Data warehouse

Data Warehouse & Data-Marts overview

Data warehouse architecture

ELT Diagram

Data Warehouse is a system used for reporting and data analysis, and is considered a core component of business intelligence. Data warehouses are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise.

The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc.). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting.

Overview

A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to:

• Consolidate data from many sources into a single database so a single query engine can be used to present data.
• Classify data according to the subject and give access according to those divisions.

Architecture

The typical Data Warehouse architecture consists of the following tiers:

• The database server, where data is loaded and stored.
• The middle tier, consisting of the analytics engine that is used to access and analyze the data.
• The front-end client tier, which includes reporting tools, analysis tools, and/or data mining tools.

Types of Data Warehouses

There are mainly three types of data warehouses:

• Enterprise Data Warehouse (EDW): Provides a centralized, consolidated database for the entire organization.
• Operational Data Store (ODS): Has a more operational perspective and is updated in real-time.
• Data Mart: A subset of a data warehouse, focused on a particular line of business, department, or subject area.

Data Warehouse Technologies

Data warehouse technology includes:

• Database systems
• Extraction, Transformation, and Loading (ETL) tools
• Online Analytical Processing (OLAP) engines
• Client reporting tools
• Data mining tools

Benefits

The key benefits of a data warehouse include:

• Improved business intelligence
• Enhanced data quality and completeness
• Historical intelligence

Challenges

However, data warehouses also face several challenges:

• Data integration from disparate sources
• High costs for data storage and management
• Ensuring data quality and consistency

Future Trends

With the advent of big data and cloud computing, the future of data warehouses is evolving towards:

• Cloud-based data warehouses
• Real-time data processing
• Increased focus on data governance and security