Setting Up a Data Mart: 5 Easy Steps

Today’s market is dominated by big data and analytics, hence, Data Mart plays a significant role in turning data into insights. Data warehouses are well-known for storing huge volumes of complex data. 

During data analytics, individuals normally want to access data quickly and with a lot of ease. This is impossible with a data warehouse, which requires one to create complex queries just to access data for generating simple reports. This explains the reason why smart companies prefer storage and access to data using this method. Instead of storing data for an entire company, a Data Mart stores a subset of data that serves the needs of a particular department. 

This makes it easier for the staff in that department to gain actionable insights from data. In this article, you will learn about Data Mart in-depth and the steps for how to build one.

 

What is a Data Mart?

A Data Mart is a smaller version of a data warehouse, and it is meant to be used by a particular department or a group of individuals in the company. 

It focuses on a single functional unit of an organization and keeps a subset of data stored in the data warehouse. It is normally controlled by a unit department in the organization. 

Since these storages are smaller in size, they are easy to create, and maintain, and are more flexible. Its objective is to provide the business user with the most relevant data in the shortest time possible. This prevents the user from having to wait for longer periods of time for queries to complete. They also facilitate the summarization of data.

Why Create a Data Mart?

• Easier Access: A Data Mart provides quick and easy access to data for specific teams.
• Targeted Data: It helps teams like marketing access relevant data for specific purposes, such as improving campaign performance.
• Reduced Complexity: A Data Mart eliminates the need to gather data from multiple systems.
• Avoids Spreadsheet Dependency: Without a Data Mart, teams often rely on spreadsheets, which can lead to confusion, errors, and inconsistencies.
• Centralized Data Source: It serves as a central location where data is organized, ensuring consistency before generating dashboards, reports, and visualizations.

What are the Types of Data Marts?

There are 3 types of Data Marts which vary depending on their relation to the data warehouse and the data sources used to build them. 

They include the following:

• Dependent
• Independent
• Hybrid

1) Dependent

These are the types of Data Marts that are created from an existing company data warehouse. It’s a top-down approach that starts with storing all the company data in a single central location, then extracting a portion of data when needed for analysis. 

To create a dependent Data Mart, a particular set of data is aggregated from the data warehouse, restructured, and loaded into the mart where users can query it. 

A dependent Data Mart can be a logical view or a physical view of a data warehouse:

• Logical view: This is a virtual table or view that is logically, rather than physically, separated from the warehouse. 
• Physical subset: This is a data extract stored in a database that is physically separated from the data warehouse. 

2) Independent

An independent Data Mart is created without the use of a data warehouse, meaning that it’s a standalone system that focuses on one business function or subject area. 

The data is extracted from internal/external data sources, processed, and loaded to the data repository where it is stored for analytics. They are easy to design and develop and they help organizations to achieve their short-term goals. 

3) Hybrid

These are the storage structures that combine data from a data warehouse and other source systems. A hybrid Data Mart combines the speed and end-user focus of the top-down approach with the advantages of the organization-level integration of a bottom-up approach. 

Data Mart vs Database vs  Data Warehouse vs Data Lake

Features	Data Mart	Database	Data Warehouse	Data Lake
Purpose	Focuses on data needed by a specific team or department for analysis and reporting.	General data storage for organizing, searching, and retrieving information.	Centralized storage for all business data, supporting company-wide reporting and analysis	Stores large amounts of raw, unstructured data for deeper analysis.
Data Scope	Contains specific data for one area of the business.	Stores data from different sources for various uses.	Covers data from the entire organization, including all departments.	Holds raw data (like text, images, or videos) ready .
Data Structure	Holds raw data (like text, images, or videos) ready.	Structured into tables, often relational (rows and columns).	Well-structured and organized data.	Unprocessed, raw data that hasn’t been organized yet.

Read more about Data Mart vs Data Warehouse

What is the Structure of Stored Data?

Data Marts normally store transactional data in rows and columns, making it easy to access, organize, and understand data. Since they store historical data, they make it easy for data analysts to understand data trends. 

Companies normally organize these in a multidimensional schema as a blueprint for addressing the needs of individuals who use databases to perform analytical tasks. 

They use the following 3 types of schema:

• Star Schema
• Snowflake Schema
• Vault Schema

Star Schema

The Star Schema is a logical collection of tables in a multidimensional database that looks like a Star shape. 

One fact table is created in the middle and surrounded by many associated dimension tables. The dimension tables don’t depend on each other, hence, this schema requires a fewer number of joins when running queries. This makes querying data easier, which makes the Star Schema good for analysts who need to access large data sets. 

Snowflake Schema

The Snowflake Schema is a logical extension of the Star Schema since it builds out the blueprint with additional dimensional tables. 

These dimension tables are taken through the process of normalization to minimize data redundancy and ensure there is data integrity. This schema requires little space to store the dimensional tables, but its complex structure can be difficult to maintain.

Vault Schema

This database modeling technique makes it possible for IT professionals to come up with agile enterprise data warehouses. 

It uses a layered structure and it was developed to handle issues with flexibility, agility, and scalability that are associated with other schema models. It makes it possible to add new data sources without disrupting the existing schema.

What are the Advantages of a Data Mart?

A few advantages of the Data Mart are listed below:

• Data Marts are efficient, and cost-effective solutions as they take are cheaper to deploy than Data Warehouses and data access.
• Hybrid Data Marts can improve performance by taking a Data Warehouse processing. When dependent data marts are placed in a separate processing facility, they significantly reduce analytics processing costs as well.
• Data Marts can be independent of each other, so any error in the central Data Warehouse doesn’t affect individual Data Marts.

What are the Disadvantages of a Data Mart?

A few disadvantages of the Data Mart are listed below:

• Limited View of Data: Since data marts only store specific data for one department or purpose, you can’t get a full picture of the company’s data.
• Hard to Keep Updated: Without good tools to move and update data, it’s tough to keep the data in a mart fresh and accurate.
• Too Many Data Marts: If each department has its own data mart, you might end up with a lot of separate systems that are difficult to manage.
• Trouble with Reporting Across Data Marts: Independent data marts make it hard to create reports that combine data from different areas of the business.
• Setup Can Be Tricky: Building data marts can be complicated, especially if the data isn’t aligned correctly, which can lead to reporting mistakes.
• Not Always the Best Option: Data marts aren’t the right solution for every situation, so it’s important to figure out if they’re the right fit for your needs.

What are the Data Mart Use Cases?

Here are a few pivotal use cases where Data Marts can come in handy:

• Improved Resource Management: You can provide each department with a separate repository to manage the imbalance of resource use by various organizational units. For instance, if the department running logistics operations performs a lot of actions with a database on a daily basis, then this might cause system malfunctions in other departments that carry out fewer database queries. Eventually, this might end up reducing the performance effectiveness of the entire company. These repositories allow you to use resources more effectively and efficiently.
• Subject-focused Data Analytics: Data Analytics plays a pivotal role in any business lifecycle. These repositories allow for more focused data analysis since they only contain records that are organized around particular subjects like sales, products, customers, etc. Since there is no extraneous information to deal with, businesses can filter more accurate and clearer insights.
• Selective Data Access: You can leverage these repositories in situations when an organization needs selective privileges for managing and accessing data. Generally, this can be the case for big enterprises that can’t reveal the entire Data Warehouse to all the users. By building multiple dependent repositories, you can help protect sensitive data from accidental writes and unauthorized access.
• Time-limited Data Projects: As opposed to corporate data warehouses that need considerable effort and time, these are much easier and faster to set up. Since, data developers and engineers work with smaller amounts of data, simpler schemas, and fewer sources, this comes in handy. Apart from this, these repositories are also easier to implement compared to a Data Warehouse. So, if you are facing any time crunches in terms of completing a data project, these repositories may be the way to go.

Procedure for Implementation

The process of building a Data Mart can be complex, but it generally involves the following 5 easy steps:

• Step 1: Design
• Step 2: Build / Construct
• Step 3: Populate / Data Transfer
• Step 4: Data Access
• Step 5: Manage

Step 1: Design

This is the first step when building a Data Mart. 

It includes tasks such as initiating a request for the Data Mart and collecting information about the requirements. Other tasks involved in this step include identifying the data sources and selecting the right data subset. 

The output of this step is the logical and physical design of the Data Mart. 

Step 2: Build / Construct

This is the step during which both the physical and the logical structures for the Data Mart are created.

In this step, you create the tables, indexes, fields, and access controls. 

Step 3: Populate / Data Transfer

This is the step in which you populate the Data Mart by transferring data into it. You can also set the frequency with which data transfer will be done, whether daily or weekly. 

To ensure that information stored in the structure is clean, it is always overwritten during the population of the Data Mart. In this step, the source information is extracted, cleaned, transformed, and loaded into the Data Mart. 

Step 4: Data Access

In this step, the data that has been loaded into the Data Mart is put into active use. Activities involved here include querying, generating graphs and reports, and publishing. 

To make it easy for non-technical users to use the Data Mart, a meta-layer should be set up and item names and database structures translated into corporate expressions. 

If possible, interfaces and APIs should be set up to ease the process of data access. 

Step 5: Manage

This is the last step when building a Data Mart and it involves the following tasks:

• Controlling user access.
• Refining and optimizing the target system to improve its performance. 
• Adding new data into the Data Mart and managing it. 
• Configuring recovery settings and ensuring that the system is available even after the occurrence of disasters. 

What are the Best Practices for Implementing Data Marts?

Here are some of the best practices for implementing Data Marts:

• It is important to involve all the stakeholders in the designing and planning stage since the Data Mart implementation can be a little complicated.
• The source needs to be departmentally structured for peak efficiency.
• This data repository may be in a different location, compared to the Data Warehouse. That’s why it is important to make sure that they have enough networking capacity to tackle the data volumes needed to transfer data to the repository.
• Implementation cost should budget the time it takes for the loading process to be completed. Load time increases with an increase in the complexity of the transformations.
• Even if the data repository is created on the same hardware, they might need some different software to handle user queries. Disk storage and additional processing power should be evaluated for a faster user response.
• The implementation cycle of such a data repository needs to be measured in short periods of time, i.e., in weeks as opposed to months or years.
• Data Mart software/hardware, implementation, and networking costs need to be accurately budgeted within your plan.

What is the Future of Data Marts is in the Cloud?

Even with the improved efficiency and flexibility that are offered by these data repositories, Big Data and big business are still becoming too much to handle for various on-premise solutions. As Data Lakes and Data Warehouses move to the cloud, so do these repositories.

With a shared cloud-based platform to house and generate data, analytics and access become much more efficient. You can generate transient data clusters for short-term analysis, or long-lived clusters can come together for more sustained work. Modern technologies are also separating data storage from computing, allowing for ultimate scalability to query data.

Other advantages of cloud-based hybrid and dependent Data Marts include:

• Resources are consumed on demand.
• Flexible architecture with cloud-native applications.
• Increased efficiency.
• Single depository containing all the Data Marts.
• Real-time, interactive analytics.
• Consolidation of resources that lowers the costs.
• Immediate real-time access to information.

Conclusion

This article explained that a Data Mart stores a subset of data warehouse data and it’s focused on one functional area of an organization. They provide departmental users with a faster way of querying data for analysis. The process of building a Data Mart involves following a set of chronological steps.

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