The Ultimate Guide Types to Data Warehouse

Data processing schemes have been supporting the growth of Big Data. Their potential to collect ample amounts of data from varying data streams is great, but they require a data warehouse to manage, analyze, and query all the data.

Keep reading to understand data warehouse, its types, benefits, functions, and much more.

What is Data Warehousing?

Data warehousing is a process of collecting, storing, and managing large volumes of structured and unstructured data from various sources to support business decision-making. It involves consolidating data from different sources, transforming it into a common format, and organizing it in a way that enables easy analysis and reporting.

The data warehouse is designed to support the analytical needs of an organization and serves as a central repository of data for reporting and data analysis. It is typically optimized for querying and reporting, rather than for transaction processing, which is the primary function of an operational database.

Data warehousing involves several stages, including data extraction, transformation, and loading (ETL), data modeling, and data analysis. The process requires specialized tools and techniques for managing the data and ensuring its quality and consistency. Other common names of data warehouse systems include:

• Decision support system
• Analytic application
• Management information system
• Executive information system
• Business intelligence system

How does the Data warehouse operate?

A data warehouse operates as a central repository wherein data comes in from one or other data sources. Data arrives at a data warehouse from relational databases and another transactional system. Data is often:

1. Structured
2. Semi-structured
3. Unstructured

The data is organized, migrated, and ingested such that people can access the organized data in a data warehouse using Business Intelligence tools, spreadsheets, and SQL clients. A data warehouse integrates data arriving from multiple sources into one comprehensive database.

By integrating all the data at one location, a company can evaluate its users in-depth. Data warehousing enables data mining which refers to patterns in the data which cause higher sales and profits.

Introduction to Data Warehouse Types

Types of the data warehouse referred to as the implementation of data warehouse in several ways like operational data stores, enterprise data warehouse, and data mart. It enables the data warehouse to be a significant module for business intelligence systems by following the mechanism of processing, handling, and performing functional changes on the data from varying data sources which helps in producing analytical reports and results for crucial decision-making standards crucial for the business experts.

1. Enterprise data warehouse: It is a type of data warehouse that brings together varied functional aspects of a company and consolidates them in a unified manner. It is a centralized location where all companies’ data from different applications and sources are made available. Once the information is stored it can be applied for analytics and be used by all departments in a company. The data is categorized depending on the subject and offer access according to the division. This type of data warehouse already follows the process of extraction and transformation.

The objective of an enterprise data warehouse is to offer an entire overview of any specific object in the data model. It can be achieved by wrangling and identifying the data from various systems. Then, it is loaded into a conformed and consistent model. Data collected by this type of data warehouse can provide access to a single location where various tools can be utilized to perform analytical functions and make various predictions. The research team can discover new patterns or trends and emphasize them to grow the business effectively.

Data marts help to segregate data easily. Correlation between entities can be enforced and built as a function of loading data into the enterprise data warehouse. It helps to perform the dicing and slicing of codes based on several categories. This further reduces costly downtime that occurs due to error-probe configurations with machine learning and adaptive approaches. It structures data that helps in functioning on a comparatively small scale, organization, and structure it. The data is stored logically and consistently.

2. Operational data store: An operational data store is used as an alternative to having an operational decision support system application. It accesses data directly from the database that supports the process of a transaction. Data available in the operational data store can be scrubbed and the redundancy can be verified and resolved by verifying the corresponding company guidelines. It further supports consolidating contrasting data from several sources such that business analysis, operations, and reporting can be conducted easily and support the company while continuing the process.

In an operational data store, most of the operations being performed currently are stored before they shift to a data warehouse for a long. It supports small amounts of data and simple queries. As it stores the recent data, it acts as a short-term or temporary memory. In comparison, data warehouse stores data for a long time and relatively permanent information.

It helps to store transactional data from one or more production systems and loosely consolidates it. Often, it is time-variant and subject-oriented. Integration occurs by using enterprise data warehouse contents and structures. The process involves cleansing, checking business rules for integrity, and resolving redundancy. It is commonly developed to include low-level atomic data which stores limited data.

3. Data mart: It emphasizes storing data for a specific function and it includes a subset of data that is stored in a data warehouse. It helps in improving user responses and decreases the volume of data for data analysis. It is easy to go ahead with the research. A data mart is much easier to use as it is a subset of the data warehouse. Moreover, it is cost-effective against a complete data warehouse. It is a single subject matter expert and is open to change as it can define its configuration and structure. The data is categorized, and it can be easily managed. Data mart has three types which are:

• Dependent data mart: By fetching data from external, operational, or both sources a dependent cart can be formed. It enables the sourcing company’s data from a single data warehouse. All data is centralized and help in creating more data marts.
• Independent data mart: It does not require a central data warehouse. This is commonly generated for smaller groups that are available in a company. It does not have any link with an enterprise data warehouse or any other type of data warehouse. All data can be used separately as it is independent. Moreover, the analysis can be conducted autonomously. To have a centralized and consistent store of data is very significant such that several users can use it.
• Hybrid data mart: It is used when inputs from multiple sources are a section of a data warehouse. It is helpful when a consumer which an ad-hoc integration. Whenever a company needs several database surroundings and fast implementation then this setup can be utilized. It needs the least data cleansing effort and the data mart enable large storage structures. A data mart is best used when smaller data-centric applications are being utilized.

Data Warehouse Architecture

You can design a data warehouse system in three ways. These strategies are categorized by the number of tiers in the architecture. The three significant types of data warehouse architecture are:

1. Single-tier architecture: This type of architecture is not a frequently implemented approach. The objective of implementing this architecture is to remove redundancy by reducing the amount of data stored.

The main disadvantage of this approach is that it does not have an element that segregates transactional and analytical processing.

2. Two-tier architecture: This architecture involves a staging area for all data sources before the layer of the data warehouse. In a staging area such as the ETL tool between the storage repository and the sources, you can check all data loaded into the warehouse is cleaned and in the most suitable format.

A two-tier architecture has a few network limitations such as it cannot expand to support a larger number of users.

3. Three-tier architecture: It is the most broadly used architecture for data warehouse systems. It comprises three tiers:

• The bottom tier: It comprises a data warehouse server commonly a relational database system that gathers, transforms, and cleanses data from several data sources via a process called ETL or ELT.

ETL expands as an extract, transform, and load.

ELT expands as extract, load, and transform.

• The middle tier: It consists of an Online Analytical Processing (OLAP) server that allows fast query speeds. ROLAP, MOLAP, and HOLAP are the three common types of OLAP models under this tier. Also, the type of OLAP model used is based on the type of database system which exists.
• The top tier: It is designated by some form of front-end user interface or reporting tool that allows end users to perform ad-hoc data analysis on their business data

What are the Main Components of a Data Warehouse?

There are four main data warehouse components as mentioned below:

1. Load manager: It is also known as the front component. It conducts all the functions linked with the loading and extraction from the data warehouse. Its functions include transformation transformations to arrange the data for entering the data warehouse.
2. Warehouse manager: It conducts functions linked with data management in the warehouse. It conducts functions such as the creation of views and indexes, analysis of data to ensure consistency, transformation, and merging of source data, generation of aggregation and denormalization, and backing-up and archiving data.
3. Query manager: It is also called a backend component. It conducts all the functions linked with the management of user queries. The functioning of this component of the data warehouse includes direct queries to the suitable tables for scheduling the execution of queries.
4. End-user access tools: It includes five different groups such as data reporting, query tools, EIS tools, application development tools, and OLAP and data mining tools.

Who needs Data Warehouse?

Different types of data warehouses are used by all types of users such as:

• Decision makers whose functions depend upon the mass amount of data.
• People who use complex and customized procedures to bring data from multiple data sources.
• Data warehouses are used by people who wish for simple technology to access data.
• It is crucial for users who want a systematic approach to forming decisions.
• It is the foremost step implemented when you wish to identify hidden patterns and trends of data flows and groupings.
• If you are looking for fast performance on a huge amount of data which is significant for charts, grids, or reports then a data warehouse proves helpful.

Applications of Data Warehouse

Data Warehouse is a central repository that is designed to store and manage large volumes of data for analysis and reporting. It is a critical component of modern data architecture that enables organizations to make data-driven decisions. Some of the common applications of data warehouses are:

1. Business Intelligence: It supports business intelligence applications that provide insights into business operations. Business intelligence tools use data from data warehouses to generate reports, dashboards, and scorecards, which help decision-makers to monitor business performance and identify trends and patterns.
2. Customer Relationship Management (CRM): Data warehouses store customer data, such as customer profiles, purchase history, and interaction history. This data is used by CRM systems to provide personalized recommendations, targeted marketing campaigns, and improve customer satisfaction.
3. Telecommunication: For sales decisions, product promotions, and to make effective distribution decisions.
4. Supply Chain Management: Storing data such as inventory levels, order history, and shipping data comes under the supply chain management application. This data is used to optimize the supply chain process, reduce costs, and improve efficiency.
5. Banking: It is used to manage the resources accessible on the desk effectively. Other banks also use the data warehouse for market research and performance analysis of the product and operations.
6. Financial Analysis: Financial data such as transaction data, revenue, and expenses are stored in a data warehouse which is used by financial analysts to generate reports and insights into the financial performance of the organization.
7. Airline: It is used for operating objectives such as analysis of route profitability, crew assignment, frequent flyer program promotion, and much more.
8. Healthcare: In healthcare, it is used to store patient data like medical records, lab results, and imaging data. This data is used by healthcare providers to improve patient care, diagnose diseases, and develop treatment plans.
9. Investment and insurance sector: Used to analyze user patterns or trends, data patterns, and to track market activities.
10. Education: Data warehouses are used in education to store student data, such as grades, attendance, and behavior to monitor student progress, identify at-risk students, and develop personalized learning plans.

Overall, data warehouses are used in various industries and sectors to support decision-making, improve operations, and drive business growth.

Data Warehouse Best Practices

Creating a data warehouse depends upon understanding the business logic of your individual use case. The needs are different, but there are a few common data warehouse best practices that you should follow:

• Designing a plan to test the integrity, accuracy, and consistency of the data.
• A data warehouse should be well-defined, time-stamped, and well-integrated.
• While deciding on a data warehouse choose the most appropriate tool, stay affirmed with the life cycle, look after data conflicts, and be ready to learn from your mistakes.
• Never replace operational reports and systems.
• Do not spend much of your time cleaning, extracting, and loading data.
• Make sure you include all stakeholders along with business administrators during the data warehouse implementation process. Implementing a data warehousing project is teamwork. You do not wish to design a data warehouse that is not useful for the end users.
• Make a training plan for the end users

Benefits of A Data Warehouse

1. Better data quality: Data warehouses are designed to integrate and consolidate data from different sources, which helps to improve data quality by reducing redundancy, eliminating inconsistencies, and ensuring data accuracy.
2. Scalability: Data warehouses are designed to handle large volumes of data, which makes them scalable and flexible enough to accommodate changing business needs.
3. Smarter decision-making: A data warehouse facilitates large-scale BI functions like data mining, machine learning, and AI tools. These tools are used by business leaders and professionals to get hard evidence for making smarter decisions virtually across all aspects of the organization, Ranging from business processes to financial management and inventory management.
4. Improved Data Integration: A data warehouse can integrate data from disparate sources, such as databases, spreadsheets, and legacy systems, into a single repository. This can help organizations streamline their data management processes and reduce the risk of data silos.
5. Faster Access to Information: It can provide faster access to information, which can be crucial for making timely decisions. This can help organizations stay ahead of the competition and respond quickly to changing market conditions.
6. Gaining and growing competitive advantage: The advantages offered by data warehouses help companies find more opportunities related to their data, more quickly as compared to from distinctive data sources.

Conclusion

In the data industry, a data warehouse is a very significant element. This is because a data warehouse helps in analyzing data stored and processed in a database. Moreover, a data warehouse helps to uncover business patterns and trends that can be presented in a report form which offers valuable insights and drives business growth. Also, it is applied across multiple sectors like banking, insurance, airline, healthcare, and others.