Background
A Database Management System (DBMS) stores data in the form of tables, uses ER model and the goal is ACID properties. For example, a DBMS of college has tables for students, faculty, etc.
A Data Warehouse is separate from DBMS, it stores a huge amount of data, which is typically collected from multiple heterogeneous sources like files, DBMS, etc. The goal is to produce statistical results that may help in decision makings. For example, a college might want to see quick different results, like how the placement of CS students has improved over the last 10 years, in terms of salaries, counts, etc.
Need for Data Warehouse
An ordinary Database can store MBs to GBs of data and that too for a specific purpose. For storing data of TB size, the storage shifted to Data Warehouse. Besides this, a transactional database doesn’t offer itself to analytics. To effectively perform analytics, an organization keeps a central Data Warehouse to closely study its business by organizing, understanding, and using its historic data for taking strategic decisions and analyzing trends.
Benefits of Data Warehouse:
- Better business analytics: Data warehouse plays an important role in every business to store and analysis of all the past data and records of the company. which can further increase the understanding or analysis of data to the company.
- Faster Queries: Data warehouse is designed to handle large queries that’s why it runs queries faster than the database.
- Improved data Quality: In the data warehouse the data you gathered from different sources is being stored and analyzed it does not interfere with or add data by itself so your quality of data is maintained and if you get any issue regarding data quality then the data warehouse team will solve this.
- Historical Insight: The warehouse stores all your historical data which contains details about the business so that one can analyze it at any time and extract insights from it
Data Warehouse vs DBMS
Example Applications of Data Warehousing
Data Warehousing can be applied anywhere where we have a huge amount of data and we want to see statistical results that help in decision making.
- Social Media Websites: The social networking websites like Facebook, Twitter, Linkedin, etc. are based on analyzing large data sets. These sites gather data related to members, groups, locations, etc., and store it in a single central repository. Being a large amount of data, Data Warehouse is needed for implementing the same.
- Banking: Most of the banks these days use warehouses to see the spending patterns of account/cardholders. They use this to provide them with special offers, deals, etc.
- Government: Government uses a data warehouse to store and analyze tax payments which are used to detect tax thefts.
Features :
Centralized Data Repository: Data warehousing provides a centralized repository for all enterprise data from various sources, such as transactional databases, operational systems, and external sources. This enables organizations to have a comprehensive view of their data, which can help in making informed business decisions.
Data Integration: Data warehousing integrates data from different sources into a single, unified view, which can help in eliminating data silos and reducing data inconsistencies.
Historical Data Storage: Data warehousing stores historical data, which enables organizations to analyze data trends over time. This can help in identifying patterns and anomalies in the data, which can be used to improve business performance.
Query and Analysis: Data warehousing provides powerful query and analysis capabilities that enable users to explore and analyze data in different ways. This can help in identifying patterns and trends, and can also help in making informed business decisions.
Data Transformation: Data warehousing includes a process of data transformation, which involves cleaning, filtering, and formatting data from various sources to make it consistent and usable. This can help in improving data quality and reducing data inconsistencies.
Data Mining: Data warehousing provides data mining capabilities, which enable organizations to discover hidden patterns and relationships in their data. This can help in identifying new opportunities, predicting future trends, and mitigating risks.
Data Security: Data warehousing provides robust data security features, such as access controls, data encryption, and data backups, which ensure that the data is secure and protected from unauthorized access.
Advantages:
Improved data quality: Data warehousing can help improve data quality by consolidating data from various sources into a single, consistent view.
Faster access to information: Data warehousing enables quick access to information, allowing businesses to make better, more informed decisions faster.
Better decision-making: With a data warehouse, businesses can analyze data and gain insights into trends and patterns that can inform better decision-making.
Reduced data redundancy: By consolidating data from various sources, data warehousing can reduce data redundancy and inconsistencies.
Scalability: Data warehousing is highly scalable and can handle large amounts of data from different sources.
Disadvantages:
Cost: Building a data warehouse can be expensive, requiring significant investments in hardware, software, and personnel.
Complexity: Data warehousing can be complex, and businesses may need to hire specialized personnel to manage the system.
Time-consuming: Building a data warehouse can take a significant amount of time, requiring businesses to be patient and committed to the process.
Data integration challenges: Data from different sources can be challenging to integrate, requiring significant effort to ensure consistency and accuracy.
Data security: Data warehousing can pose data security risks, and businesses must take measures to protect sensitive data from unauthorized access or breaches.
There can be many more applications in different sectors like E-Commerce, telecommunications, Transportation Services, Marketing and Distribution, Healthcare, and Retail.
Reference :
http://www3.cs.stonybrook.edu/~cse634/presentations/DataWarehousing-part-1.pdf
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