Key Components of Data Warehouse: An In-depth Guide

A data warehouse is a fundamental structure in present-day, data-driven companies, providing a facility for storing, managing, and analysing big data. Understanding how a data warehouse works requires investigating its constituent parts like any complex system. These components work together as individual elements of a system responsible for organizing, efficiently storing, and retrieving information.

What is a Data Warehouse?

A data warehouse, therefore, is a large centralized store of data, usually structured but can contain data that are unstructured in some instances. It is intended to support querying, reporting, and analyses and is a valuable tool in decision-making. Also, the operational databases are more concerned with the present and current transactions than the data warehouse. A common form this data takes contains subject-oriented information and is integrated, time-variant, and non-volatile – the best to use in trend analysis and strategic planning.

• Data Sources: These are the data sources or databases collected and fed to the data warehouse. Some data stores include operational databases, external databases, and flat files.

• Internal Data: Prepared reports from different divisions, such as employment and finance, as well as reports used in strategic decision-making, resource management, and performance evaluation, will give an understanding of the company’s performance.

• Archived Data: Backup or archive data refers to old data stored for use but could be kept for compliance, legal or analytical purposes. It ensures compliance of the company’s operations with the existing legal provisions and enables examination of the trends occurring in the past.

• External Data: Industry data, data from third-party databases, and industry reports give industry perspective to the analysis, comparison, and tracking purposes. Data governance ensures that data is accurate, complete and secure through its life cycle.

• Data Extraction, Transformation, and Loading(ETL):  Data in the ETL (Extract, Transform and Load) process of data involves the process whereby data is gathered from the source systems, processed and placed into the data warehouse as a form that would be most useful. It refers to the period data undergoes some activities such as processing, cleaning, analyzing and aggregation for business use.

• Data Storage: Data storage is the subcomponent of the data warehouse that is responsible for data storage. Some benefits of data storage in a data warehouse are data consolidation, fast access to data and better quality data due to data transformation. The issues are high costs of data storage provided service, loss or damage to important data, and security threats related to keeping large amounts of confidential information in one place.

• Hard Disc Drives (HDDs): This employs spinning platters and read/write heads for magnetic data storage.

• Solid State Drives: This utilises flash memory, ensuring faster data access than HDDs.      

• Storage Area Networks(SANs) and Network-Attached Storage: This provides scalable options for centralised data management.

• Data Warehouse Database: It is the central repository where data is stored in a structured and optimized format.  This is typically used as a relational database management system (RDBMS) to organize data in tables and schemas. The data is often organized in a star or snowflake schema for analytical queries.

• Data Mart:  The data mart is a considerably more limited version of the data warehouse and is used in organizations by a solitary or a particular business segment like OLAP applications. A data mart helps improve reporting and analysis within only some categories of users.

• Metadata: The metadata is the data about the data. This describes the structure, definitions, and relationships within the data warehouse, helping users understand the context and usage of the data. The metadata includes information on data lineage, transformation rules, and data sources.

• OLAP(Online Analytical Processing) Cube: OLAP cubes are used for fast querying and data analysis from the end user’s perspective. They permit users to look at the data regarding social and temporal or product dimensions and so on. OLAP cubes are well suited to analytical queries.

Functions of a Data Warehouse

• Data Integration: A data warehouse is the logical assimilation of data from distributed sources into a consolidative library. It becomes an effective means of maintaining data content integrity and accuracy that is adequate for serving the current and future needs of its processing within the system.

• Historical Data Retention: Data warehouses keep long-term and more detailed records, thus enabling trend analysis and reporting over long periods.

• Analytical Capabilities: Every structured data warehouse supports different levels of analysis and queries, builds complex and informative reports and is equipped to aid strategic decision-making.

• Performance Optimization:  Data warehouses are optimized for queries on large amounts of data in terms of time needed to perform these operations accomplished through indexing and aggregation, among other things.

• Separation from Operational Systems:  Data warehouses also prevent reporting and querying from consuming significant time on the systems that support important business operations, as the analytical processing occurs in a different system.

Also Read: Exploring Advantages and Disadvantages of Data Warehouse

Types of Data Warehouse

There are several types of data warehouses, each with unique characteristics and use cases. Let’s describe the most common types:

• Enterprise Data Warehouse(EDW): An EDW is a single operational data warehouse used throughout the organization to access all business data. EDWs are large centralized systems built to meet disparate analytical and reporting requirements.

• Departmental Data Warehouse: A data mart is part of a data warehouse for a large organization and is created to address the needs of a particular department. Data marts are generally considered smaller and more focused than an organization’s data warehouse.

• Data Governance and Security: The means and procedures for keeping data warehouse quality reliable and secure, namely, data access authorities, data audit trails, and data quality control.

Benefits of Data Warehouse

• Enhanced Decision-Making: The data warehouse is characterised by the processors’ integration of information from different sources and a consistent way to present it. This rich set of data can be helpful to improve decision-making among organizations.

• Improved Data Quality and Consistency: Data cleansing, validation, and transformation during warehousing to make the data accurate in the organization. This, in turn, eliminates mistakes and improves the efficiency of analytical results.

• Historical Data Analysis: Most data warehouses capture information on past performances, and organizations can gauge their future performances based on the information.

• Faster Query Performance: Data warehouses are optimized for analytics to facilitate faster querying and reporting and emphasise real-time data processing.

• Integration of Data From Multiple Sources: The data warehouses consolidate data from disparate sources such as CRM, ERP and financial systems, providing a single version of the truth for the organization.

• Support for Business Intelligence Tools: It is integrated with different BI and reporting tools useful for generating the dashboards, KPI and other reports.

• Enhanced Data Security: The data warehouses often include robust security features to protect sensitive data, ensuring compliance with industry regulations and standards.

• Time and Cost Efficiency: Data warehouses minimise the time and undertaking costs drawn out in gathering, restructuring, and reporting data to other parts of an organization.

Also Read: Characteristics of Data Warehouse

Difference between Database and Data Warehouse

Here is the comparison between a Database and a Data Warehouse in a tabular form.

Also Read:  Top 30 Data Warehouse Interview Questions & Answers

Conclusion

In a data warehouse, the various components act as the framework poles through which large amounts of data can be captured, stored, processed and analyzed. They include data source, the ETL process, storage components, metadata, and tools for access and analysis, all of which play a vital role in interacting with the components and converting raw data into meaningful information and knowledge. Having analyzed the data integrity, consistency, and availability, these components enable companies to make informed decisions and level the playing field.

While organizations have gradually incorporated data-driven approaches, the manager for a data warehouse must still be aware of and manage these components: data acquisition, processing, storage and retrieval systems, data distribution, and data presentation. If you want to explore the key components of a data warehouse in detail, check out the Accelerator Program in Business Analytics and Data Science offered by Hero Vired in collaboration with edX and Harvard University.