In today’s rapidly changing corporate environment, organizations are turning to cloud-based technologies for convenient data collection, reporting, and analysis. This is where Data Warehousing comes in as a core component of business intelligence that enables businesses to enhance their performance. It is important to understand what is data warehouse and why it is evolving in the global marketplace.

In this article, we’ll provide an overview of Data Warehouse – explore key concepts like data warehouse architecture, characteristics of data warehouse, what is data management, the benefits of data warehouse, and data warehouse applications in Data Science.  

What Is a Data Warehouse

Data warehouses serve as a central repository for storing and analyzing information to make better informed decisions. An organization's data warehouse receives data from a variety of sources, typically on a regular basis, including transactional systems, relational databases, and other sources.

A data warehouse is a centralized storage system that allows for the storing, analyzing, and interpreting of data in order to facilitate better decision-making. Transactional systems, relational databases, and other sources provide data into data warehouses on a regular basis.

A data warehouse is a type of data management system that facilitates and supports business intelligence (BI) activities, specifically analysis. Data warehouses are primarily designed to facilitate searches and analyses and usually contain large amounts of historical data.

A data warehouse can be defined as a collection of organizational data and information extracted from operational sources and external data sources. The data is periodically pulled from various internal applications like sales, marketing, and finance; customer-interface applications; as well as external partner systems. This data is then made available for decision-makers to access and analyze. So what is data warehouse? For a start, it is a comprehensive repository of current and historical information that is designed to enhance an organization’s performance. 

Key Characteristics of Data Warehouse

The main characteristics of a data warehouse are as follows:

  • Subject-Oriented

A data warehouse is subject-oriented since it provides topic-wise information rather than the overall processes of a business. Such subjects may be sales, promotion, inventory, etc. For example, if you want to analyze your company’s sales data, you need to build a data warehouse that concentrates on sales. Such a warehouse would provide valuable information like ‘who was your best customer last year?’ or ‘who is likely to be your best customer in the coming year?’

  • Integrated

A data warehouse is developed by integrating data from varied sources into a consistent format. The data must be stored in the warehouse in a consistent and universally acceptable manner in terms of naming, format, and coding. This facilitates effective data analysis

  • Non-Volatile

Data once entered into a data warehouse must remain unchanged. All data is read-only. Previous data is not erased when current data is entered. This helps you to analyze what has happened and when. 

  • Time-Variant

The data stored in a data warehouse is documented with an element of time, either explicitly or implicitly. An example of time variance in Data Warehouse is exhibited in the Primary Key, which must have an element of time like the day, week, or month.

Database vs. Data Warehouse

Although a data warehouse and a traditional database share some similarities, they need not be the same idea. The main difference is that in a database, data is collected for multiple transactional purposes. However, in a data warehouse, data is collected on an extensive scale to perform analytics. Databases provide real-time data, while warehouses store data to be accessed for big analytical queries. 

Data warehouse is an example of an OLAP system or an online database query answering system. OLTP is an online database modifying system, for example, ATM. Learn more about the OLTP vs. OLAP differences.

Data Warehouse Architecture

Usually, data warehouse architecture comprises a three-tier structure.

Bottom Tier

The bottom tier or data warehouse server usually represents a relational database system. Back-end tools are used to cleanse, transform and feed data into this layer. 

Middle Tier

The middle tier represents an OLAP server that can be implemented in two ways. 

The ROLAP or Relational OLAP model is an extended relational database management system that maps multidimensional data process to standard relational process. 

The MOLAP or multidimensional OLAP directly acts on multidimensional data and operations.

Top Tier

This is the front-end client interface that gets data out from the data warehouse. It holds various tools like query tools, analysis tools, reporting tools, and data mining tools

How Data Warehouse Works

Data Warehousing integrates data and information collected from various sources into one comprehensive database. For example, a data warehouse might combine customer information from an organization’s point-of-sale systems, its mailing lists, website, and comment cards. It might also incorporate confidential information about employees, salary information, etc. Businesses use such components of data warehouse to analyze customers. 

Data mining is one of the features of a data warehouse that involves looking for meaningful data patterns in vast volumes of data and devising innovative strategies for increased sales and profits.  

Types of Data Warehouse

There are three main types of data warehouse.

Enterprise Data Warehouse (EDW)

This type of warehouse serves as a key or central database that facilitates decision-support services throughout the enterprise. The advantage to this type of warehouse is that it provides access to cross-organizational information, offers a unified approach to data representation, and allows running complex queries. 

Operational Data Store (ODS)

This type of data warehouse refreshes in real-time. It is often preferred for routine activities like storing employee records. It is required when data warehouse systems do not support reporting needs of the business. 

Data Mart

A data mart is a subset of a data warehouse built to maintain a particular department, region, or business unit. Every department of a business has a central repository or data mart to store data. The data from the data mart is stored in the ODS periodically. The ODS then sends the data to the EDW, where it is stored and used.   

Data Warehouse Example

Let us look at some examples of how companies use data warehouse as an integral part of their day-to-day operations.

Investment and Insurance companies use data warehouses to primarily analyze customer and market trends and allied data patterns. In sub-sectors like Forex and stock markets, data warehouse plays a significant role because a single point difference can result in huge losses across the board.  

Retail chains use data warehouses for marketing and distribution, so they can track items, examine pricing policies and analyze buying trends of customers. They use data warehouse models for business intelligence and forecasting needs. 

Healthcare companies, on the other hand, use data warehouse concepts to generate treatment reports, share data with insurance companies and in research and medical units. Healthcare systems depend heavily upon enterprise data warehouses because they need the latest, updated treatment information to save lives.   

Data Warehousing Tools 

Wondering what Data warehouse tools is? Well, these are software components used to perform several operations on an extensive data set. These tools help to collect, read, write and transfer data from various sources. What do data warehouses support? They are designed to support operations like data sorting, filtering, merging, etc. 

Data warehouse applications can be categorized as:

  • Query and reporting tools
  • Application Development tools
  • Data mining tools
  • OLAP tools

Some popular data warehouse tools are Xplenty, Amazon Redshift, Teradata, Oracle 12c, Informatica, IBM Infosphere, Cloudera, and Panoply. 

Benefits of Data Warehouse

Wondering why businesses need data warehousing? Well, there are several benefits of data warehouse for end users.

  • Improved data consistency
  • Better business decisions
  • Easier access to enterprise data for end-users
  • Better documentation of data
  • Reduced computer costs and higher productivity
  • Enabling end-users to ask ad-hoc queries or reports without deterring the performance of operational systems
  • Collection of related data from various sources into a place

Companies having dedicated Data Warehouse teams emerge ahead of others in key areas of product development, pricing, marketing, production time, historical analysis, forecasting, and customer satisfaction. Though data warehouses can be slightly expensive, they pay in the long run. 

Build Your Career in Data Warehousing 

If you are looking to work as a Business Intelligence (BI) professional or learn data warehousing, you have many exciting career options available. Data architects, database administrators, coders, and analysts are some of the most sought-after BI professionals. Prepare yourself for a job interview with our data warehouse interview questions, and enroll in our top courses for a brighter future in data science.

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With data sources growing larger, businesses of the future need to devise better data insights and data analysis. Prepare for the future with Data Science Courses offered by a leading eLearning institute like Simplilearn and position yourself as an asset for top organizations. 

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