Data Warehousing

Data warehousing is the process of collecting, integrating, storing, and managing data from multiple sources in a central repository. It enables organizations to organize large volumes of historical data for efficient querying, analysis, and reporting.

The main goal of data warehousing is to support decision-making by providing clean, consistent, and timely access to data. It ensures fast data retrieval even when working with massive datasets.

Need for Data Warehousing

• Handling Large Data Volumes: Traditional databases store limited data (MBs to GBs), while data warehouses are built to handle huge datasets (up to TBs), making it easier to store and analyze long-term historical data.
• Enhanced Analytics: Databases handle transactions; data warehouses are optimized for complex analysis and historical insights.
• Centralized Data Storage: A data warehouse combines data from multiple sources, giving a single, unified view for better decision-making.
• Trend Analysis: By storing historical data, a data warehouse allows businesses to analyze trends over time, enabling them to make strategic decisions based on past performance and predict future outcomes.
• Business Intelligence Support: Data warehouses work with BI tools to give quick access to insights, helping in data-driven decisions and improving efficiency.

Components of Data Warehouse

The main components of a data warehouse include:

• Data Sources: These are the various operational systems, databases, and external data feeds that provide raw data to be stored in the warehouse.
• ETL (Extract, Transform, Load) Process: The ETL process is responsible for extracting data from different sources, transforming it into a suitable format, and loading it into the data warehouse.
• Data Warehouse Database: This is the central repository where cleaned and transformed data is stored. It is typically organized in a multidimensional format for efficient querying and reporting.
• Metadata: 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: These 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: OLAP tools allow users to analyze data in multiple dimensions, providing deeper insights and supporting complex analytical queries.
• End-User Access Tools: These are reporting and analysis tools, such as dashboards or Business Intelligence (BI) tools, that enable business users to query the data warehouse and generate reports.

Read related article - Data Warehouse Architecture

Characteristics of Data Warehousing

Data warehousing plays a key role in modern data management by helping organizations store, integrate, and analyze data effectively. Its main features include:

• Centralized Data Storage: Combines data from various sources into one place for a complete view.
• Query & Analysis: Supports fast and flexible data analysis for better decision-making.
• Data Transformation: Cleans and formats data for consistency and quality.
• Data Mining: Finds hidden patterns to discover insights and predict trends.
• Data Security: Protects data with encryption, access control, and backups.

Read more about Characteristics and Functions of Data warehouse

Types of Data Warehouses

The different types of Data Warehouses are:

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

Data Warehouse vs DBMS 

Issues Occur while Building the Warehouse

1. When and How to Gather Data?

• Source-driven: Data sources push updates to the warehouse periodically or continuously.
• Destination-driven: The warehouse pulls data on a fixed schedule.
• Perfect sync is costly, so data is slightly outdated — acceptable for analysis.

2. What Schema to Use?

• Sources have varied formats.
• The warehouse stores a cleaned, unified version — not a direct copy, but a consistent snapshot for analysis.

3. Data Transformation and Cleansing

• Fixes errors like typos or invalid codes using reference data.
• Fuzzy lookup helps match similar but not identical values.

4. How to Propagate Updates?

• If warehouse schema = source schema -> easy sync.
• If not -> it becomes a view maintenance challenge.

5. What Data to Summarize?

• Raw data is large; store summaries (e.g., total sales by category).
• Aggregates support efficient querying without full details.

Read more about Difficulties of Implementing Data Warehouses

Real world Example 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. 

1. E-commerce: Flipkart

• Data Gathering: Orders, returns, payments, user clicks, delivery updates.
• Schema: Combines source data into a structured star schema for analysis.
• Cleansing: Standardizes customer names, locations, and product categories.
• Updates: Near real-time or scheduled loads for fresh insights.
• Summarization: Bestsellers by category, regional demand trends, logistics performance.

2. Banking: HDFC Bank

• Data Gathering: ATM transactions, online banking, credit card usage, loan records.
• Schema: Integrates data from core banking, CRM, and fraud detection systems.
• Cleansing: Fixes inconsistencies in account info, transaction logs, and addresses.
• Updates: Transaction data is batched and uploaded nightly.
• Summarization: Daily cash flow reports, high-risk account flags, and customer profitability analysis.

Advantage and Disadvantage of Data Warehousing