A data warehouse is an organizational structure that collects large amounts of historical information to facilitate more informed business decisions. By having access to this accumulated knowledge, decision makers can make better forecasts and predictions for the future.

A data warehousing (DW) architecture was first defined in 1999 by Gartner as “a collection of technologies used to extract value from structured and semi-structured data”.1 Since then, it has become one of the most important concepts in analytics because it lays out the fundamental components and relationships of a DW.

In fact, almost every advanced analytics method — such as predictive modeling, statistical analysis, clustering, or optimization techniques — are either directly related to or require you to have a well designed DW.2

Data warehouses store vast troves of data organized into tables and columns. This organization is referred to as the schema of the database and allows us to easily search through different parts of the information.3 The way these databases are interconnected is via a standard term: ODBC.4

This article will go into greater detail about how to choose your initial database platform, what types of schemas exist, and some common terms in data warehousing. But before we get too technical, let’s take a look at some examples!

Examples of data warehouses

There are many types of data warehouses depending on what type of information they contain and how they organize and retrieve it.

Types of data warehouses

There are two main types of data warehousing structures that most companies use. They are what we refer to as an external or off-site database, and internal or in-house databases.

The difference between the two is where the warehouse stores its information. An external warehouse does not reside within your organization’s environment, nor do they connect back into your existing systems.

This makes it much more difficult to access the information because you have to go through additional steps to get it. External warehouses also cost extra money to maintain which can be prohibitive if you don’t have a lot of resources available.

An internal data warehouse works by storing all of this information locally, within your organizational boundaries. This removes one of the major limitations of external warehouses – accessibility. You would just need to make sure that everyone has permission to view the information!

However, internal data warehouses come with their own set of problems. Because everything is stored together, there is always a risk that someone will accidentally delete or add something they aren’t supposed to when accessing the information.

Data warehouses fall under a broad category known as relational databases. These differ from regular databases like MySQL or Oracle in that they organize information using strict rules and patterns.

There are several different variations of how people structure relational databases, but the basics remain the same. Information is organized via tables which relate to each other using relationships.

Requirements for a data warehouse

The first requirement is that you will need to have a source of all your company’s information. This can be internal sources, external sources, or both depending on what types of reports you want to create in the future.

Data warehouses are not focused on just gathering raw data, rather they focus on analyzing this data so that users get the most relevant insights.

By having access to all of the necessary internal and external sources, then your organization will know everything it needs to know!

That is why the second important element of establishing a data warehousing department at your business is internal communication.

You must make sure that everyone in your organization knows about the data warehouse and has full permission to use it.

This way, people do not worry if something gets left out because someone else has access to the same information.

It also helps prevent gossip and rumors since no one has complete permissions.

Selection of a data warehouse software

When determining which database management system (or what is sometimes referred to as a “data warehousing” or DWA software, make sure that it can handle very large amounts of concurrent read and write activity.

A common misconception about DWAs is thinking that they are only for big companies with lots of money to invest in technology. This isn’t the case at all!

Data warehouses really come in two main flavors- clustered columnar storage and non-clustered rippled file systems. Both have their pros and cons, but most experts agree that clustering is usually the better pick unless you have a good reason why not.

So how do you know if your organization has a good reason to choose one over the other? Here are some tips to help you determine the right fit.

Designing a data warehouse

The first thing to consider when designing a data warehousing structure is what your business needs are. Do you have limited access to internal systems, or do you have enough resources to start building one yourself?

If you’re in the former situation, then it may be time to outsource your DW. There are many reputable vendors that can help you get up and running quickly with very little overhead.

The cost of outsourcing will vary depending on which vendor you choose and how much work you need done, but we recommend doing some research and comparing prices before making a decision.

In either case, make sure to look at their past performance as well as reviews from other customers to determine if they are reliable.

Data warehouse upgrades

A lot of times, it can be tricky deciding whether to add new columns or tables into your data warehouse. This is particularly true when you are trying to maximize performance by narrowing down what information you have access to.

In fact, some experts even suggest that having too many internal details in your DW may actually hurt performance! That isn’t very practical though, is it?

Luckily, there are several strategies for upgrading your data warehousing set up. Here we will go over three such strategies, along with tips and tricks for each one.

Data warehouse maintenance

The other major part of this job is keeping your data warehouse active and operational. This includes ensuring that all tables are kept up to date, ensuring adequate disk space for the database, running regular performance checks, and updating any software or configurations.

Data warehouses typically have several steps in their processing cycle which can take minutes or hours to run through depending on how long it takes to complete them. If you notice a lag in response time or if the system gets hung during a process, it’s important to check to see what happened last so that you can prevent the same thing from happening again.

It is very common to use log files or databases as a way to access information about past activity.