DATA WAREHOUSE: Definition and How It Works

Data Warehouse
Data channel

We may readily define a “data warehouse” as the secure electronic storing of information by a business or other organization. A data warehouse’s purpose is to build a repository of historical data that can be retrieved and examined to provide helpful insight into the organization’s activities. There is diverse information about a data warehouse and this article will in turn serve as a guide to providing detailed information on what it is all about, including its types, tools involved, and an example to work with. Let’s go into detail. 

What is a Data Warehouse?

Data warehousing, also known as an enterprise data warehouse (EDW), is a system that collects data from several sources into a single, central, consistent data storage to facilitate data analysis, data mining, artificial intelligence (AI), and machine learning. This term enables an organization to execute complex analytics on massive amounts of historical data (petabytes and petabytes) in ways that a regular database cannot.

Data warehousing systems have been a part of business intelligence (BI) solutions for more than three decades, but they have lately developed as new data types and data hosting technologies have emerged. We can also say that data warehousing was traditionally hosted on-premises—often on a mainframe computer—and its functionality centered on obtaining data from various sources, purifying and preparing the data, and loading and maintaining the data in a relational database. Data warehousing may now be housed on a dedicated appliance or in the cloud, and most data warehouses also include analytical capabilities as well as data visualization and presentation tools.

How a Data Warehouse Works

When businesses began to rely on computer systems to create, file, and retrieve critical business documents, the need for data warehousing grew. IBM researchers Barry Devlin and Paul Murphy originated the notion of data storage in 1988.

Data warehousing is intended to allow for the examination of historical data. Also, data collected from numerous heterogeneous sources might provide insight into a company’s performance. Data warehousing is intended to enable users to perform queries and analytics on historical data generated from transactional sources.

The data that is added to the warehouse does not change and cannot be changed. The warehouse is the source from which analytics on prior events are done, with a focus on changes over time. Warehoused data must be stored in a secure, dependable, retrievable, and manageable manner.

Maintaining a Data Warehouse:

To keep this data warehouse running, some measures must be taken. Data extraction is one phase that requires obtaining vast amounts of data from numerous sources. Data cleaning is the process of going through a set of data for errors and fixing or excluding any that are identified after it has been compiled.

The cleaned-up data is subsequently transformed from database format to warehousing format. After being stored in the warehouse, the data is sorted, consolidated, and summarized to make it easier to utilize. As the various data sources are updated, additional data is added to the warehouse over time.

W. H. Inmon’s Creating the Data Warehouse, a practical handbook first published in 1990 and reissued multiple times, is an important book on data warehousing.

Businesses can now invest in cloud-based data warehousing software services from Microsoft, Google, Amazon, and Oracle, among others.

Types of Data Warehouse

There are three main types of Data Warehouse (DWH), which are as follows:

#1. Enterprise Data Warehouse (EDW)

A centralized warehouse is an enterprise data warehouse (EDW). It offers decision support services throughout the organization. Also, it provides a uniform approach to data organization and representation. It also allows you to categorize data by subject and grant access based on those classifications.

#2. Operational Data Store

When neither a data warehouse nor an OLTP system can meet an organization’s reporting needs, an operational data store, or ODS, is required. Data warehousing in ODS is refreshed in real-time. As a result, it is extensively used for mundane tasks such as keeping employee details.

#3. The Data Mart

A data mart is a subdivision of data warehousing. It is specifically developed for a specific business line, such as sales, finance, or sales. Data can be collected directly from sources in an independent data mart.

What are the 5 Components of Data Warehouse?

There are five major Data Warehousing Components:

#1. Warehouse database

The warehouse manager is in charge of operations related to data management in the warehouse. It performs tasks such as data analysis to verify consistency, index and view building, denormalization and aggregate generation, source data transformation and merging, and data archiving and backup.

#2. Sourcing, Acquisition, Clean-up, and Transformation Tools (ETL)

The data source, transformation, and migration technologies are used in data warehousing to accomplish all conversions, summarizations, and changes required to transform data into a single format. Extract, Transform, and Load (ETL) Tools are another name for them.

Their capabilities include:

  • Anonymize data as per regulatory stipulations.
  • Eliminating unwanted data in operational databases from loading into Data warehouse.
  • Search and replace common names and definitions for data arriving from different sources.
  • Calculating summaries and derived data
  • In case of missing data, populate them with defaults.
  • De-duplicated repeated data arriving from multiple datasources.

These Extract, Transform, and Load tools may generate cron tasks, background jobs, COBOL programs, shell scripts, and so on that update data in the data warehouse system on a regular basis. These tools are also useful for Metadata maintenance.

These ETL Tools must cope with database and data heterogeneity concerns.

#3. Metadata

The term “meta data” conjures up images of high-level technological data warehousing concepts. It is, however, pretty straightforward. Metadata is information about data that defines the data warehousing system. It is used to construct, maintain, and manage data warehousing.

Meta-data is vital in the data warehousing architecture because it identifies the source, usage, values, and attributes of the data warehousing data. It also specifies how data is altered and handled. It is tightly linked to the data warehousing system.

For example, a line in the sales database may contain:

4030 KJ732 299.90

This is a meaningless data until we consult the Meta that tells us it was

  • Model number: 4030
  • Sales Agent ID: KJ732
  • Total sales amount of $299.90

As a result, Meta Data are critical components in the transformation of data into knowledge.

The following questions can be answered with metadata:

  • What tables, characteristics, and keys are there in the Data Warehouse?
  • Where did the information come from?
  • How frequently is data reloaded?
  • What cleansing transformations were used?

Metadata can be divided into the following categories:

  • Technical Meta Data: This type of Metadata comprises warehouse information that is used by data warehousing designers and administrators.
  • Business Meta Data: This type of Metadata contains detail that allows end-users to easily interpret the information housed in the data warehousing system.

#4. Query Tools

One of the key goals of data warehousing is to provide organizations with information to help them make strategic decisions. Users can interact with the data warehouse system via query tools. Backend components are another name for query managers. It handles all processes connected to the administration of user requests. The operations of the data warehousing component are to direct queries to the proper tables for query scheduling.

#5. Data warehouse Bus Architecture

The flow of data in your warehouse is determined by the Data Warehouse Bus. In data warehousing system, data flow is classified as Inflow, Upflow, Downflow, Outflow, and Meta flow.

When creating a Data Bus, keep in mind the shared dimensions and facts across data marts.

Data Marts:

A data mart is an access layer that is used to distribute data to users. It is promoted as a viable choice for large-scale data warehouses because it requires less time and money to construct. Yet, there is no universal definition of a data mart, and it varies from person to person.

In a nutshell, a data mart is a division of a data warehouse. The data mart is utilized for data partitioning that is developed for a certain group of consumers.

Data Warehouse Example

To get a good example of this data warehouse, consider a fitness equipment manufacturer. Its best-selling product is a stationary bicycle, and the company is thinking of extending its portfolio and launching a new marketing campaign to support it.

It uses its data warehousing process to better understand its current customers. It can determine whether its consumers are mostly women over the age of 50 or guys under the age of 35. Also, it may help you learn more about the shops that have had the greatest success selling their bikes, as well as where they are located. It may be able to examine internal survey findings and learn what former customers liked and disliked about their items.

All of this information assists the corporation in deciding what type of new model bicycles to create and how to promote and advertise them. It’s based on hard data rather than gut instinct. With this data warehouse example, I believe the process will now be easily understandable.

Data Warehouse Tools

There are numerous data warehouse tools on the market, but the most popular types include:

#1. MarkLogic

MarkLogic is one of the most popular types of data warehouse tools and also a good example of a valuable data warehousing solution that uses a variety of enterprise capabilities to make data integration easier and faster. This tool aids in the execution of extremely complex search operations in a data warehouse. It may query several sorts of data, such as documents, relationships, and metadata.

#2. Oracle

Oracle is the industry’s most popular database. It provides a diverse range of data warehousing solutions for both on-premises and cloud deployments. Also, it contributes to better client experiences by enhancing operational efficiency. It also comes in as one of the popular types of data warehouse tools to give a trial.

#3. Amazon RedShift

Amazon Redshift is a data warehousing application. It is a straightforward and low-cost tool for analyzing various forms of data using conventional SQL and existing BI tools. It also enables the execution of complicated queries on petabytes of structured data via the query optimization technique.

What is a Data Warehouse vs Database?

A data warehouse differs from a database in the following ways:

  • A database is a transactional system that analyzes and updates real-time data to ensure that only the most up-to-date information is available.
  • A data warehouse is designed to collect structured data over time.

A database, for example, may just include the most current address of a client, whereas a data warehouse may store all of the customer’s addresses for the previous ten years.

What are the Four Stages of Data Warehousing?

Before, firms began with pretty simple data warehousing applications. Yet, more complex data warehousing applications emerged over time.

The following are the general types of stages in a data warehouse (DWH) use:

#1. Offline Operational Database

At this point, data is simply copied from one operating system to another. Loading, processing, and reporting of copied data have no effect on the operational system’s performance.

#2. Offline Data Warehouse

The Datawarehouse receives regular updates from the Operational Database. Datawarehouse data is mapped and changed to fulfill Datawarehouse objectives.

#3. Real time Data Warehouse

Datawarehouses are updated at this step whenever a transaction occurs in the operational database, for example, an airline or train reservation system.

#4. Integrated Data Warehouse

DataWarehouses are regularly updated at this level when the operating system makes a transaction. After that, the Datawarehouse generates transactions, which are subsequently given back to the operational system.

What are the Characteristics of Data Warehouse?

Subject-oriented, time-variant, integrated, and non-volatile are the four types or example of data warehouse characteristics, commonly known as data warehousing features.

What are the Seven 7 Functions of Warehousing?

  • Storage
  • Protection of Goods
  • Transport of Goods
  • Financing
  • Services with a monetary value
  • Stabilization of Prices
  • Management of Information

What are the Two Types of Warehousing?

Public and private warehouses are the two main types of warehouses.

What is the Purpose of Data Warehouse?

Data warehousing is the centralized collection of data that can be studied to make better decisions. Data flows into a data warehouse on a regular basis from transactional systems, relational databases, and other sources.

What are the 4 Basic Functions in a Warehouse?

Whatever the product, every warehouse moves it, stores it, keeps track of it, and sends it out. Storage, material handling, packing and shipping, and barcode equipment are the four key categories of equipment that come from these four activities.

What are the three 3 Process used in a Data Warehouse?

The process of Flow in the datawarehouse includes the following steps:

  • The data must be extracted and loaded.
  • Data cleaning and transformation.
  • Data should be backed up and archived.

In conclusion

Data warehousing is the collection of information about a company’s business and how it has performed over time. It is the source of analysis that discloses the company’s past achievements and failures and guides decision-making. It was created with input from employees in each of its core departments.


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