Overview
Data warehouse vs data mart are two different topics as data mart is a subset of the data warehouse. It divides them into small units which are called data marts. So, in this blog, we will understand what is data warehouse whole is different from other terms like database, data mart, and data lake.
What Is Database
Database is referred to the collection and organization of data and information so that data can be easily accessed, retrieve, edit, and managed. It is created in such a way that one set of software programs can be accused by multiple users at the same time.
Database management and handling are the primary objectives of many industries. As these industries’ websites work on handling.
Like, For dynamic websites of industries hotels, train, airplane bookings or hospitals, etc have to handle the database worldwide to check the availability.
The concept of the database has existed since the 1960s, the only difference which came across is the meaning and the understanding of what is a database.
- From a File-Based database in 1698, which is maintained in a flat file.
- Hierarchical Database from 1968-1980. It was called an information management system, in which files are related in a parent/child manner.
- A network data model is called Integrated Data Store. It was standardized in 1971. According to this model, files are related as owners and members
- A cloud-based database is accessed over the internet and it is used to store, manage, and retrieve structured, and unstructured data via a cloud platform. Because it provides offered as a managed service so it is called a database as a service (DBaaS).
What Is Data Warehouse
So, to understand data warehouse vs data mart let’s understand them separately. A data warehouse is a huge collection of data in the business to make appropriate decisions.
Earlier in the 1980s, the transitions of data were becoming operational work. So, a data warehouse supports the transition of data and decision-making of the business.
What Is Data Mart
Now, after understanding what is a data warehouse, let’s understand the second part of data warehouse vs data mart.
A data mart is referred to as a subset of the data warehouse. Whereas it only focuses on one functional area of an organization.
For example one data mart focus on one department- marketing, human resource, finance, etc.
It is often controlled by one department, as the source of data is very less than data wherehouse. Plus data marts have small sizes and are flexible.
Since each department does not require to access every data from the warehouse. So, the organization creates the data marts close to departments, which not only reduces response time and also reduces the load on the data warehouse.
Moreover, it protects the organization’s data. Nowadays the concept of data marts has proven an emerging enterprise network technology.
Types of Data Mart
So to understand data warehouse vs data marts let’s understand the different types of data marts and their purpose. Here are the different types of data mart:
Dependent Data Mart
In the case of a dependent data mart, it depends on one single data warehouse for the source of data. As all the data from different sources are gathered, cleaned, and stored in a centralized location. Afterward, different data marts are created. But the source of data will always be in the data warehouse.
So, in this case, users can excess to both data mart ad data warehouse or they are restricted to data mart only.
Independent Data Mart
An Independent data mart is created without the central data warehouse. As the data is sourced directly from operational and external sources.
An independent data mart has neither a relationship with the enterprise data warehouse nor with any other data mart. In an Independent data mart, the data is input separately, and its analyses are also performed autonomously.
Hybrid Data Mart
A hybrid data mart has a combined source for input data which are data warehouse and different internal and external data sources. Mostly, in the case of ad-hoc integration, like after a new group or product is added to the organization.
When there are multiple database environments and a need for fast implementation in an organization.
Also, these types of data marts are flexible, support large storage and require the least amount of cleaning.
What Is Data Lake
A data lake can be used at any scale to store data at one centralized repository(a central location in which data is stored and managed).
It allows you to store structured and unstructured data at any scale. Plus it allows you to store your data without constructing any structure and to run different analyses, reports charts, etc.
Moreover, it also allows the user to machine learning and real-time data movement.
As the data lake is an extension of a data warehouse, so it’s important to understand the concept to understand data warehouse vs data mart. Here are the different elements of a data lake:
Data movement
Data movement allows you to import any amount of data in real-time. Plus it can allow you to restore the data in its original format and also it can be resizable according to requirements.
Secure Store
A data lake not only provides real-time data movement, but also provides the storage of data of database crawling, cataloging, and indexing of data. Finally, data must be secured to ensure your data assets are protected.
Analytics
Data Lakes allow various roles in your organization like data scientists, data developers, and business analysts to access data with their choice of analytic tools and frameworks.
Machine Learning
Data Lakes will allow organizations to generate different types of insights including reporting on historical data and doing machine learning. Where models are built to forecast likely outcomes and suggest a range of prescribed actions to achieve the optimal result.
Data Warehouse VS Database
So, a Data warehouse vs database are two different things but it often becomes confusing. Before we understand the data warehouse vs data mart So, here is the difference between a data warehouse and database
| Bases | Database | Data Warehouse |
| Purpose | The database is mainly used for recording. | A data warehouse is mainly used to analyze |
| Processing Method | It uses Online Transactional Processing (OLTP) | It uses Online Analytical Processing (OLAP). |
| Usage | It performs the operational work of a business. | It helps in analyzing the business. |
| Orientation Towards | Application | Subject |
| Storage Limit | Single application in most cases | Countless application |
| Availability | Real-Time | Refreshed from sources system when needed |
| Data Type | Data stored in the database is up to date | Current and Historical Data is stored in Data Warehouse. It May not be up to date |
| Data Summary | Detailed Data is stored in a database. | It stores highly summarized data. |
Data Warehouse VS Data Mart
So, now let’s understand the difference between data warehouse vs data marts. How they are different in their meaning purpose and their functionality. Here is the difference between a data warehouse and data mart:
| Parameter | Data Warehouse | Data Mart |
| Definition | A Data Warehouse is a large repository of data collected from different organizations or departments within a corporation. | A data mart is an only subtype of a Data Warehouse. It is designed to meet the need of a certain user group. |
| Usage | It helps to take a strategic decision. | It helps to take tactical decisions for the business. |
| Objective | The main objective of a Data Warehouse is to provide an integrated environment and a coherent picture of the business at a point in time. | A data mart is mostly used in a business division at the department level. |
| Designing | The designing process of a Data Warehouse is quite difficult. | The designing process of Data Mart is easy. |
| Data Handling | Data warehousing includes a large area of the corporation which is why it takes a long time to process it. | Data marts are easy to use, design and implement as they can only handle small amounts of data. |
| Focus | Data warehousing is broadly focused on all departments. It is possible that it can even represent the entire company. | Data Mart is subject-oriented, and it is used at a department level. |
| Data type | The data stored inside the Data Warehouse are always detailed when compared with the May data mart. | Data Marts are built for particular user groups. Therefore, data is short and limited. |
| Subject-area | The main objective of a Data Warehouse is to provide an integrated environment and a coherent picture of the business at a point in time. | Mostly hold only one subject area- for example, Sales figures. |
| Data storing | Designed to store enterprise-wide decision data, not just marketing data. | Dimensional modeling and star schema design are employed for optimizing the performance of the access layer. |
| Data type | Time variance and non-volatile design are strictly enforced. | Mostly includes consolidation data structures to meet the subject area’s query and reporting needs. |
| Data value | Read-Only from the end-user standpoint. | Transaction data regardless of grain-fed directly from the Data Warehouse. |
| Scope | Data warehousing is more helpful as it can bring information from any department. | Datamart contains data, of a specific department of a company. There are maybe separate data marts for sales, finance, marketing, etc. Has limited usage |
| Source | In Data Warehouse Data comes from many sources. | In Data Mart data comes from very few sources. |
| Size | The size of the Data Warehouse may range from 100 GB to 1 TB+. | The Size of Data Mart is less than 100 GB. |
| Implementation time | The implementation process of the Data Warehouse can be extended from months to years. | The implementation process of Data Mart is restricted to a few months. |
Data Warehouse VS Data Lake
Here is the difference between the data warehouse and data lake
| Characteristics | Data Warehouse | Data Lake |
| Data | Relational from transactional systems, operational databases, and line of business applications | Non-relational and relational from IoT devices, websites, mobile apps, social media, and corporate applications |
| Schema | Designed before the DW implementation (schema-on-write) | Written at the time of analysis (schema-on-read) |
| Price/Performance | Fastest query results using higher-cost storage | Query results getting faster using low-cost storage |
| Data Quality | Highly curated data that serves as the central version of the truth | Any data that may or may not be curated (ie. raw data) |
| Users | Business analysts | Data scientists, Data developers, and Business analysts (using curated data) |
| Analytics | Batch reporting, BI and visualizations | Machine Learning, Predictive analytics, data discovery, and profiling |
Frequently Asked Questions (FAQs)
What is a difference between a data warehouse vs data mart?
A data warehouse stores data in a structured format. It is a central repository of preprocessed data for analytics and business intelligence. A data mart is a data warehouse that serves the needs of a specific business unit, like a company’s finance, marketing, or sales department.
What is data mart in data warehouse?
A data mart is a simple form of a data warehouse that is focused on a single subject or line of business, such as sales, finance, or marketing. Given their focus, data marts draw data from fewer sources than data warehouses.
What Is data warehouse definition?
A data warehouse is a type of data management system that is designed to enable and support business intelligence (BI) activities, especially analytics.
Is data mart a type of data warehouse?
A data mart is a subset of a data warehouse focused on a particular line of business, department, or subject area.