What’s the difference between a data mart and a data warehouse, and which might be a better fit for your business? Keep reading for a comparison of these two data storage solutions.
Big data tends to be chaotic: it might come from a variety of sources and in various formats and volumes. Combining different data sources into a standardized, actionable format is no easy task. Thankfully, industry has come up with several data architectures to streamline data infrastructure implementation.
Each data architecture is a blueprint that defines data-related processes. It governs how we collect, transform, store, and distribute data, as well as how stakeholders use it. A well-defined architecture should reflect your business’s data strategy. When properly implemented, a data architecture should also provide a comprehensive view of your enterprise data while enabling your end-users to access the data they need.
Data warehouses and data marts are among the most commonly employed data architectures. Before looking at both in turn, we’ll cover some of the most common misconceptions regarding data warehouses and data marts.
A data warehouse (DWH) is a database that consolidates multiple other databases into a unified location. Traditionally, companies required two types of databases: one for storage and another for analysis. This gave rise to DWHs, which were developed to facilitate reporting and data analysis. There are a few arguments for using data warehouses:
But data warehouses are not without shortcomings.
For one, data warehouses have a slow time-to-market. It can take months or years to integrate legacy, operations, and third-party vendor data. DWHs also require resources to build, use, and maintain. The cost can be high, so implementing a DWH needs solid justification. We discuss this in greater depth in our article on enterprise data warehouses.
You can think of a data mart as a smaller, domain-specific data warehouse. A data mart does not offer an enterprise-wide view of data; it focuses on processes specific to a business unit like marketing or finance. The limited scope of data marts means they’re cheaper and faster to build compared to DWHs.
End-users can see a data mart as a black box. They care about retrieving the data and analyzing it, and data marts provide the APIs they need. Data warehouses usually require more complex queries, making data retrieval not as straightforward.
Data marts can be built from an existing data warehouse (using a “top-down approach”), or separately from data sources (using a “bottom-up approach”). We illustrate both approaches below:
Both approaches have arguments in their favor. The top-down approach ensures uniformity across your data marts, but you’ll require a DWH. On the other hand, the bottom-up approach does not require a pre-existing DWH. It is faster and more convenient for many businesses to build a data mart from scratch.
But the fragmented nature of data marts brings us back to a familiar problem.
Without proper data governance, corporate departments are able to create overlapping data marts. This gives rise to conflicting data definitions, redundancies, different data interfaces, and multiple competing sources of the truth.
To avoid this problem, it’s important that data marts conform to a company-wide data standard. This will also prove useful for eventually integrating data marts into a data warehouse.
Data warehouses are almost unavoidable for companies that work with big data. This is especially true for organizations who collect their own data with established strategies and pipelines. Though implementation and maintenance costs have historically been high, tools like Amazon Redshift, Snowflake, and BigQuery make data warehouses increasingly more accessible.
If you’re a smaller company with limited resources and your company’s analytics investment does not need to cover every department, you might want to opt for data marts. They’re faster to implement, even without an organization-wide data strategy in place. Once your business starts using data marts, you can always consolidate them into a data warehouse.
For example, consider Company A, a 10-person law firm serving a small number of clients. Because the firm collects a limited amount of data on a small number of clients, a data mart can be a more practical solution than a data warehouse.
On the other hand, consider Company B, a Fortune 500 utility company with millions of customers and thousands of employees. In this case, a data warehouse might make more sense as it’s able to store and maintain larger datasets across multiple business departments.
The chart below provides a summary of the main differences between data warehouses and data marts.
|Data Warehouse||Data marts|
|Focus||Data integration||Data integration|
|Intended users||Data scientists, engineers, and analysts||Any business user|
|Time to market||Slow||Fast|
|Cost of implementation||High||Low|
In this article, we compared data warehouses to data marts. Both solutions allow companies to more efficiently perform data analyses and thus gain better insights. The choice between a data warehouse and a data mart may depend on your data strategy, the size of your company, and your resources.
Still not sure how to proceed? We’re happy to help!
At Mighty Digital we’re experts in planning, implementing, and maintaining data storage solutions. We’ll help you implement the right tooling to take your data-driven organization to the next level.