Data Warehouse, or more popularly known as business intelligence solution is a mickle set of information conditioned to business, analyzed, and assessed by businesses to make strategic and efficient decisions. This huge amount of data reaches data warehouse through different means of data-generating activities like which can be the same as varied in nature, for instance, user’s purchasing behavior data from website and mobile applications like Amazon and Flipkart.

Technology has made the data warehouse more efficient as the data of millions of users can be accessed only in a click. Every day when a new user signs up or log in to Amazon to buy a new iPhone, or updates his social media accounts with a new job, or adds a new song to his Spotify playlist, or subscribes to Netflix, he ends up generating thousands of bytes of data unconsciously that the considered business uses to understand the behavior of the user. This data helps the businesses to curate their platform or services as per the need or interest of the user.

Take the example of Amazon – the biggest e-commerce website in the world. When the user searches for a product for the first time, for instance, a mobile phone, on Amazon website or application the system ends up creating a record of the search pattern. Next time, when the same user will log in again or search for the mobile phone, the system will show him the devices with which he interacted the most in the last search. This is possible because of the data collected. Similarly, Netflix will suggest a movie or show of the same genre that you have seen just now.

• Paul Murphy and Barry Devlin, from IBM, are accredited for introducing the business data warehouse at the end of the 80s.
• Data from multiple sources can be stored at one point called a data warehouse.
• Quick business decisions can be taken in no time if the required data is available and organized in the data warehouse.
• Quick access to data and efficient analysis of data helps to increase the productivity of the business.
• Millions are people who are contributing to the data warehouse at any given point of time.

Data Warehouse Architecture

Data Warehouse Architecture is a complex process that showcases the overall blueprint or the roadmap of the Data Warehouse which is used for communicating the previous data of the users to the clients for their utilization. Generally, the three known virtual architecture for the data warehouse is basic – where data from routine activities of the organization, users of the organization’s services and other processes such as mining and analysis is stores in the form of summary and metadata; with staging area – where data received from operational system is mined, analyzed, simplified and organized before storing it as summary or metadata.

Staging area along with data marts are included in the third architecture. Datamart as the name implies is the area where the raw data and metadata are organized as whether it is purchased, on sale, or stored as inventory. The three different types of data warehouse architecture are 1) Single-tier architecture, 2) Two-tier architecture, and 3) Three-tier architecture which is composed of the bottom, middle, and top tier. Three-tier architecture is the most commonly used architecture.

• Although single-tier architecture reduces the amount of data, it is not in use anymore because of the technical snags it shows.
• The two-tier architecture is not flexible and supports only a limited number of users which has made businesses abandon this design.
• The bottom tier of the three-tier architecture is actually the database of the data warehouse.
• The database is mediated to the intended recipient of the database through the middle tier.
• To extract the desired data out from the data warehouse, it is put together with the top-tier.

Data Warehouse Concepts

To understand the concepts of Data warehouse, it is important to look at the major things on which the whole warehousing process relies on. The atmospheric crux of data warehousing comprises of an ETL (which is an acronym for extraction, transformation, and loading) of data and other tools and techniques to manage the data and to make it business-ready.

While they may appear similar at first, the data warehouses can be easily distinguished from online transaction processing (OLTP) which usually includes the operational data, unlike the data warehouse which completely relies on historical data only. Due to the large quantity of data from the data warehouse, processing takes much more time if compared to OLTP.

Different concepts of Data Warehousing are listed below:

• Dimension Data Model: For data warehouse process dimension data model is used frequently because of its ease of operation and storage.
• Conceptual Data Model: Data entered into the warehouse has to go through this model to recognize the top-ranked bond between the entities.
• Data Integrity: To check the integrity of the data, one has to check that data is consistent and has no redundancy as well. 
• Fact Table: The fact table, as the name reads, contains only facts that help to define the relationship among the different dimensions on the table.
• OLAP: OLAP is the acronym for On-Line Analytical Processing which is an analytical tool to identify the reason for the increased sales or similar questions.
• MOLAP, ROLAP, and HOLAP: MOLAP or Multidimensional On-Line Analytical Processing stores data in a multidimensional cube. ROLAP or Relational On-Line Analytical Processing stores the data in the relational database and tamper the data. HOLAP or Hybrid On-Line Analytical Processing is the combination of MOLAP and ROLAP.

What is a Data Warehouse?

Data Warehouse is an advanced software technology that assembles the data in a coherent manner from multiple users and other data-driven wellsprings to analyze it for business-related purposes. Data warehouses provide intelligence information about the different behavior of the people that are generated in the form of data like-profile data, purchasing data, marketing data, stock data, consuming data, etcetera.

For the usual analysis in the business, it is important to have the relevant data received from the users or potential users of that business. However, in this analysis transaction data is excluded to focus majorly on the transaction only. This regular analysis of data helps businesses to make better decisions that can benefit both the users and the business.

Data Warehouse Definition

Data Warehouse is a collection of data, in the language of computing technology, that uses the data for rigorous analysis that helps to extract the business intelligence information and supports the decision-making process of the business. Data warehouses are the storage units for data where raw data and metadata can be stored in the form of a summary or any other orderly structure. This data is collected from multiple sources and stored all together without any overlapping among them.

• Data Warehouse helps to maintain a data repository.
• It maintains the historical data that can be used in the future.
• It arranges the data in a manner that can be easily understood by the users.
• Data warehouse supports the decision-making process of the business by providing easy access to the data.

Data warehouse vs data mining

Data mining and data warehouse are two different terms which are somehow interrelated to each other, however, still differ from each other in a major view. Data mining can be defined as a set of procedures where raw data from the available large pool of the data is used to extract the desired data that can be used to develop effective strategies to flourish the business. A huge amount of unstructured data is available in the world that can be used by businesses for their benefit. The results of data mining help to identify the patterns and correlations between the data used for mining.

• Data mining speeds up the process of decision-making.
• The patterns and correlations in the dataset are identified using data mining tools.
• Businesses using data mining for decision making have a competitive advantage over their competition.
• Data mining tools can be used by anyone with a short training period.

Data warehouse interview questions

With the rise of data, the everyday number of jobs in this sector is also rising. Businesses are in dire need of skilled workers who can use the never-ending data to help the organization make better decisions. Computing technology is attracting more and more people towards it because of its lucrative nature and handsome salary package. However, organizations go only select excellent candidates that are selected after passing through a rigorous selection process. Some of the questions asked in the Data Warehouse interview are listed below:

• What is Data Warehousing?
• What are the stages of Data Warehouse?
• What is OLTP?
• Define OLAP, MOLAP, ROLAP, and HOLAP?
• What is the difference between OLTP and OLAP?
• What are fact tables?
• What is Metadata?
• Name the different ETL tools?
• What are the different types of data warehouse architecture?
• What is data mining?
• How is data mining different from data warehousing?

Data Warehouse Components

Though we know that a data warehouse is a software-based system, it is not an unknown fact that a hardware device is necessary to run any software. Thus, we can say that the data warehouse has two types of components – software components and hardware components. A data warehouse is built according to the requirement of the business and it can be improved by including an extra unit of the desired component. These components may vary as per the demand and needs of the organization. Different components of the data warehouse are listed below:

• Source data component: Data received from multiple sources is categorized into Production data, Internal data, Archived data, and External data.
• Data staging component: Data from various sources undergoes three main functions – data extraction, data transformation, and data loading.
• Data storage component: Data is structured at a quick pace and efficient manner and stored in data repositories. 
• Information delivery component: This component delivers the information from the stored unit to a specified location.
• Metadata component: Data about the large set of data is stored in the metadata component

Data warehouse characteristics

The data warehouse is subject-oriented. If you want to analyze which product of your organization is doing well in the market, you can analyze the revenue and profit generated from that product. The data warehouse is integrated. Data is made integrated by removing redundancy and inconsistency in the data. Data is not subjected to any change once it reaches the data warehouse and hence, the data warehouse is said to be non-volatile. A data warehouse is Time-variant. Data stored in the data warehouse is maintained on a defined time interval either weekly, monthly, or annually.

• Data is simplified and redundancy is eliminated to improve performance.
• A huge quantity of data collected from a previous search is used.
• Frequently asked questions on the internet generate a huge quantity of data.
• The efficient data warehouse has a fast performance irrespective of its architecture.

Data warehouse vs. database

For routine life solutions, data is arranged into small sets to analyze and assess it. These solutions are used by organizations to help in the growth of the business and to make the services more friendly to the user. Whereas, a data warehouse is a place where historical data which is to be directed to other places is gathered from multiple sources.

Data Warehouse Schema

Schemas are solely developed to counter the fresh and different requirements of the large databases designed for On-Line Analytical Processing. In laymen terms, it can be defined as the logical presentation of the complete database. Be it a database or data warehouse, both the system requires to have a well-designed schema for the efficient and easy understanding of the data in the database. A schema contains all the records of the database including name and other descriptions. Though schema may appear as metadata, it is completely different.

• The four different types of schemas are Star Schema, Snow Flake Schema, Galaxy Schema, Fact Constellation Schema.
• Star Schema is multidimensional in nature and each dimension has a dedicated primary key.
• Star Schema is not associated with any parent table.
• Snow Flake Schema may have one or more than one parent table to normalize the complete structure.

Data warehouse examples

As the demand for data is rising, the tools to analyze this data are also increasing in the market. However, not all of them are as efficient and well-performing. But there are some tools that are efficient for storing data, analyzing, and interpreting. No matter how many new designs come out of the world of technology, the data warehouse will remain the most timely and efficient tool. Some of the popularly known data warehouse tools are listed below:

• Oracle: One of the popular names in the database market, oracle’s data warehouse system is highly efficient at its job.
• Amazon Web Services: Their service is extremely handy and popular and its popularity can be realized by the fact that Netflix, the biggest streaming platform, uses AWS for its functions.
• Cloudera: Cloudera has come out as the most famous warehouse tool in the last few years.
• Teradata: Talk about the name of warehouse service in the market, and every tech-savvy person will give you the same answer – Teradata.
• MarkLogic: Established in 2001, MarkLogic has built a respectable name for itself in the warehousing services.

Data warehouse vs. data mart

A data warehouse is an organized collection of data in the form of a summary, raw data, and metadata received form operational support. This data from the data warehouse provides important business intelligence. On the flip side, unlike the data warehouse, data mart collects the data only from a limited number of sources which include data from the internal operating system. The primary focus of data mart remains on one topic only and does not deviate from it.

• Datamart is one of the major components of a data warehouse.
• Datamart uses only a limited chunk of data.
• Due to the use of a small portion of data, this system is comparatively faster than the data warehouse.
• Datamart may collect data either from a data warehouse or from external data sources.

Types of data warehouse

Out of the many types of data warehouses, the three main types of data warehouses are, 1) Enterprise Data Warehouse, 2) Operational Data Store, and 3) Data Mart. An Enterprise data warehouse necessitates the decision-making system for the business. Operational data stores, commonly known as ODS, are the simple data storing unit. When data warehouse and OLTP fail to fulfill the requirement of the business they opt for an operational data store. Datamart is nothing but a small set of data from the data warehouse.

• An Enterprise data warehouse shows the consolidated representation of the data.
• For routine operational activities, businesses use an operational data store.
• The operational data store is preferred by organizations because of its real-time approach.
• To extract results from the finance department, or sales department data mart is used.

Applications of Data Warehouse

The efficient and fast approach of the data warehouses has made it very popular. Applications like analysis, sorting, interpreting allows a number of industries to use a data warehouse as their primary tool since industries produce data every minute and that too in large amount. Some of the popular industries that use data warehouses in their day to day functions are banking and insurance, hospitality and healthcare, manufacturing and distribution, retailer services, telephonic transportation, and government and education are some of them.

• Banking Industry uses a data warehouse because it can be used for banking research, employees’ performance analysis, and to develop new programs.
• To maintain tax records, the impact of new policies, the effect of on-going policies is analyzed by the government sector.
• Education Institutes use data warehouses to analyze the demographics of their students, faculties, and workers.
• The Healthcare sector uses data warehouses mainly for the marketing campaign and to advertise their brand.

Data warehouse need

In a blunt statement, the only need for a data warehouse is to make better decisions. But as we learn more, there are many sharp reasons why we need a data warehouse. Every business wants to flourish and grow. Every business wants a turnover of crores. Every business wants to remain in the game forever. But how is this possible? YES, better decision. Each better decision contributes to the success of the organization. Some other reasons that why we need a data warehouse are listed below.

• A data warehouse is needed to keep the historical record from the source system
• Reporting and analysis of particular information is can be done by a data warehouse.
• Data warehouse helps to create the metadata which gives the user information about the source data in the data warehouse.
• If needed, data mining can be done on the data warehouse to make a strategic decision

Data Warehouse Features

A data warehouse can be relational or multidimensional in nature or it can be designed either way depending upon the need of the person who is going to use this consolidated process for the benefit of its business. Data warehouse derives the data from multiple sources and thus increasing the quantity of data in the store. However, nowadays it is presumed for any business to have its own data warehouse to control and manage generation of data every day. Some of the key features of a data warehouse are:

• It stores a large amount of data that is collected from multiple source systems.
• Data warehouse uses On-Line Analytical Processing.
• It is a multidimensional analytical and reporting system.
• Data is stored in a simplified manner and in the form of a summary.

A data warehouse in Data Mining

Data mining is the process of analyzing large chunk of data from a set of provided data to identify any patterns or trends that can help the business to predict the probability of occurrence of any particular event and make a decision accordingly. It is important to understand that data warehouse and data mining are two different things. In simple terms, data mining is done on the data that is collected and stored in the data warehouses. Since data mining gives the prediction from the data, it is also known as KDD (Knowledge Discovery in Data).

• Data warehousing includes the collection and recording of data, whereas data mining analyses the data to predict the trend.
• Data warehousing handled by engineers but analysis of data is performed by data scientists.
• Data warehousing aims at providing the relevant data to the organization but data mining aims at providing future insight.
• Data mining is extensively used in marketing campaigns, elections campaigns, etcetera.

FAQs

Q. What is a data warehouse with an example?

A data warehouse is a complicated system that assembles information from multiple source systems and records them in an ordered structure and stored in the form of a summary. This data helps the business to make decisions that can help the organization to become a successful enterprise in the world of business. An example is data collected from the search history of people on YouTube. This data helps YouTube to serve users with better and relevant content. 

Q. Where are data warehouses located?

A data warehouse is a complex system that collects the data from millions of source devices and systems and stores them as a summary or metadata in the raw form. A data warehouse does not have any physical location as it is a system-based software that businesses use to make decisions for the successful venture. However, if a virtual location is to be mentioned then a data warehouse is located in the devices of your office.

Q. Which database is best for the data warehouse?

Although a number of the database system are available in the computing technology market, the most reliable and leader of this market is Teradata which is providing these services for the past 30 years and have been praised by multiple businesses for its efficiency and accuracy. Other data warehouses that are as good as Teradata are Amazon Web Services, Oracle, Cloudera, and MarkLogic. All of these are available in the market and are the most trusted tools.

Q. What is a data warehouse and its types?

A data warehouse is a system that records, analyses, and interprets the data collected from various other source systems. This data is stored in the raw form in the data warehouses which can be later used for data mining. Three main types of data warehouses are Enterprise Data Warehouse, Operational Data Store (also known as ODS), and Data Mart.

Q. Why is the data warehouse needed?

A data warehouse is a system that gathers the data from multiple source systems and records it in the system as metadata or summary. This raw data is mined using data mining and the result is used to interpret and forecast future trends. The data stored in the data warehouse is known as historical data and used by an organisation in weekly, monthly, annually, or in any desired time interval. This data helps the business owners, managers, and leaders to take decisions in the business’s favor.