A data warehouse is a relational database designed for analytical rather than transactional work, capable of processing and transforming data sets from multiple sources. On the other hand, a data mart is typically limited to holding warehouse data for a single purpose, such as serving the needs of a single line of business or company department.
A data mart is a specialized and focused subset of a data warehouse, designed to cater to the specific analytical needs of a particular business unit, department, or user group within an organization. Unlike a data warehouse, which serves as a centralized repository for the entire enterprise, a data mart hones in on a specific subject area or use case. It is curated to contain only the relevant data required for a particular analytical purpose, making it more streamlined and efficient for querying and reporting.
Data marts offer targeted insights and support faster decision-making within their designated areas, providing valuable and contextually relevant information to their respective users. As standalone entities or as parts of a data warehousing strategy, data marts offer a tailored solution for the analytical needs of different business segments.
What’s Are the Differences between a Data Mart and a Data Warehouse?
As a data mart is a subset of a data warehouse, businesses may use data marts to provide user access to those who cannot otherwise access data. Key difference between data marts vs. data warehouses is that data mart offer cost-effective storage and faster analysis due to its specialized, smaller design.
Let's dive into differences between a data mart and a data warehouse:
- Size: In terms of data size, data marts are generally smaller, typically encompassing less than 100 GB. In contrast, data warehouses are much larger, often exceeding 100 GB and even reaching terabyte-scale or beyond.
- Range: Data marts cater to the specific needs of a single line of business or department within the organization. On the other hand, data warehouses are designed to be enterprise-wide, spanning across multiple functional areas and serving the data requirements of the entire organization.
- Sources: Data marts draw data from a limited number of sources, while data warehouses have a more comprehensive scope, collecting data from a diverse array of sources.. Data warehouses integrates information from numerous operational systems, applications, and external feeds to offer a holistic and comprehensive view of the organization's data landscape.
The Need for Creating a Data Mart
Slow and overloaded data warehouses are often the underlying reason for creating data marts and frequently serve as their underlying data source. Often, as data volumes and analytics use cases increase, organizations cannot serve every analytics use case without degrading the performance of their data warehouse, so they export a subset of data to the mart for analytics.
Snowflake: Eliminating the Need for Data Marts
Snowflake revolutionizes traditional data mart approaches with its advanced cloud data architecture, offering vast scalability and flexibility. Integrating Apache Iceberg tables, Snowflake enhances its warehousing and lakehouse capabilities, efficiently managing diverse data types and optimizing queries. This innovation eliminates the need for separate data marts by maintaining high performance within a unified system.
Snowflake supports various workloads, including AI/ML and cybersecurity, within an integrated platform. Its elastic architecture swiftly scales to new demands without disrupting ongoing operations, exemplifying agility and efficiency for modern data management.