requirements are small but tomorrow’s may not be. The trade-offs are that your information sits on someone else’s servers and you pay a monthly fee to keep using them.
Cost works differently in each case. An on-site setup may cost more at the start but be cheaper over time, because there is no rising monthly charge. Talking to an IT provider who understands the business is the best way to decide.
Summary
This article explains what a data warehouse is: a system that draws data from different sources into a single centralised store, usually cloud-hosted today. It covers its three-tier structure and its uses in machine learning, AI and business decision-making. It weighs benefits such as better data quality and faster insight against drawbacks like cost and difficulty handling unstructured data, noting data lakehouses as an alternative.
Questions this article raises
- How does a data lakehouse actually differ from a traditional data warehouse in practice?
- What size of business typically needs a full data warehouse rather than simpler analytics tools?
- How much does cloud hosting reduce the cost of running a data warehouse compared with on-premise appliances?
