• Understand the definition of a Data Warehouse
• Learn about the types of data that exist in a Data Warehouse
• Learn the purpose of Data Warehouses
• Understand how Data Warehouses work in comparison to other types of data storage.
If you want to turn all of the data that your business generates into meaningful insights, a data warehouse is a vital resource. Data warehouses play a critical role in the more extensive data management process by making it easy to store and analyze large volumes of information.
This article explains what data warehouses are, how they work, and how they compare to other data storage solutions, such as databases and data lakes.
Data Warehouse, Defined
A data warehouse is a body of data stored in a specific, structured way. Usually, data warehouses support a particular use case, such as analyzing data to glean business intelligence insights.
Other software generally performs the analytics operation(s). Data warehouses provide the data that drives analytics, but they don’t analyze the data themselves. For that, you’ll want a data querying and analysis tool, like LogDNA.
Although all data warehouses have structure and organization (which is one of the characteristics that makes them different from other types of data storage resources, like data lakes), we can create data warehouses in various ways. You can use multiple open-source platforms to develop a data warehouse on your infrastructure, or you could use a managed data warehouse service running in a public cloud.
Likewise, the strategies used to structure and organize data inside a data warehouse vary widely. What matters is that the data has some organization to it, which is more important than the data storage strategy, how the information is labeled, etc.
Which Data Can Exist in a Data Warehouse?
A data warehouse can store virtually any data. Indeed, part of the purpose of a data warehouse is to allow businesses to collect data from various sources, then store the data in a systematic manner that facilitates easy analysis.
That said, examples of data that businesses commonly store in a data warehouse include:
- Logs from applications and operating systems
- Business transaction data
- Logs of user authentication and authorization requests
- Data from CI/CD operations
- Network traffic logs
Note, too, that the data stored in a data warehouse can vary in age. Data warehouses often include near real-time data generated, but they could also contain data from weeks, months, or even years in the past.
What Is the Purpose of a Data Warehouse?
The primary purpose of a data warehouse is to provide a central repository of information that can be quickly analyzed and queried to generate relevant insights.
The specific types of insights generated from a data warehouse can vary. Data warehouses can answer business questions, like “Which products generated the most revenue this quarter?” or “Which applications drive the highest volume of sales?” They could also answer technical questions, such as “What is happening on the network when [X] application experiences a performance slowdown?” or “Which applications have the lowest availability?”
Data warehouses are also helpful because, as noted above, they make it possible to store historical data alongside more recent data. That doesn’t mean data warehouses are a solution for archiving data; data archives are distinct from data warehouses. However, unlike application logs (which typically only record information from a fixed period before they fill up and become overwritten), data warehouses can be used to store and compare data from multiple points in time. As such, data warehouses help to track trends over time.
Data Warehouses vs. Other Types of Data Storage
While there are similarities between data warehouses and other data storage solutions and strategies, data warehouses have unique features that distinguish them from other solutions.
Data Warehouses vs. Databases
In general, databases are very similar to data warehouses, in that databases also store data in a structured way that enables easy analysis.
However, the main difference between data warehouses and databases is that databases typically only store one type of data. For instance, a database may keep sales records or transaction records. In contrast, data warehouses store multiple data types in a central location.Because databases can be one of the data sources for a data warehouse, you can think of a data warehouse as a “database of databases.”
Data Warehouses vs. Data Lakes
Data lakes store data in raw, unstructured form. They are typically used to house data before being transferred to a data warehouse. So, while data lakes are helpful for initial data collection, they don’t enable easy data analytics, as data warehouses do.
Data Warehouses vs. Data Lakehouses
In recent years, the concept of the ”Data Lakehouse” has grown in popularity. A data lakehouse is essentially a mix between a data lake and a data warehouse. In a data lakehouse, you can store unorganized, unstructured data while still analyzing it.
Data lakehouses achieve this goal by automatically structuring raw data. This approach makes the data more organized than it would be in a data lake, but without the considerable effort required to set up a data warehouse.
Although data lakehouses can be more advantageous than data warehouses in some cases, businesses might choose to use a data warehouse instead of keeping their raw data separate from their structured data. In a data lakehouse, you lose this separation. In addition, the majority of currently available data lakehouse solutions are proprietary platforms. Businesses that want to set up their own data management platform using open source tools will find it easier to do so if they build a data warehouse instead of a data lakehouse.
Data warehouses are a core building block of efficient data management and analytics strategy. When paired with analytics tools like LogDNA, they allow businesses to store and query large quantities of data to gain important business-related and technical insights.