In today’s rapidly changing data world, organizations are moving and looking cloud-based solutions to conveniently collection of data, their reporting, and analysis. This is where the Data Warehouse comes in as a core component for their business intelligence and decision making system. It’s critical to know about data warehouses and the reasons behind their evolution in the global economy.

In this article, you will learn concepts of Data Warehouse and explore data warehouse architecture, characteristics, what is data management, the benefits of data warehouse, and data warehouse applications in various fields.  

Table of Content

Definition

A data warehouse is a type of data management system that facilitates and supports business intelligence (BI) activities, specifically analysis and making an informed decision. It is a sizable set of business data that is utilized to support an organization’s decision-making.

Introduction

Bill Inmon coined the phrase “data warehouse” for the first time in 1990. He defines a data warehouse as an integrated, subject-oriented, time-variant, and non-volatile data gathering. This information aids analysts in making sensible decisions within a company.

A operational database experiences daily frequent modifications due to the various transactions that occur within it. If any senior level executive wishes to review past comments on any type of data for say—product, supplier, or customer—no data will be available for him to review because the data has been updated based on recent transactions.

On the other hand, a data warehouse offers us multidimensional views of consolidated and generalized data. A data warehouse not only offers us a consolidated and generalized view of the data, but also capabilities for online analytical processing, or OLAP. We may analyze data in a multidimensional space more effectively and interactively with the use of these technologies. Data mining and data generalization are the outcomes of this approach.

The following characteristics can be applied to a data warehouse:

• It is a research-purpose database that aggregates data from multiple apps.
• It facilitates comparatively few users having mostly lengthy conversations.
• Data warehouse provides a historical perspective on information by combining historical and present data.
• It is heavily used for reading.
• Data warehouse has several large tables.

Key Features

The key features of a data warehouse are

• Subject Oriented − A data warehouse is subject oriented because it provides information around a subject or a particular topic rather than the organization’s ongoing operations. These subjects can be promotions, inventory, customers, suppliers, sales, revenue, etc. For example, if you want to analyze your company’s revenue data, you need to build a data warehouse that concentrates on revenue. Such a warehouse would provide valuable information like ‘which field was your best last year?’ or ‘which is likely to be your best field in the coming year?’
• Integrated − Building a data warehouse involves combining data into a standardized format from a variety of heterogeneous sources, including relational databases and flat files. The nomenclature, format, and coding of the data must be maintained in the warehouse in a way that is uniform and widely accepted. The efficient analysis of data is improved if followed properly.
• Time Variant − A specific time period is associated with the data gathered in a data warehouse. From a historical perspective, the information found in a data warehouse is useful. Time is either explicitly or implicitly noted in the data that is kept in a data warehouse. In data warehouses the Primary Key must have a time component such as the day, week, or month as an element of it.
• Non-volatile − When something is non-volatile, new data is added without erasing the existing data. It’s all read-only data. When new data is entered, the old data is not deleted. This aids in the analysis of what has done and when. Because the operational database and the data warehouse must be maintained separately, frequent changes made to the operational database do not appear in the data warehouse.

Data Warehouse Architecture

Three Tier Architecture of Data Warehouse

A data warehouse architecture generally consists of three-tier that are mentioned below

• Bottom Tier − The bottom tier is the data warehouse server and it is a relational database system. Back end tools and utilities are used to load data into this tier. These back end tools and utilities are generally perform Extract, Clean/Transform, Load, and refresh functions
• Middle Tier − The OLAP Server, which is located in the middle layer, can be used in the following ways:
  • via Relational OLAP (ROLAP) which is a relational database management system that has been expanded. It helps standard relational operations are to be mapped to multidimensional data operations.
  • Through the Multidimensional OLAP (MOLAP) model, multidimensional data and processes are implemented directly
• Top-Tier − This tier is the front-end user/client facing layer. It is responsible for getting data out from the data warehouse. This layer consists of the query tools, reporting tools, analysis tools and data mining tools

Types of Data Warehouse

There exist three primary categories of data warehouses.

Enterprise Data Warehouse (EDW)

An enterprise data warehouse (EDW) is a database, or group of databases, that centralizes data from many applications and sources for usage by the entire company for analytical purposes. EDWs can be stored in the cloud or on-site(locally) on servers.

This type of warehouse acts as an enterprise-wide key or central database to assist decision-support services. This kind of warehouse has the benefit of allowing complicated queries to be executed, offering a consistent approach to data representation, and facilitating access to information across organizational boundaries.

• An enterprise warehouse compiles all of the data and informations related to a whole company.
• It offers data integration across the entire organization.
• Data from operating systems and outside information sources are combined.
• The amount of data may range from a few gigabytes to terabytes, hundreds of gigabytes, or more.

Operational Data Store (ODS)

A central database known as an operational data store (ODS) offers a snapshot of the most recent information from several transactional systems for operational reporting. In order to make data available for business reporting, it helps businesses to bring together original data from multiple sources in one location.

Real-time updates are made to this kind of data warehouse. It is frequently chosen for regular tasks like keeping personnel files. It becomes necessary when data warehouse solutions are unable to meet the business’s reporting requirements.

Benefits of ODS

The advantages of an ODS mostly relate to business operations since it offers up-to-date, clean data from several sources in one location.

• The ODS offers a unified repository that legacy, fragmented, or ineffectively communicating IT systems can feed into.
• ODS reporting can be more sophisticated than reports from separate underlying systems because it is concentrated on a snapshot of operational data. Reports can offer a comprehensive view of operational processes because the ODS is designed to give a consolidated view of data combined from many systems.
• As data in ODS is up-to-date, before delving into component systems, users may diagnose issues more quickly because of current picture of operational status. An ODS, for instance, makes it possible for customer care agents to quickly locate an order, as well as any relevant troubleshooting information.
• An ODS contains important, time-sensitive business procedures, such alerting a financial alert automatically when a customer overdraws an account. Without up-to-date and linked operational data, these rules alone would not be able to significantly increase efficiency. Taken together, they represent a form of process automation.

Operational Data Stores Vs. Data Warehouses

An ODS is designed to serve completely different functions compared to a data warehouse. Here are some differences between them:

• Positioned between the data sources and the data warehouse, an ODS can serve as a temporary home for a data warehouse.
• A data warehouse is made to execute complicated queries on massive amounts of data, whereas an ODS is made to do simple queries on small amount of data.
• Because an ODS constantly overwrites data, it only deals with current operational data and basic status-level reporting. A data warehouse can combine data from several historical views and continuously add entries to already-existing tables.

Data Mart

A data mart is a subset of a data warehouse designed to support a certain division, area, or business unit. A business’s departments each have a central repository, or data mart, where they store data. A defined set of users can swiftly obtain crucial insights by using data marts to make specific data available to them, saving time and avoiding the need to search through a whole data warehouse. For instance, a lot of businesses might have a data mart that corresponds with the finance, sales, or marketing departments.

Put otherwise, we can assert that data marts include information unique to a certain group. For instance, information on products, clients, and sales may be included in the marketing data mart. Data marts are confined to subjects.

Points to remember about data marts −

• Data marts are implemented on servers running Unix/Linux or Windows. They are put into use on inexpensive servers.
• Weeks, as opposed to months or years, are used to create the data mart cycles.
• In the long run, a data mart’s life cycle could be complicated if its planning and design are not organization-wide.
• Data marts are generally small and compact
• Data marts are generally customized by subjects, groups or departments
• The source of a data mart are particular subjects, groups or departments
• Data mart posses flexibility

Benefits of Data Marts

Data marts are made with a relatively limited scope of data in order to cater to the demands of particular groups. A data mart’s goal is to give business users the most pertinent information quickly, even if it can still hold millions of records.

A data mart offers the end user a number of advantages with its more compact, targeted design, such as the following:

• Cost-efficiency: When establishing a data mart, there are numerous aspects to take into account, including the scope, integrations, and the extraction, transformation, and loading (ETL) procedure. But generally speaking, a data mart only costs a small portion of what a data warehouse does.
• Simplified data access: Because data marts only store a small portion of the total amount of data, users can get the information they require more quickly and with less effort than they could if they were working with a data warehouse’s larger data set.
• Faster access to insights: Enterprise-level strategic decision-making, which affects the entire business, is facilitated by intuition obtained from a data warehouse. Departmental decision-making is facilitated by business information and analytics powered by a data mart. Teams can employ targeted data insights to further their own objectives. Teams that find and extract useful data faster experience expedited business processes and increased productivity, which benefits the organization.
• Less complicated data maintenance: A data warehouse can accommodate several company lines and contains a multitude of business information. Data marts concentrate on a single line and hold less than 100GB, which reduces clutter and simplifies maintenance.
• Simple and quicker implementation: Because a data warehouse gathers information from numerous internal and external sources, it takes a long time to install, particularly in a large organization. However, because a data mart only requires a limited portion of data, deployment is typically more effective and requires less setup time.

Types of Data Marts

Based on how they relate to the data warehouse and the many data sources of each system, there are three different kinds of data marts.

• Dependent data marts: Segments of an enterprise data warehouse that are divided are called dependent data marts. Starting with the central storage of all company data, this top-down method is implemented. Every time a certain subset of the primary data is needed for analysis, the recently constructed data marts extract it.
• Independent data marts: Without the assistance of a data warehouse, independent data marts works as stand-alone systems. Data about a certain topic or business process can be extracted by analysts from internal or external data sources, processed, and then stored in a data mart repository until the team needs it.
• Hybrid data marts:  Data from operational sources and current data warehouses are combined in hybrid data marts. This integrated strategy delivers the enterprise-level integration of the independent technique along with the speed and user-friendly interface of a top-down strategy.

Data Warehouse: Tools & Processing

There are several tools are available in the markets that can be utilised for creating a data warehouse. These tools are categorized as per their applications:

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

The three main categories of data warehouse applications that are listed below are data processing, data mining, and analytical processing.

• Data Processing − It helps information contained in a data warehouse to be processed. Processing methods for the data include basic statistical analysis, querying, and reporting with crosstabs, tables, charts, and graphs.
• Analytical Processing − It helps in information present in a data warehouse to be processed analytically. Basic OLAP operations, such as slice-and-dice, drill down, drill up, and pivoting, can be used to evaluate the data.
• Data Mining − By identifying hidden patterns and relationships, building analytical models, classifying information, and making predictions, data mining aids in the discovery of new information. The visualization tools can be used to present these mining results.

Difference between OLAP and OLTP

The main differences between OLAP and OLTP systems that you should be aware of are outlined below.

Data Warehouse: Benefits

A copy of the data from the original transaction systems is kept in a data warehouse. This intricacy of the architecture offers the chance to:

• Recognize market patterns to improve your forecasts.
• In data warehouses, complicated queries that would be difficult to construct and manage in numerous normalized databases may be simpler.
• Data warehouses’ structures are easier for end users to explore, comprehend, and query.
• Unify data in a single database and data model by integrating data from several sources. More data gathering into a single database to enable data presentation using a single query engine
• Reduce the issue of database isolation level lock contention in transaction processing systems that arises when extensive, time-consuming analysis queries are attempted to be executed in transaction processing databases.
• Save the history of your data even if the original transaction systems don’t
• Integrate data from several source systems to provide a centralized picture for the entire organization. This advantage is always beneficial, but it’s especially so now that the company has expanded through mergers.
• Boost the quality of the data by using consistent classifications and descriptions, identifying or even correcting inaccurate data.
• Consistently display the organization’s data
• For all relevant data, regardless of the source, provide a single standard data model.
• Reorganize the information so that business users can understand it.
• Reorganize the data to provide superior query performance—even for intricate analytical queries—while avoiding any negative effects on the operating systems.
• Enhance the usefulness of running corporate programs, particularly CRM systems.
• Make inquiries to assist in decision-making. more straightforward to write
• Sort and categorize repeated data

Summary

• A data warehouse is a kind of information system that stores data from one or more sources, both historical and current. Traditional data warehouses, cloud data warehouses, and virtual data warehouses are some examples of these sources.
• Because it provides information on subjects rather than an organization’s ongoing operations, a data warehouse is subject-oriented.
• Integration in a data warehouse refers to creating a single unit of measurement for all comparable data from various sources.
• Additionally, data warehouses are non-volatile, meaning that when new data is added, the old data is kept intact.
• Because the data in a DW has a long shelf life, a data warehouse is time-variant.
• The Data Warehouse Architecture is composed of five primary components: 1)Database 2)ETL Tools 3)Metadata 4)Utilizing Query Tools 5)Data Marts
• These are the four primary query tool categories. 1. Tools for reporting and querying 2. Tools for Application Development; 3. Tools for Data Mining 4. Tools for OLAP
• All of the conversions and summaries are carried out using the data sourcing, transformation, and migration tools.
• Meta-data is crucial to the Data Warehouse Architecture because it describes the characteristics, values, and sources of data warehouse data.

Learn more about databases

• What is Data Warehouse by Google
• What is Data Warehouse by Oracle
• AWS Redshift Vs Snowflake: How To Choose?
• NoSQL Vs SQL Databases: An Ultimate Guide To Choose
• Cloud Load Balancing: How To Choose?
• Cloud-Based Data Architecture: How To Integrate With Business
• Oracle 19C Database