What Is a Data Mart?
A data mart is a subset of enterprise data warehouses. Data marts are tailored to meet the specific needs of a business unit or department. A broader data warehouse provides an organization’s entire range of data. Data marts provide a focus on a particular subject, such as sales, marketing, or human resources.
Much to the disappointment of data warehouse pioneers, traditional data warehousing became cumbersome to manage, so data marts emerged to provide more flexible and distributed data sources to meet business demands for more immediate and relevant data. Different data marts have grown in usage as traditional enterprise data warehouses have declined.
How a Data Mart Works
A data mart operates as a streamlines an entire data warehouse that is a focused segment of a data warehouse designed to cater to the specific needs of a department or business unit within an organization. Here’s a breakdown of how a data mart typically functions:
Data Sourcing
A dependent data mart mainly sources its data either from the central data warehouse. An independent data mart can get data directly from external data sources.
This step determines the type of data that will be stored in the data mart and how it aligns with the specific requirements of the business unit it serves.
Data Processing
Once the data is sourced, we can process data. This involves organizing, cleaning, and structuring the data. The objective is to transform raw data into a format that is suitable for analysis and decision-making while maintaining data governance.
Data processing in a data mart is typically less complex than in a full-fledged data warehouse due to the more single subject-focused nature of data marts.
ETL (Extract, Transform, Load) Process
The ETL process is a core component of how a data mart or data warehouse operates. It involves:
Extracting data from data sources such as a larger data warehouse, and data lakes.
Transforming this data into a structured, consistent format.
Loading it into the data mart for storage and analysis.
Data Storage and Management
In this phase, the transformed data is stored in the data mart’s database. The data is managed to ensure its integrity, accuracy, and data governance.
Data storage in all types of data marts is organized to support quick and efficient retrieval of information.
Data Access and Utilization
Business users and analysts access the data stored in their own data mart to perform various types of analyses. This might include generating reports, populating business intelligence dashboards, conducting data mining, or performing predictive analytics.
The data mart is designed to make this access easy and efficient, particularly for the specific domain it serves.
Business Intelligence Integration
Data marts are often integrated with business intelligence (BI) tools and applications. This allows for the creation of dashboards, visualizations, and other analytical functions that help business users make better-informed decisions based on the data available in the data mart.
Maintenance and Updating
Regular maintenance is required to ensure the data mart remains effective and efficient. This includes updating the data to reflect current information, optimizing the database for performance, and ensuring that the data mart continues to meet the evolving needs of its users.
Types of Data Marts
Dependent Data Marts: These are directly linked to the enterprise data warehouse, drawing data exclusively from this centralized source. Data warehouses provide the data warehouse infrastructure.
Independent Data Marts: As the name suggests, independent data marts operate separately from the central data warehouse. They gather data directly from external data sources, making them suitable for smaller organizations or specific projects that require agility and independent operation.
Hybrid Data Marts: Combining features of both dependent and independent models, hybrid data marts source data from a mix of the central data warehouse and other external sources. This model offers flexibility in data sourcing and is often used in dynamic business environments.
Structures of a Data Mart
The structure of a data mart is a crucial aspect that defines its functionality, efficiency, and suitability for specific business needs. There are several structures that data marts can take, each with its own characteristics and advantages.
The choice of structure depends on various factors, including the nature of the data, the specific requirements of the business department, and the desired outcome of the data analysis. Here are the primary structures commonly used in data marts:
Star Schema
A star-structured data mart schema is one of the simplest and most common structures for data marts.
It consists of a central fact table that contains the main data elements and is surrounded by dimension tables.
Each dimension table is linked to the fact table through a foreign key and contains descriptive attributes related to the data in the fact table.
This structure is beneficial for query performance as it simplifies the data model and enables faster data retrieval.
Snowflake Schema
The snowflake schema is a more complex version of the star schema.
It further normalizes the dimension tables into multiple related tables, forming a structure that resembles a snowflake.
While it can reduce redundant data and improve data integrity, the snowflake schema can also result in more complex queries and potentially slower query performance.
Galaxy Schema (or Fact Constellation Schema)
The galaxy schema can be seen as a collection of star schemas and is used for more complex data mart structures.
The key difference is that it involves multiple fact tables that share dimension tables, suitable for analyzing data from different perspectives or for data marts that need to support a wide range of queries and analyses.
Normalized Approach
In this approach, the focus is on reducing redundancy and maintaining data integrity by using a traditional normalized database structure.
While it ensures a high level of data consistency, it can lead to complex queries and might not be as efficient for read-heavy operations typical in data marts.
Denormalized Approach
This approach simplifies the structured data by combining data into larger tables, reducing the number of joins required during queries.
It improves query performance, making it suitable for analytical queries in data marts, but at the expense of increased data redundancy and potential challenges in maintaining integrity.
The choice of the appropriate structure for a data mart should align with the specific data requirements and analytical goals of the department or line of business it serves. A department can create multiple data marts. All the data stored in other data marts can be federated as needed.
By carefully selecting the right structure, organizations can optimize the performance and utility of their data marts for more effective data-driven decision-making.
Comparative Analysis of Data Mart and Other Data Storage Technologies
Understanding how one data mart technology compares to other approaches can be helpful when making a technology selection decision.
Below we explore various data management approaches, emphasizing their unique characteristics and contrasting them with data marts.
Database and Data Mart: Complementary Tools for Data Management
Database: A database, most often a relational data management system, forms the backbone of data storage and organization. It stores data in two-dimensional tables, linked using key columns to facilitate efficient retrieval and analysis.
Data Mart builds on a database server as the fundamental structure for data collection and management. A data mart uses data extracts from databases (or other sources) and refines it for specific departmental use. One reason to create data marts is to provide a more user-friendly interface for data analysis and retrieval which is an area where a legacy data warehouse fails.
Data Warehouse: The Comprehensive Repository
Data Warehouse: This centralized full data warehouse system aggregates raw data from diverse sources, processing it into structured data, usable formats for enterprise-level analysis.
Data Mart vs. Data Warehouse: Both data marts and data warehouses store and manage data, but they differ in scope, size, and application. A data warehouse encompasses a broad range of organizational data. A data mart is a smaller data warehouse focused on specific subjects or departments, often providing more detail than the central data warehouse. When considering a data mart vs. a data warehouse, a distributed data strategy favors the data mart.
Data Lake: The Unstructured Data Reservoir
Data Lake: It is a large storage repository that holds a vast amount of raw data in its native format until needed. Data lakes are usually built on shared file systems such as Hadoop. Cloud-based data lakes use file systems such as AWS s3 storage.
Data Mart vs. Data Lake: Contrasting with the structured and processed nature of data marts, data lakes store data without predefined schemas in a shared file system in raw form. Data lakes are more suitable for deep analytical tasks and predictive modeling, often using historical and current data to identify data trends. Data marts and data warehouses are built on database technology.
Data lakes contain mainly unstructured data files such as audio, video, and text files.
Some data marts and data warehouses can be extended to connect to external data.
Data lakes and data warehouses can be integrated into a data lakehouse which is an emerging trend. In this case, the data warehouse contains links to the co-resident data lake data.
OLAP and Data Mart: Multidimensional Analysis
OLAP (Online Analytical Processing): OLAP is a technology that allows for complex multidimensional analysis. OLAP cubes allow users to analyze data from multiple dimensions.
OLAP cubes are typically constructed overnight with aggregated and summarized data.
Data Mart vs. OLAP: While some data marts incorporate OLAP for multidimensional data structuring, they are not synonymous. The OLAP cube aggregates data to speed up query times.
An Operational Data Store (ODS) and its Relation to a Data Mart
Operational Data Store (ODS): An ODS is a real-time or near-real-time database that integrates data from multiple databases containing transactional data for current reporting.
An ODS is considered to be an operational support system rather than a decision support tool, as is the case for data marts, data warehouses, and data marts.
Data Mart vs. ODS: An ODS is designed for short-term, operational reporting, usually with a limited scope.
Data marts often contain historical data for in-depth analysis, providing insights over longer periods.
Benefits of Data Marts in Business
Data marts bring numerous advantages to business operations. First and foremost, they enable faster and more efficient access to data than a data warehouse.
The enterprise data warehouse can be complex and expensive to maintain. Other data mart benefits include the ability to delegate administration of independent data marts to the department that understands their data the most.
This specificity not only speeds up data retrieval but also enhances the quality of BI and analytics.
Another significant benefit of all types of data marts is their role in reducing data redundancy. By focusing on specific data subsets, they avoid the pitfalls of storing duplicate information that often plagues larger data warehouses.
A data mart can contain historical data that is specific to a particular business area.
Key Considerations in Data Mart Implementation
As with a data warehouse, implementing a data mart requires careful planning and consideration of many factors. The choice between dependent, independent, or hybrid data marts is key.
Integration with existing data warehouses is another important consideration. For dependent and hybrid data marts, ensuring seamless connectivity and data flow from the centralized data warehouse is essential for maintaining data consistency and accuracy.
Businesses also need to consider the technology stack used for the data mart. This includes selecting the right database management systems, ETL tools, and BI solutions that align with the specific requirements of the data mart.
Steps to implement a data mart
Implementing a data mart involves a series of steps. Here is a general outline of the steps involved in data mart implementation:
Requirement Analysis
Understand the specific needs of the business or department that the data mart will serve.
Identify the key stakeholders and their data requirements, including the types of data needed and how it will be used.
Consider if the data marts will duplicate or augment the data in an existing data warehouse. In a data fabric architecture, the enterprise data warehouse becomes distributed as independent data marts that are peers in the data architecture. A dependent data mart duplicates data. An independent data mart provides greater autonomy to the delegated data owner.
Data Source Identification
Determine where the data will come from, whether it’s from an existing data warehouse, data lake, various databases, or external sources.
Assess the quality, availability, and format of the data.
Designing the Data Mart
Choose the appropriate structure for the data mart (e.g., star schema, snowflake schema).
Design the data model, considering how data will be organized, stored, and accessed.
Extraction, Transformation, and Loading (ETL)
Develop processes to extract data from the identified sources such as a data warehouse or data lake.
Transform the data into a suitable format, including cleaning and consolidating data.
Load the transformed data into the independent or dependent data mart.
Building the Database
Set up the database that will house the data mart, configuring storage, indexing, and other database parameters.
Implement the designed schema within the database.
Implementation of BI Tools
Integrate BI tools that access data for reporting and data analysis.
Set up dashboards, reports, and analytics tools as required by the stakeholders.
Testing and Validation
Conduct thorough testing to ensure data accuracy and the proper functioning of the data mart.
Validate the data mart with end-users to ensure it meets their requirements.
Deployment and Rollout
Deploy the data mart into a production environment.
Train end-users on how to use the data mart and associated tools.
Maintenance and Updates
Regularly maintain the data mart to ensure it continues to meet user needs.
Update the data mart as business requirements change or as new data becomes available.
Performance Monitoring and Optimization
Monitor the performance of the data mart and make adjustments as needed.
Ensure that the data mart remains relevant and can deliver data efficiently and effectively over time.
Data Mart and Business Intelligence
Data marts play an important role in providing BI capabilities. By providing targeted data specific to a business unit, they allow for more relevant and focused analytics.
This leads to more informed decision-making, as data analysts and business users access data to gain meaningful insights and trends specific to their departmental needs.
Data marts can be optimized for various BI applications, such as reporting, data visualization, and predictive analytics.
Challenges and Solutions in Data Mart Deployment
While data marts offer significant benefits, they also present certain challenges. One of the main issues is the risk of creating data silos, where individual data marts operate in isolation without adequate integration with other data systems leading to a fragmented view of the organization’s data.
The Future of Data Marts and Cloud Integration
The future of data marts and data warehouses is closely tied to the evolution of cloud technology. Cloud-based data marts offer scalability, flexibility, and cost-effectiveness, making them an attractive option for businesses looking to modernize their data infrastructure.
Cloud architecture also facilitates easier integration of multiple data marts and other data sources, paving the way for more a cohesive and comprehensive data strategy. Cloud data warehouses and data marts lower management costs and offer instant capacity when needed.
As cloud technology continues to advance, we can expect to see more innovative uses of data marts in leveraging data analytics, business intelligence, predictive analytics, AI, and machine learning.
Conclusion
Data marts are essential components of modern data management strategies. They offer focused, efficient, and accessible data solutions that provide improved BI and decision-making.
As businesses continue to evolve the role of data marts will undoubtedly continue to play a role in providing more advanced analytics.
