Data Warehouse VS Data Mart

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 it is different from other terms like database, data mart, data lake.

What Is Database

What is 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 program 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, dynamic websites of industries hotels, train, aeroplane 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.

  1. From a File-Based database in 1698, which is maintained in a flat-file.
  2. Hierarchical Database from 1968-1980. It was called an information management system, in which files are related in a parent/child manner.
  3. 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
  4. A cloud-based database is accessed over the internet and it is used to store, manage, and retrieve their structured, unstructured data via a cloud platform. Because it provides offered as a managed service so it is called database as 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 an operational work. So, data warehouse supports the transition of data and decision making of the business.

A huge amount of data in a data warehouse consists of data from different departments like sales, marketing, finance, and external sources like a May was customer and, shareholders. Mostly it includes the elements of the human resource database.

And, in the case of data warehouse vs data marts, data marts are the subparts of the data wherehouse. As it is the mega data storage centre and data market are small data storage.

Features of Data Warehouse 

To understand the data warehouse vs data mart it’s important to understand how and why the data warehouse is important. So here the data warehouse features and how it is important for an organization.

Features of Data Warehouse
Features of Data Warehouse

Integrated

Data in the data warehouse is constructed from the integration of data from various sources- relational databases, flat files, etc.  As a result, data is well integrated which makes the analysing of data easy. Plus it helps the decision-maker to integrate, read and analyze the data from different sources.

Non-volatile 

Non-volatile means that the data in the data warehouse will not get erased when new data is added to the operational database. As a result, when any changes are made in the operational database the data warehouse remains unchanged.

Subject Oriented  

A data warehouse is called subject-oriented as it shows the data based on the subject, not based on the ongoing operation. For, instance data is assessed as financial data, marketing, HR etc.

Time-Variant

The data warehouse collects the data which is identified based on its period. It represents the information from the historical point of view.

Data Warehouse Architecture

As the data warehouse vs data marts biggest difference is the architecture. Data warehouse architecture is divided into three tire data warehouse architecture. So the three tire includes:

  1. Bottom Tier: The database lies at the bottom tier of data warehouse architecture. As, the data is cleansed, transformed and loaded into a layer using back end tools.
  2. Middle Tier: The middle tier in the Data warehouse is an OLAP server which is implemented using either ROLAP or MOLAP model. For a user, this application tier presents an abstracted view of the database. This layer also acts as a mediator between the end-user and the database.
  3. Top-Tier: The top tier is a front-end client layer. The top tier is the tools and API that you connect and get data out from the data warehouse. It could be Query tools, reporting tools, managed query tools, Analysis tools and Data mining tools.

Here is the diagram of data warehouse architecture:

Data Warehouse Architecture
Data Warehouse Architecture

Now let’s understand each component of data warehouse architecture and its purpose.

Data Warehouse Database

  1. External Sources –
    The external source is a source from where data is collected irrespective of the type of data. Data can be structured, semi-structured and unstructured as well.
  2. Stage Area –
    Since the data, extracted from the external sources do not follow a particular format, so there is a need to validate this data to load into the data warehouse. For this purpose, it is recommended to use the ETL tool.
    • E(Extracted): Data is extracted from an external data source.
    • T(Transform): Data is transformed into the standard format.
    • L(Load): Data is loaded into the data warehouse after transforming it into the standard format.

Metadata

The name Meta Data suggests some high-level technological Data Warehousing Concepts. However, it is quite simple. Metadata is data about data that defines the data warehouse. It is used for building, maintaining and managing the data warehouse.

Metadata helps to answer the following questions

  • What tables, attributes, and keys does the Data Warehouse contain?
  • Where did the data come from?
  • How many times do data get reloaded?
  • What transformations were applied with cleansing?

Metadata can be classified into the following categories:

  1. Technical Meta Data: This kind of Metadata contains information about the warehouse which is used by Data warehouse designers and administrators.
  2. Business Meta Data: This kind of Metadata contains detail that gives end-users a way easy to understand the information stored in the data warehouse

Query Tools

One of the primary purpose different in data warehouse vs data mart is the query solving abilities. As the main purpose of data warehouse architecture is employee interacting with a data warehouse.  So, these tools are divided into four different categories:

  1. Query and reporting tools
  2. Application Development tools
  3. Data mining tools
  4. OLAP tools

Query and reporting tools

Further classification of query and reporting tools are:

Reporting tools

Reporting tools can be further divided into production reporting tools and desktop report writer.

  1. Report writers: This kind of reporting tool are tools designed for end-users for their analysis.
  2. Production reporting: This kind of tools allows organizations to generate regular operational reports. It also supports high volume batch jobs like printing and calculating. Some popular reporting tools are Brio, Business Objects, Oracle, Powersoft, SAS Institute.
Managed query tools

This kind of access tools helps end-users to resolve snags in database and SQL and database structure by inserting meta-layer between users and database.

Application development tools

In some cases, building a warehouse consists data finance custom graphic and analytical tools is not sufficient for an organisation’s analytical needs are not sufficient for the analytical needs of an organization. So, some custom reports are developed using application development tools.

Data mining tools

Data Mining refers to the process to discover new patterns, trends, correlations by mining a large amount of data. However, there are data mining tools which make these process automatically.

OLAP tools

These tools are based on the concepts of a multidimensional database. It allows users to analyse the data using elaborate and complex multidimensional views.

Data Marts

A data mart is an access layer that is used to get data out to the users. It is presented as an option for a large size data warehouse as it takes less time and money to build. However, there is no standard definition of a data mart is differing from person to person.

What Is Data Mart

Now, after understanding what is data warehouse, let’s understand the second part of data warehouse vs data mart. 

A data mart is referred to as the 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 size and flexible.

Since each department does not requires 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 reduce the load on the data warehouse. 

Moreover, it protects the organization data. Nowadays the concept of data marts has proven as emerging enterprise network technology.

Types of Data Mart

So to understand data warehouse vs data marts let understand the different types of data marts and their purpose. Here are the different types of data mart:

Types Of Data Mart
Types Of Data Mart

Dependent Data Mart

In the case of dependent data mart, it depends on one single data warehouse for the source of data. As, all the data from different sources gathered, cleaned and stored in a centralised location. Afterwards, different data marts are created. But the source of data will always be in the data warehouse. 

Dependent Data Mart
Dependent Data Mart

So, in this case, user 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. 

Independent Data Mart
Independent Data Mart

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.

Hybrid Data Mart
Hybrid Data Mart

When there are multiple database environments and a need for fast implementation in an organization. 

Also, these types of data mart are flexible, support large storage and requires the least amount of cleaning.

Steps in Implementing a Datamart

The process of implementing a data mart in an organization involves multiple steps. So, to understand the complete difference between data warehouse vs data mart. Here, are the steps involved in implementing a data mart:

Steps in Implementing Datamart
Steps in Implementing Datamart

Designing

Designing is the very first phase of implementing a data mart. According to this step, an organization needs to write all the important information, equipment and other requirements for designing a data mart.

Tasks that are involved in designing part of a data mart are:

  • Identifying the source of data 
  • Designing the correct data subset
  • Collecting all the business and technical data requirements
  • Design a structure of a datamart.

Constructing

After creating a design for a data mart, an organization needs to construct a physical database. A physical database is referred to data or information which is gathered and an organization is desired to store.

Secondly, the physical database and logical structures also need to be built. According to the construction stage, an organization needs to implement the structure which is designed in an earlier stage.

For example, in an earlier stage, the data is gathered from different sources and in this stage data is implemented in different tables, indexes etc.

Moreover, an organization also need to make sure about storage management, data security, multiple users access, fast data access, and security.

Populating

Afterwards, an organization needs to populate the data mart. So, in this step an organization need to perform the following steps:

  • Sourcing the data
  • Cleaning and transforming data to data mart
  • Source data from to data mapping
  • Loading data in a data mart 
  • Storing data in data marts

To complete the task, extract transform load tools are used. As, these tools data sources, perform source-to-target mapping, extract the data, transform, cleanse it, and load it back into the data mart. 

Note: this step tool also creates some metadata relating to things like where the data came from, how recent it is, what type of changes were made to the data, and what level of summarization was done.

Accessing

Now, in the data mart implementation process, an organization will put the data to use. Like, creating reports, querying data, publishing the reports, analysing etc. So, in this step, the metadata and structures are transformed into business use.

 Secondly and most importantly organization have to set up these business use structures and maintain them.

Managing

And in last, an organization needs to make sure of:

  • Continuous addition of new data in data marts
  • Multiple users access management
  • Data recovery in case of system failures
  • Optimization of the system to improve effectiveness and efficiency in the organization. 

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 structure and unstructured data at any scale. Plus it allows you to store your data without constructing any structure and to run different analysis, reports charts etc.

Moreover, it also allows the user machine learning, 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 it also provides to store data of database crawling, cataloguing, 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, 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 data warehouse and database

Bases DatabaseData Warehouse
Purpose The database is mainly used for recording.A data warehouse is mainly used to analyze
Processing MethodIt uses Online Transactional Processing (OLTP)It uses Online Analytical Processing (OLAP).
Usage It performs the operational work of a business.It helps in analysing the business.
Orientation TowardsApplicationSubject
Storage LimitSingle application in most casesCountless application
AvailabilityReal-TimeRefreshed from sources system when needed
Data TypeData stored in the database is up to dateCurrent and Historical Data is stored in Data Warehouse. It  Maywas not up to date
Data SummaryDetailed Data is stored in a database.It stores highly summarized data.
Data Warehouse VS Database

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 data warehouse and data mart: 

ParameterData WarehouseData Mart
DefinitionA 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.
UsageIt helps to take a strategic decision.It helps to take tactical decisions for the business.
ObjectiveThe main objective of Data Warehouse is to provide an integrated environment and coherent picture of the business at a point in time.A data mart mostly used in a business division at the department level.
DesigningThe designing process of Data Warehouse is quite difficult.The designing process of Data Mart is easy.
Data HandlingData 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.
FocusData warehousing is broadly focused on all the 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 typeThe data stored inside the Data Warehouse are always detailed when compared with the May was data mart.Data Marts are built for particular user groups. Therefore, data short and limited.
Subject-areaThe main objective of Data Warehouse is to provide an integrated environment and coherent picture of the business at a point in time.Mostly hold only one subject area- for example, Sales figure.
Data storingDesigned to store enterprise-wide decision data, not just marketing data.Dimensional modelling and star schema design employed for optimizing the performance of the access layer.
Data typeTime variance and non-volatile design are strictly enforced.Mostly includes consolidation data structures to meet the subject area’s query and reporting needs.
Data valueRead-Only from the end-user standpoint.Transaction data regardless of grain-fed directly from the Data Warehouse.
ScopeData 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
SourceIn Data Warehouse Data comes from many sources.In Data Mart data comes from very few sources.
SizeThe size of the Data Warehouse may range from 100 GB to 1 TB+.The Size of Data Mart is less than 100 GB.
Implementation timeThe implementation process of Data Warehouse can be extended from months to years.The implementation process of Data Mart is restricted to few months.
Data Warehouse VS Data Mart

Data Warehouse VS Data Lake

Here is the difference between the data warehouse and data lake

CharacteristicsData WarehouseData Lake
DataRelational from transactional systems, operational databases, and line of business applicationsNon-relational and relational from IoT devices, web sites, mobile apps, social media, and corporate applications
SchemaDesigned before the DW implementation (schema-on-write)Written at the time of analysis (schema-on-read)
Price/PerformanceFastest query results using higher cost storageQuery results getting faster using low-cost storage
Data QualityHighly curated data that serves as the central version of the truthAny data that may or may not be curated (ie. raw data)
UsersBusiness analystsData scientists, Data developers, and Business analysts (using curated data)
AnalyticsBatch reporting, BI and visualizationsMachine Learning, Predictive analytics, data discovery and profiling
Data Warehouse VS Data Lake

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