Setting Up a Data Mart: 5 Easy Steps

on Data Warehouse, ETL, Tutorials • March 21st, 2022 • Write for Hevo

Data Mart | Hevo Data

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. 

Table of Contents

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. Whereas a data warehouse draws data from many sources, a Data Mart draws data from only a few sources. 

Since these storages are smaller in size, they are easy to create, 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. Data warehouses store a broader range of data, which is not summarized, making it difficult to sort through masses of data and increasing query times. 

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Why Create a Data Mart?

A Data Mart offers easier access to data needed by a particular team or line of business within your organization. For instance, if your marketing team is looking for data to help improve campaign performance during the holiday season, combining and sifting through data across various systems could turn out to be costly in terms of accuracy, time, and money.

Teams that are forced to locate data from multiple sources quite often depend on spreadsheets to migrate this data and collaborate. This could lead to confusion, human errors, complex reconciliations, and multiple sources of truth. Therefore, these tools have become increasingly popular as a centralized place where the required data is organized and collated before dashboards, reports, and visualizations are generated.

How to Move Data Marts to the Cloud?

Business teams are toiling to become more data-driven and agile to guide strategy and improve the day-to-day decision-making, yet they typically struggle to convert the voluminous data into actionable insights. CFOs spend on average 2.24 hours per day looking through spreadsheets. Although business teams might turn to IT for help, IT teams may have a hard time keeping up with business users’ demands for increased access to faster query times, more disparate data sources, and larger volumes of data.

Setting up these data repositories might be a challenge for IT teams already burdened with a heavy workload since they also need to manage those data repositories on an ongoing basis and ensure robust security. Moving these repositories to the cloud helps solve the concerns of both business and IT teams by migrating security and administration tasks to the cloud service provider which reduces the need for manual intervention and also reduces the operational costs.

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:

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 Data Warehouse

Both Data Warehouses and Data Marts are crucial central data repositories, but they serve different needs within an organization.

A Data Warehouse is known as a system that accumulates data from multiple disparate sources into a single, central, consistent data store to support Artificial Intelligence (AI), Data Mining, and Machine Learning. This ultimately leads to the enhancement of Business Intelligence and sophisticated analytics. With the help of this strategic collection process, Data Warehouse solutions can collect data from varied sources to make it available in a single unified format.

A Data Mart is defined as a more focused version of a Data Warehouse. It contains a smaller subset of data integral to and needed by a single team or a select group of users within an organization. It is usually built from an existing Data Warehouse through a complex procedure that leverages various tools and technologies to construct and design a physical database, populate it with data and set up sophisticated management and access protocols.

Despite being a challenging process, it allows an enterprise to discover more focused insights faster than working with a broader Data Warehouse dataset. For instance, marketing teams can benefit from creating a Data Mart from an existing Data Warehouse, since its activities are usually performed in isolation from the rest of the business. Therefore, the team doesn’t need access to the complete enterprise data.

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

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 Data Pipeline 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 the 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 Data Pipeline are listed below:

  • In an independent Data Marts model, the business may not have ready to access data cross-data-mart reporting
  • Deploying Data Marts can sometimes be cumbersome because it is important to align fields. If it is not handled properly, there might be errors in building reports to run across Data Marts.
  • One needs to first identify the company’s requirements. Data Marts are not always the right solution for every group.

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 of 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

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 occurrences 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 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.

What are the Limitations of Data Marts?

The following are the challenges that enterprises encounter when building and using Data Marts:

  • Lack of an effective ETL tool that can connect to the existing data warehouse and other data sources that company users need to extract insights from. 
  • Lack of a data integration tool that can update the data in the mart and ensure that the resulting analytics are up-to-date. 
  • It cannot provide enterprise-wide data analysis since it only stores a subset of the warehouse data. 
  • A company with many departments may end up creating too many unrelated Data Marts, one for each department. These may become a big hurdle to manage. 

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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