Published July 20, 2022

Compare and Contrast: Database Vs Data Warehouse

Learn the differences and similarities between two data analysis tools and which may be best fit for your organization

Big data can be confusing, and the solutions to analyze your data are oftentimes costly and a bit cumbersome. To ensure you are getting a solution that is optimized for your specific business needs, here are a few important questions to ask yourself: 

  • What data needs to be analyzed?
  • Are there multiple data sources?
  • For what purpose is the data being analyzed?

Understanding the answers to these questions, and many more like it, will be imperative in the decision-making process. Although to achieve an understanding of which data solution you need, you must first comprehend the difference between some of the key solutions. Take, for example, data warehouse vs database. What is the difference between the two? And what do they each mean in the context of your organization’s data? Let’s take a brief look into each to evaluate which of these two concepts can truly yield the most benefits from your data. 


What Is a Database?

Simply put, a database possesses specific data sets organized in a table format. The data within these databases are aggregated primarily for data organization and retrieval. Databases may be considered subsets of data that are collected from human entries (through a software application) and/or generated by various devices. 

Bringing this to the data warehouse vs. database conversation, it would be within the data warehouse solution that analytical queries can be compiled to establish relationships between a variety of data sets, or information within the databases themselves.

Regardless of the business size or industry, databases will exist within an organization. However, the more data a business possesses, the higher the need for a more complex solution to aggregate and analyze that data. This is where a variety of solutions may come into play, such as data warehouse solutions.


What Is a Data Warehouse?

A data warehouse is a solution that is structured specifically for the handling of business intelligence and data analytics activities. The schema utilized within data warehouses has usually been predefined with data structures that provide quicker results with less complexity. This enables anyone who has access to analyze and interpret data points with an elevated sense of ease.

Data warehouses are typically a good fit for enterprises with a use-case based need to help organizations extract value from numerous data points oftentimes when working with disparate data sources. Data warehouses can be hosted both on-prem, in the cloud, or a combination of the two. Interested in learning more about data warehouses? Read “Data Lake vs. Data Warehouse“.


The Relationship Between a Database and a Data Warehouse

Databases and data warehouses certainly tie together. Databases can be found in any organization, and categorize a variety of departmental data or company-wide data, depending on the database itself. However, as the organization begins to grow and more data is needed to cross-reference or determine relationships to empower business decisions, data analysts often request a more robust tool to help gather and interpret the data. 

A data warehouse can almost be considered a warehouse for a predetermined set of databases within the organization. Not all databases in a business will go into a data warehouse, because it may not make sense from an analysis perspective. For instance, you likely would not need internal transactional data inside a data warehouse that is aggregating data points on marketing campaigns.


The Differences: Data Warehouse vs Database

Databases can be as simple or complex as the creator wishes to make them. They are often a basic table format, with data arranged into columns and rows. There may also be multiple partitions, with data segmentation based on various categories. For instance, accounts receivable data might be partitioned by clients, subsidiaries, or some other designation that represents a logical distinction. The same could be said for marketing, with a partition for each marketing campaign executed. Going back to the former question of data warehouses vs databases, databases are often kept internally or on cloud-based platforms, and typically are used to store and retrieve data for routine queries.

By contrast, data warehouses usually offer quite sophisticated analytical features, with an array of benefits. They are typically used to perform complex queries on very large datasets.  Due to their level of sophistication, and the amount of data within them, they often come at a higher price point than databases. 

When selecting a data warehouse for your organization, it  is important to understand which data warehouses will best suit your particular business needs by understanding what each solution has for features, abilities, and integration. Furthermore, scheduling demos to take a look at the capabilities of a data warehouse will greatly help in the decision-making process.  


Get the Data Analytics Solutions You Need With Ocient

Ocient is the leading hyperscale data analytics provider that offers data warehouse solutions to large-scale organizations. By offering massively parallelized processing and performance with a smaller footprint, Ocient reduces total cost of ownership by up to 80%. Ocient’s high-performance data warehouse platform offers limitless scaling – enabling one solution to meet your growing organization’s data needs. We make storing and analyzing your data easier, simpler, and more powerful.

Contact us now or schedule a demo to get the inside look at our next-generation, hyperscale capabilities.