Understanding Data Warehouse Concepts: A Beginner’s Guide

A data warehouse is a central database containing large volumes of data in structured and unstructured formats. It is thus developed to support querying and data retrieval for business intelligence needs. Data warehouses are widely used in effective decision-making because they consolidate organizational information. This article highlights some of the most important ideas, structures, and advantages of data warehousing.

What is a Data Warehouse?

A data warehouse may be defined as an integrated, subject-oriented, time-variant, current, active database containing all the data it needs in real time. Its purpose is to assist various individuals in evaluating info and preparing the appropriate reports and choices as this tool consolidates historical and currently obtainable data. Unlike other databases designed to process transactions, data warehouses are created to quickly store and process massive amounts of data with diverse queries.

Data warehousing came into existence towards the end of the eighties and at the beginning of the nineties when managers realized that the issue of large volumes of potential organizational data needed to be dealt with in a standardized manner.  It is an age characterised by the transition from transactional systems, where the main focus was the routine operational aspects of a business, to analytical systems intended to give information to support decision-making.

Also Read: Data Warehousing and Data Mining in Detail

Early Beginnings: The 1970s and 1980s

The early background of DW lay in the early 1970s when organizations started organizing computer bulk work. Nevertheless, the information produced by such systems was typically unstructured and stored in different databases or files. It also fragmented the attempts to obtain an integrated perspective on organizational data.

In the mid-1980s, very large-scale integrated companies such as IBM and Teradata developed other technologies for managing data concerning one another. At this point, there was a recognized need for the consolidation of warehouses for housing and data processing. Nevertheless, the initial models of generating such repositories were problematic owing to restricted computation abilities and storage space.

Features of a Data Warehouse

• Subject-Oriented: The data is organised around business subjects like sales, inventory, and finance.

• Integrated: The data from different sources is cleaned and standardised to ensure consistency.

• Non-Volatile: Once data is entered, it remains unchanged, preserving historical records.

• Time Variant: This contains time-stamped data for trend analysis over different periods.

Components of a Data Warehouse

• Source Systems: These are the operational databases and external systems from which data is extracted.

• ETL Process: This stands for extract, transform, and load. This process gathers data from source systems, transforms it into a usable format, and loads it into the warehouse.

• Data Storage: The central repository where data is stored, typically organized in schemas such as stars or snowflakes.

• Metadata: The data about, which helps in data cataloging, retrieval, and usage.

• BI Tools: The applications that allow users to query, report, and visualize data.

Data Warehouse Architecture

The data warehouse architecture refers to the design and structure of the various layers and components.

• Bottom Tier: The last level or data warehouse server typically depicts a relational DBMS system. Back-end tools, which are tools for data cleansing and transformation, are applied to feed data into this layer.

• Middle Tier: The middle tier consists of an OLAP server that can be implemented in two ways. The ROLAP or the Relational OLAP model is an extended RDBMS that performs multidimensional data to standard relational data processes. The MOLAP or multidimensional OLAP directly refers to multidimensional data and operations since it works only with multidimensional data.

• Top Tier: The front-end client interface gets data out from the data warehouse. This holds various tools like query, analysis, reporting, and data mining tools.

Types of Data Warehouse

There are three types of data warehouses.

• Enterprise Data Warehouse (EDW):  This kind of warehouse serves as a key or even chief data source for the solution providers that services the corporation. The benefit of this sort of warehouse is that they want access to other organisations’ data in the same commercial region. A single analytical model is used to portray the data and offers the actualisation of elaborate queries.

• Operational Data Store(ODS): This type of data warehouse is updated instantly. They are much more often chosen for minor tasks such as archival storage of employee records. The need arises when the DW systems fail to meet the business’s reporting requirements.

• Data Mart: A data mart is a part of a data warehouse restricted to containing information specific to a department, region, or business division. Each department in the business will have some data mart or data repository to store the data. The information from the data mart is moved to the ODS at the time intervals. The ODS then forwards the data from the source system to the EDW, where it is used and stored.

Goals of Data Warehousing

• For reporting purposes as well as for analysis
• This keeps the recorded data of the organization’s past events.
• To be a cornerstone of the decision-making process.

Also Read: Top 30 Data Warehouse Interview Questions & Answers

Conclusion

Today’s business intelligence could not exist without data warehouses, which store and facilitate access to significant information. They act as a single source of unification for the total data required in a company or an organisation to be gathered from different sources. Forcing analytics and decision-making activities, data warehouses allow organizations to analyse data to enhance performance and competition in a modern data-driven environment. Yet, as businesses progress, getting acquainted and profiting from data warehouse ideas becomes a pivotal factor in unlocking enterprise data potential and guaranteed viability. To get a complete walkthrough of databases professionally, you can try the Accelerator Program in Business Analytics and Data Science by Hero Vired, which is offered in collaboration with edX and Harvard University.