A Data warehouse is the foundation of a successful Business Intelligence program. It is an increasingly important business intelligence tool that allows organizations to ensure consistency as it is programmed to apply a uniform format to all collected data. It helps to standardize data from different sources. It can also reduce the risk of interpretation error and improve the overall accuracy. It improves the speed and efficiency of accessing different data sets and makes it easier for the business to make more informed decisions.
Data warehousing is very important for your business as it can help you create a central location and permanent storage space for the different data sources required to support a company’s analysis, reporting, and other business functions. Therefore it can be applied directly to business processes, including sales, financial management, inventory management, and marketing segmentation.
Data warehousing can deliver enhanced business intelligence so that executives and managers do not require to make business decisions based on limited data. It is becoming a crucial strategic part of the data science initiatives of almost every organization. So there is a high demand for data experts in this domain who have data science certification as well as experience to handle data issues.
In this article, we are trying to throw some light on the topic “What is Data Warehouse?”
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What is Data Warehouse?
A data warehouse is known as a data management system that is designed to enable and support business intelligence activities, especially data analytics. It is a large collection of business data used to help an organization make data-driven decisions. Here data warehousing process is known for collecting and managing data from different sources to provide meaningful insights. It is an excellent blend of components and technologies which aids the strategic use of data. It is typically used to connect and analyze business data from heterogeneous sources. Data warehousing is considered the core of the BI system built for data analysis and reporting.
Data warehousing aims to create a trove of historical data that can be retrieved and analyzed to provide useful insights into the organization’s operations. Data Warehouse is also known as an enterprise data warehouse (EDW). These data warehouses are repositories of integrated data from one or more disparate sources. They can store a huge amount of current and historical data.
Data warehousing is a crucial part of business intelligence that encompasses the information infrastructure that modern businesses use to track their past successes and failures and report their decisions for the future. The important thing in building an effective data warehouse is defining the information critical to the organization and identifying the sources of the information. So it is designed as an archive of historical information that enables the analysis of historical data.
A data warehouse provides a new design that helps to reduce the response time and helps to enhances the performance of queries for analytics and reports. So we have here Data warehousing system which is known by many different names such as DSS (Decision Support System), Management Information System, Executive Information System, Analytics Application, and Business Intelligence Solution.
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Types of Data warehouse
The three main types of Data warehouses are:
- Enterprise Data warehouse- EDW (Enterprise Data Warehouse) is known as a centralized warehouse. It provides decision support services across the enterprise. These warehouses are mainly a collection of databases that offer a unified approach for organizing, classifying, and representing data according to the subject. It also provides access according to those divisions.
- Operational Data Store- ODS (Operational Data Store) is known as a central database that is used for operational reporting as a data source for the enterprise data warehouse. ODS is required when neither Data Warehouse nor OLTP systems support organizations reporting needs. It is a complementary element to an EDW that is used for operational controls, reporting, and decision-making processes. In ODS, a data warehouse is refreshed in real-time so it is widely preferred for routine activities like saving records of the employees, etc.
- Data Mart- A Data Mart is known as a subset of a data warehouse that is usually oriented to a specific team or business lines like sales or finance. In an independent data mart, data can collect directly from sources. Datamart is subject-oriented that makes specific data available to a defined group of users more quickly and provides them with critical insights.
Data Warehouse Architecture
Now let us know about Data Warehouse architecture. It has basically three-tier architecture that consists of a :
- Bottom Level- The bottom level or bottom-tire consists of a data warehouse server and a relational database system that collects, cleans, and transforms data from multiple data sources through a process known as Extract, Transform, and Load ( ETL).
- Middle Level- The middle level or middle-tier consists of online analytical processing (OLAP) servers which are helpful in fast query speed. At this level, three types of online analytical processing models can be used, which are known as ROLAP, MOLAP, and, HOLAP. These types of OLAP are used on the basis of the type of database systems that exist.
- Top Level- Top-level or top tier is known as a reporting tool that is represented by some kind of front-end user interface. It enables end-users to conduct ad-hoc data analysis on their business data.
So the entire work of the data warehouse depends on these three tiers. A data warehouse collects and organizes data into a comprehensive database where data is sorted into several different tables depending on the data type and layout. Data warehousing is important as it helps store confidential business details such as employee details, salary information, etc.
The proper data warehousing platform can help you think ahead of your competitors and also help your business climb to the top of the competition curve.
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