Data Warehousing: Concepts and Need

• A data warehouse is designed to help businesses analyze historical data and make informed decisions.
• Data from different operational systems is collected, cleaned, and stored in a structured way, enabling efficient querying and reporting.
• The goal is to produce statistical results that may help in decision-making.
• Data warehouses ensure fast data retrieval even with vast datasets.

Need for Data Warehousing

• Traditional databases can only store a limited amount of data (MBs to GBs), but a data warehouse is designed to handle much larger datasets (TBs).
• Data warehouses allow businesses to store and manage massive amounts of historical data.
• Data warehouses are built specifically for data analysis, enabling businesses to perform complex queries and gain insights from historical data.
• A data warehouse acts as a central repository for all organizational data, helping businesses to integrate data from multiple sources for better decision-making.
• By storing historical data, data warehouses allow businesses to analyze trends over time for strategic decisions based on past performance and predict future outcomes.
• Data warehouses support business intelligence tools and reporting systems, providing decision-makers with easy access to critical information.
• Data warehouses enhance operational efficiency and support data-driven strategies.

Components of Data Warehouse

• Data sources are the various operational systems, databases, and external data feeds that provide raw data to be stored in the warehouse.
• The ETL (Extract, Transform, Load) process is responsible for extracting data from different sources, transforming it into a suitable format, and loading it into the data warehouse.
• The data warehouse database is the central repository where cleaned and transformed data is stored, typically organized in a multidimensional format for efficient querying and reporting.
• Metadata describes the structure, source, and usage of data within the warehouse, making it easier for users and systems to understand and work with the data.
• Data marts are smaller, more focused data repositories derived from the data warehouse, designed to meet the needs of specific business departments or functions.
• OLAP (Online Analytical Processing) tools allow users to analyze data in multiple dimensions, providing deeper insights and supporting complex analytical queries.
• End-user access tools are reporting and analysis tools, such as dashboards or BI tools, that enable business users to query the data warehouse and generate reports.

Characteristics of Data Warehousing

• Data warehousing provides a centralized repository for all enterprise data from various sources, such as transactional databases, operational systems, and external sources.
• Data warehousing integrates data from different sources into a single, unified view, which helps eliminate data silos and reduce data inconsistencies.
• Data warehousing stores historical data, which enables organizations to analyze data trends over time to identify patterns and anomalies for business performance improvement.
• Data warehousing provides powerful query and analysis capabilities that enable users to explore and analyze data in different ways to identify patterns and trends, and to make informed business decisions.
• Data warehousing includes a process of data transformation, which involves cleaning, filtering, and formatting data from various sources to make it consistent and usable.
• Data warehousing provides data mining capabilities, which enable organizations to discover hidden patterns and relationships in their data for identifying new opportunities, predicting future trends, and mitigating risks.
• Data warehousing provides robust data security features, such as access controls, data encryption, and data backups, to ensure that data is secure and protected from unauthorized access.

Types of Data Warehouses

• Enterprise Data Warehouse (EDW): A centralized warehouse that stores data from across the organization for analysis and reporting.
• Operational Data Store (ODS): Stores real-time operational data used for day-to-day operations, not for deep analytics.
• Data Mart: A subset of a data warehouse, focusing on a specific business area or department.
• Cloud Data Warehouse: A data warehouse hosted in the cloud, offering scalability and flexibility.
• Big Data Warehouse: Designed to store vast amounts of unstructured and structured data for big data analysis.
• Virtual Data Warehouse: Provides access to data from multiple sources without physically storing it.
• Hybrid Data Warehouse: Combines on-premises and cloud-based storage to offer flexibility.
• Real-time Data Warehouse: Designed to handle real-time data streaming and analysis for immediate insights.

Data Warehouse vs DBMS

• A common database is based on operational or transactional processing where as a data warehouse is based on analytical processing.
• Databases store current and up-to-date data used for daily operations , while data warehouses maintain historical data over time for trend analysis, future predictions, and decision support.
• A database is generally application specific and a data warehouse is integrated generally at the organization level, by combining data from different databases.

Issues Occur while Building the Warehouse

• Data sources transmit new information continually or periodically in a source-driven architecture, or the data warehouse periodically sends requests for new data to the sources in a destination-driven architecture.
• Data warehouses typically have slightly out-of-date data, which is usually not a problem for decision-support systems.
• Data sources constructed independently are likely to have different schemas, requiring schema integration and data conversion to the integrated schema before storage.
• The task of correcting and preprocessing data is called data cleansing.

Example Applications of Data Warehousing

• Social Media Websites: Websites like Facebook, Twitter, and LinkedIn analyze large data sets related to members, groups, and locations and store it in a single central repository.
• Banking: Banks use warehouses to see the spending patterns of account/cardholders to provide special offers and deals.
• Government: Government uses a data warehouse to store and analyze tax payments, which are used to detect tax thefts.

Advantages of Data Warehousing

• With centralized data in warehouses, decisions may be made more quickly and intelligently.
• Provides strong operational insights through business intelligence.
• Guarantees data quality and consistency for trustworthy reporting.
• Capable of managing massive data volumes and expanding to meet changing requirements.
• Fast and effective data retrieval is made possible by an optimized structure.
• Data warehousing can result in cost savings over time by reducing data management procedures and increasing overall efficiency, even when there are setup costs initially.
• Data warehouses employ security protocols to safeguard confidential information, guaranteeing that only authorized personnel are granted access to certain data.
• The data warehouse is designed to handle large queries, so it runs queries faster than databases.

Disadvantages of Data Warehousing

• Data warehousing can be expensive, requiring significant investments in hardware, software, and personnel.
• Data warehousing can be complex, and businesses may need to hire specialized personnel to manage the system.
• Building a data warehouse can take a significant amount of time, requiring businesses to be patient and committed to the process.
• Data from different sources can be challenging to integrate, requiring significant effort to ensure consistency and accuracy.
• Data warehousing can pose data security risks, and businesses must take measures to protect sensitive data from unauthorized access or breaches.

Threats that Might Affect Data Warehousing

• Data's security and integrity might be compromised by a number of possible dangers associated with data warehousing.
• Unauthorized Access: Unauthorized access is when someone enters the data warehouse without having the necessary authority.
• Theft: Theft is when someone takes data from the data warehouse.
• Hacking: Hacking is gaining illegal access to a computer network, particularly a data warehouse, with the purpose of obtaining, modifying, or damaging the data.
• Insider threats: Insider threats are when someone with access privileges to the data warehouse abuses that access for their own advantage or malevolent intentions.

Mechanisms for Guaranteeing Data Warehouse Security

• Access Controls: Setting up access restrictions to make sure that only people with permission can access the data warehouse.
• Authentication and Authorization: Implementing authorization mechanisms to ensure that users only have access to the data they need to carry out their job functions and implementing authentication mechanisms such as two-factor.
• Encryption: Protecting data from illegal access by encrypting it both in transport and at rest which calls for the use of encryption methods and secure key management procedures
• Auditing: Establishing auditing procedures to keep an eye on user behavior and spot any security breaches which entails recording user activity and inspecting it to look for irregularities or suspect conduct.
• Data Masking: Data masking approach protects sensitive data by replacing it with fake or obfuscated data that has the same format and properties but hides the actual data.
• Data Loss Prevention: Data backup and recovery procedures are implemented along with security measures like firewalls and intrusion detection and prevention systems to prevent unwanted access, alteration, or destruction of data.
• Regulatory Compliance: Putting in place the proper security processes and controls to safeguard data and guarantee legal compliance.

Advantages of Security in Data Warehousing

• Strong data security improves consumer trust by guaranteeing the confidentiality and integrity of customers' data.
• Enhancing decision-making, access restrictions, and audit trails can assist assure the quality and integrity of data.
• A business may reduce the enormous financial implications of security breaches by using more efficient security solutions.

Disadvantages of Security in Data Warehousing

• Data warehousing procedures may become more complicated as a result of the implementation of security measures, making it more challenging to administer and maintain them.
• Access restrictions caused by security measures like access controls can slow down corporate agility and decision-making processes.
• Data warehouse security is crucial in the current digital world to protect sensitive data.