According to the latest estimates, more than 400 million terabytes of data are generated daily. With growing volumes of data, it becomes essential to implement modern strategies for effective data management. To optimally utilize the potential of data, you must store it in reliable and scalable solutions.
There are multiple data storage solutions available in the market, including databases, data warehouses, data lakes, and data marts. Among these, a data mart constantly supports analytics teams in addressing domain-specific requirements. The graph below reflects the Google search trends for the term “data mart” over the past five years.
Such consistent interest highlights its relevance to data professionals and enthusiasts worldwide.
This guide comprehensively covers every aspect of a data mart, along with its types, working principles, implementation steps, and comparisons with other data storage systems.
What Is a Data Mart?
A data mart is a data storage system that contains a subset of data corresponding to an organization’s business unit. It is part of a broader system, like a data warehouse, that reduces data ambiguity by restricting data access based on departmental use cases. Constraining the information to only a subset of the original data enables the generation of business-specific insights.
For example, your organization might consolidate large amounts of data from various sources, including marketing platforms, ERP solutions, and IoT devices, into a database. This creates a unified view of diversified information. However, to store data for a specific department, such as marketing, you can use a data mart.
Importance of a Data Mart
- Data Management: Compared to other data storage systems, a data mart provides better data management capabilities. Focusing on a single domain confines the amount of data to a limit, reducing clutter.
- Data Accessibility: Storing data in a data mart can aid in enhancing the accessibility of specific information. It contains information relevant to a department within your organization. Instead of searching through the full database or a data warehouse, you can quickly retrieve the data from a mart.
- Insight Generation: Implementing this data storage system can support in production of better insights that cater to the specific business domain. For example, by analyzing marketing-related data, you can produce effective marketing campaigns targeting potential customers.
- Cost Optimization: As data marts only store a portion of the overall data, it is considered a budget-friendly option compared to setting up a new data warehouse. It only incurs a fraction of the cost of a data warehouse.
Types of Data Mart
You can set up a data mart using three different approaches: dependent, independent, or hybrid.
Let’s explore each type in detail:
Dependent Data Mart: In dependent solutions, the data mart stores a fraction of data from an existing data warehouse. The data is first extracted from diverse data sources and stored in a warehouse. After the data is available, you can query and retrieve the domain-specific information in a data mart. In this way, you can segment the entire data warehouse, distributing subject-specific data among various marts.
Independent Data Mart: Solutions that don’t rely on an existing central data warehouse are independent. You can directly extract business data from internal or external sources and store it in a data mart. This approach is useful if you need a quick analytical solution without the overhead of a full-scale data warehouse.
Hybrid Data Mart: These data marts consolidate data coming from an existing warehouse as well as external sources. With this solution, you can test data arriving from independent sources before loading it into the permanent storage system.
What Are the Structures of a Data Mart?
Data marts store data in well-defined structures, which makes the data easier to access. The information is organized using multi-dimensional schemas. Here are the key data mart structures:
Star Schema
This is a star-shaped structure where a central fact table is linked to multiple-dimension tables. The fact table consists of transactional data that you can use for analysis, while the dimension table contains descriptive information about the fact table. Each dimension table is linked to the fact table with a unique identifier—a foreign key—such as a customer ID.
Snowflake Schema
Snowflake schema is an extension of the star schema that uses normalized dimension tables to store fact details. Each dimension table is broken down into smaller components, or subdimensions, to gain more storage efficiency.
However, the query performance of the snowflake schema deteriorates when compared with the star schema. The denormalized structure of the star schema, while introducing data redundancy, can improve query speed by reducing the need for complex joins.
Fact Constellation Schema
A fact constellation schema, also known as galaxy schema, contains multiple fact tables that share some common dimension tables. This structure is preferable for complex scenarios of storing interrelated data. Using fact constellation, you can define the relationships between different business processes in a data mart.
Data Mart: Working Principle
The working principle of a data mart depends on the type of solution that is being used. It requires a data retrieval mechanism for extracting data from either a warehouse or an external source.
To populate a data mart, you must create an extract, transform, and load (ETL) pipeline. In this pipeline, you can extract data from one or more sources and transform it into a format compatible with the data mart schema. After the data transformation phase, you can consolidate the transformed data into the storage system.
Steps for Implementing a Data Mart
To implement a data mart, follow this structured guideline:
Step 1: Understand Business Requirements
Before getting started, you must thoroughly understand your business requirements. Identify the need for a data mart. This initial phase assists in determining the goals that your organization intends to achieve with this solution.
Step 2: Choose the Data Mart Architecture
After clearly defining the requirements, you can select the specific data mart architecture that aligns with the business needs. It is important to ensure that the chosen architecture is compatible with your existing tech stack. Following the design of the architectural framework, you can decide on deployment methodology—whether to deploy in the cloud or on-premises.
Step 3: Define the Data Mart Schema
You can start creating a schema to store your data. The structure of the schema defines how data will be saved in the mart. Depending on the type of data you have and the analysis needs, you can choose from star, snowflake, or fact constellation schemas.
Step 4: Data Migration
Populate the data mart with relevant information. In this stage, you can create strategies to develop data pipelines that efficiently handle data migration. To consolidate data, the structure of the data must match the target schema. You can accomplish this by establishing ETL data pipelines that transform data before loading it into the storage space.
Step 5: Implement Security Measures
You must secure the data storage solution from unauthorized access. This step requires you to define privacy measures like establishing multi-factor authentication (MFA) and authorization controls, data encryption, and role-based access control (RBAC).
Step 6: Continuous Maintenance
Continuous maintenance of a data mart is crucial for ensuring system reliability. This requires you to regularly monitor system health and identify potential issues that might reduce efficiency. Performance tuning processes, like database indexing, can optimize retrieval operations.
Data Lake vs Data Mart vs Data Warehouse
| Aspect | Data Lake | Data Mart | Data Warehouse |
| Key Purpose | Used to store raw, unprocessed data from various sources. | A specialized subset of a data warehouse focused on a specific business unit. | Used to consolidate data from multiple sources for analytics and reporting. |
| Data Type Support | Structured, semi-structured, and unstructured | Structured, domain-specific data. | Primarily structured data. |
| Data Sources | Wide variety of data sources, including marketing, ERP, CRM, and IoT. | Limited number of sources that produce business-focused information. | Multi-source support. |
| Use Case | It can allow the management of terabyte and petabyte-scale data. | Analysis of smaller datasets, usually under 100 GB. | Analysis of larger datasets (>100GB). |
| Business Scope | Organization-level. | Department- or team-specific. | Enterprise-level. |
| Pricing | It will initially cost less, but pricing can go up based on scalability and processing requirements. | Lower cost than data lake and warehouse. | High cost as it offers enterprise-scale support. |
Key Use Cases
- Market Analysis: Consolidating data into a data mart can be beneficial for analyzing potential business opportunities. By migrating data into a centralized repository, you can get detailed information about the competitive landscape of individual industries. You can apply machine learning algorithms to the market data to predict future trends.
- Sales Analytics: You can use a data mart to store sales information, such as customer details, transaction history, product information, and key performance indicators (KPIs). This can assist your sales department in tracking how different products perform in a particular demographic group.
- Resource Planning: Integrating specific ERP into a data mart can help create strategies that improve resource utilization. By implementing these plans, you can save costs and optimize business performance.
Challenges
- Developing a custom data mart involves a thorough understanding of business requirements. This can be challenging and time-consuming.
- To ensure operational efficiency, it is crucial to plan out error management strategies before beginning data migration.
- While data marts support departmental needs, storing large amounts of information in isolated data solutions can lead to data silos. To overcome this limitation, you can use both warehouses and data marts together. However, this approach requires more management and resources.
- Establishing ETL pipelines can be difficult, especially if the data is available on third-party platforms. To store complex data, you must define robust transformation strategies to make it compatible with the data mart schema.
Closing Remarks
Data marts offer increased data access efficiency and flexibility. However, as the data volume grows, on-premise solutions can face scalability and management challenges. To overcome these issues, you can deploy these storage systems on a cloud, which not only improves data management but also optimizes costs.
Once the data is efficiently stored, you can apply machine learning principles to create business-oriented insights that can assist in improving performance. While the advantages are significant, you must also consider the challenges, like data security, of developing a new data storage system. Addressing these limitations in the early stages can assure long-term success.
FAQs
What is data mart?
A data mart is a focused data storage solution that only holds your organization’s department-specific information.
How to create a data mart?
You can follow a structured procedure that includes steps like understanding business requirements, establishing data mart architecture and schema, migrating data, implementing security measures, and continuously maintaining the solution.
What are the benefits of data marts?
Some of the most common benefits are enhanced accessibility, cost-effectiveness, simpler management, and quicker insight generation.
What is the key difference between data mart vs data lake?
The key difference between data mart and data lake arises due to the scenario for which each solution is used. For instance, to store domain-specific structured data, you can use a data mart. If the data is unstructured and raw, you can choose a data lake as a storage system.
