A data warehouse contains highly structured data acquired from different sources using a combination of methods. Different types of data warehouses can be used to implement data in a structured way. It is important to understand what data warehouse is and why it is evolving. Let’s dive into this article to learn in detail what is data warehousing, its different types, components, applications, and more about data warehouses.
What is a Data Warehouse?
A data warehouse can be defined as a large, centralized repository of data that is used for reporting, analysis, and decision-making purposes. Data warehouses are typically designed to handle structured data from a variety of sources, including transactional systems, operational databases, and external sources such as customer surveys and market research.
A data warehouse is a type of data management system which is used to manage large amount of historical data. A data warehouse acts as a centralized archive for all the stored data. But what is data warehousing? The process of data warehousing involves compiling and using the collected data so that organizations can gain valuable insights and answer several business questions.
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How Does Data Warehouse Work?
A data warehouse transforms relational data and other data sources into multidimensional analytical concepts. During this transformation, metadata gets formed to increase the speed of searches. A semantic layer exists on the top of the data layer to organize and map complex data into simple terms for the quick building of analyses. An analytics layer is present on top of the semantic layer to provide authorized users access to visualize and interpret data.
Key Characteristics of Data Warehouse
Now that we have understood what data warehouse is, Let’s look at the key characteristics of a data warehouse are as follows:
A data warehouse always offers information on a specific topic. For instance, you can get information about a supply chain or sales inventory.
Historical information can be stored in a data warehouse. For instance, you will be able to easily retrieve information from 3 months, 6 months, or even longer.
Integration involves using a common unit of measurement for all related data. A data warehouse requires you to store simple data in a universally acceptable manner. Moreover, the data should be consistent in terms of layout and nomenclature. It is particularly useful for big data analysis.
Since a data warehouse is non-volatile, past data cannot be erased from it. The information is read-only and can only be modified on a routine basis. It also enables statistical evaluation so that you don’t need complicated procedures to comprehend what events occurred and when.
In a data warehouse, data mining is a capability that involves searching for significant patterns in large quantities of data and developing inventive approaches to boost sales and profits. Learn more about data mining and data warehousing in detail.
Types of Data Warehouse
There are several types of data warehouse system. The major types of data warehousing are as follows:
Enterprise Data Warehouse (EDW)
An Enterprise Data Warehouse is a centralized type of data warehousing. It offers support throughout the organization to make decisions. It comes with a unified approach for data organization and representation. It enables you to segment data according to subject and grant access according to the classifications.
Operational Data Store (ODS)
An operational data store is necessary when a firm’s information needs cannot be met by an OLTP system or a data warehouse. An ODS can be refreshed in real-time and used for routine tasks like stashing employee records.
A data mart is a sub-segment of a data warehouse. This type of data warehouse is useful for specific business segments like sales, funding, or both. Data directly collected from different sources can be stored in an independent data mart.
Components of Data Warehouse
Data warehouse is huge data management system which is made up of multiple components. The different components of a data warehouse are as follows:
A load manager is the front component of a data warehouse. It helps prepare data before it can enter different types of available warehouses.
After data enters the warehouse, a warehouse manager becomes responsible for managing it. A warehouse manager evaluates, transforms, and aggregates data inside a warehouse.
A query manager is the backend component of a data warehouse. It is responsible for tackling user queries related to management.
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Advantages of Data Warehouse
There are multiple ways in which data warehouse can be used. Let’s look at the major advantages of data warehouse in detail:
- Data warehouse enables businesses to access crucial data from different sources in a single place.
- A data warehouse offers consistent information about various cross-functional activities, like ad-hoc reporting and queries.
- It ensures that different sources of data can be integrated to lower stress on the production system.
- It can help shorten the total turnaround time for reporting and analysis.
- A data warehouse can save a user’s time in retrieving data from different sources.
- A data warehouse can store a huge amount of historical data, and it enables users to assess data from different periods to make future predictions.
Disadvantages of Data Warehouse
Let’s look at the major disadvantages of data warehouse in detail:
- Data warehousing is not useful for unstructured data.
- Creating and implementing can be time-consuming.
- It gets outdated quite fast.
- You will struggle to make changes in different data types and ranges, data source schema, indexes, and queries.
- Even though it seems easy on the surface, it is extremely complicated.
- Organizations need to use a lot of their resources for implementing data warehouses.
Applications of Data Warehouse <H2>
A data warehouse is necessary for multiple industries for analyzing, reporting, reporting, and ensuring strict discipline. Let us look at some examples of how companies use different types of data warehouse in their day-to-day operations. The top industries where the need for data warehousing is necessary are as follows:
The right data warehousing solution is necessary for the effective handling of existing funds. It helps with customer information analysis and regulatory changes. Moreover, it also helps with the identification of proper industry trends to make informed decisions.
Data warehouses also have massive applications in the healthcare industry. The warehouses contain clinical, employee, and financial data. Data analysis can be performed easily to gain valuable insights for resource planning.
In the insurance industry, data warehousing can help with maintaining existing customer records. It also helps with the analysis of customer records to gain information about client trends. It helps with bringing more customers into the business.
Data warehousing is crucial in the educational industry to develop a better understanding of students and faculty members. Educational institutions find access to real-time data feeds, which enable them to make valuable and informed decisions.
The service sector needs data warehouses to track customer information, financial records, and other resources. It enables them to identify patterns and enhance decision-making for better outcomes.
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Data Warehousing Tools
So far, we have covered, what is data warehouse, data warehouse types and major advantages and disadvantages associated with data warehouse. The top data warehousing tools are as follows:
In 2010, Microsoft Azure was launched as a cloud computing platform. It enables users to develop, assess, execute, and tackle applications and services with the help of Microsoft-managed data centers. Microsoft Azure can help fulfil the needs of small as well as massive web applications.
CloverDX is a data integration platform for individuals who need greater control while tackling complex problems in high-stress environments. CloverDX is a data warehouse tool that communicates extremely well with external systems.
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Tableau is a popular data warehouse tool in the business intelligence industry. The tool supports the analysis of complex data using a simple format. Data visualizations performed using the tableau tool are available as dashboards and worksheets.
Exadata is useful for autonomous data warehouses and can help automate administrative tasks. The self-driving platform can automate everything by employing adaptive machine learning. The data warehouse tool also uses columnar processing and parallelism to improve efficiency and flexibility.
MariaDB acts as a high-performance database and provides support for customer-facing applications. The data warehouse tool is also useful for creating columnar databases for real-time analytics. It also uses massively parallel processing to run SQL queries across multiple rows.
In this guide we have learned what is data warehouse, major types of data warehouse, advantages and disadvantages associated with data warehouse. In conclusion, a data warehouse is an essential tool for businesses seeking to make informed decisions based on vast amounts of data. By collecting, integrating, and managing data from various sources in a centralized repository, a data warehouse provides a single source of truth that can be used to generate valuable insights and improve decision-making processes.