Chen Cuello
MAY 26, 2023
5 min read
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Is your business handling a significant volume of daily transactions? Do you possess extensive historical data that you wish to analyze for business improvement? In that case, you require a database and a data warehouse. 

However, it’s crucial to understand which type of data should be stored in each system. This article provides high-level definitions and detailed explanations of databases and data warehouses, highlighting their distinct purposes, differences, and how you can utilize them for effective business growth.

What is a Data Warehouse? 

To understand the difference between a database and a data warehouse, first, we must answer the question, “What is a data warehouse?” A data warehouse is a centralized repository of structured, organized, and historical data from various sources within an organization. It supports business intelligence, data analysis, and reporting.

Its primary purpose is to provide a consolidated view of data across different systems and departments, integrating and transforming it into a standardized format for analysis.

Data warehouses facilitate complex queries, data mining, and analytical processing, using a multidimensional data model for varied perspectives. Data warehouses are subject-oriented, focusing on specific areas like sales or finance, enabling efficient analysis.

There are three types of data warehouses:

What is a Database?

A database is a collection of data stored in a system or software as structured information, which enables the functioning of the particular system or software. It acts like a virtual filing cabinet, helping you organize and manage information efficiently. Databases allow you to store, retrieve and manipulate data, ensuring data integrity. Unlike a warehouse, a database uses a single source.

There are four types of databases: 

  • Object-oriented databases
  • Non-relational databases
  • Relational databases
  • Hierarchical databases

Data Warehouse and Database: Key Differences 

Database vs. data warehouse, so what are the main differences between them? Let’s take a look at their purpose, use, structure, volume, integration, reporting, analysis, and performance.

Purpose and Use

Database stores structured data in the computer system or software and uses it for the functioning of that particular software or system. On the other hand, a data warehouse aims to store historical and current data for data analysis within a predefined scheme of parameters.

Data Structure

Another difference between a database and a data warehouse is the data structure. Data gathered in a database is structured as efficiently as possible without duplicate info. In contrast, the structure within the data warehouse is denormalized because the data warehouses emphasize the data read over the data writing operations.

Data Volume

A data warehouse volume is much larger than the volume of a database. The main reason for this is that data warehouses are usually used for data mining, while databases are used for the functioning of a particular system. 

That’s why a data warehouse can process big chunks of data, the Online Analytical Processing (OLAP) data warehouse, while a database has a much lower capacity.

Data Integration

Another difference is the data integration process. A database needs written code that’ll function like a scheme or a set of data integration parameters, taking into account all the client’s applications. The typical database integration scheme is as follows:

  • The client sends a request or an application to the database master server.
  • The master server checks the request against external and internal sources and extracts it from them.
  • The master server consolidates the data.
  • The master server serves it back to the customer.

On the contrary, the data integration process of data warehouses is much more complex because it combines data from multiple sources into a single unified view. Following are the data integration steps of the data warehouse integration:

  • New chunks of data enter the system.
  • They’re checked against the data warehouse parameters and existing integration rules.
  • They’re combined and stored in the data warehouse.

Query and Reporting

When we compare database vs. data warehouse, we’ll see that all databases, except OLTP databases, have limited query and reporting capacities. The reason is that there are historical records within the database. On the other hand, data warehouses store different information with various histories, which makes them ideal for queries and reports.

Data Analysis

The information and data stored in a database have a limited history range, and that’s why they’re hard to analyze. In contrast, data warehouses are specifically designed for analysis and reporting, so it’s much easier to access the data and conduct a query. 

Scalability and Performance

Data warehouses are more scalable than databases because they can process large volumes of data. Contrary, databases are more efficient for small-scale queries and transactions.

Benefits of Data Warehouse

Dbms vs. data warehouse also differ in their key benefits. Following are the advantages of using and operating a data warehouse.

Business Intelligence and Analytics

A data warehouse is designed to support management solutions, decisions, and analytics. It optimizes day-to-day operations and supports all organizational levels.

Enhanced Data Quality and Consistency

Data warehouses improve quality and consistency by enabling employees to find disparities before transferring the information into the warehouse.

Historical Data Retention

A data warehouse can store historical data for up to seven years, supporting the management in time-relevant and business-efficient operations and decisions.

Simplified Data Management

Data warehouses can eliminate complex data by storing, analyzing, and accessing parameters and data pipelines.

Benefits of Database

To make the right choice, you need to know the difference between Dbms and data warehouse within the framework of the database benefits.

Flexibility and Ease of Use

Databases are flexible and easy to use because of features like growing, shrinking, or changing datasets to meet new sets of requirements. Moreover, they’re sortable and searchable, allowing easy access and navigation.

Real-time Data Processing

Real-time databases, especially cloud databases, are designed and coded for collecting, processing, and enriching data-point series or so-called “data streams” in real-time.

Fast Data Retrieval

Another benefit of using databases is the fast data retrieval option. A famous feature is the indexing option which you can utilize for organization and location purposes. The fastest data retrieval system is MySQL or SQL relational databases.

Application Development

Another benefit of databases is the application development or the opportunity to create or implement new databases or enhance the existing ones with brand-new applications. Database application development is also used for integration, security, and benchmarking.

How to Choose Between Data Warehouse or Database

The database vs. data warehouse debate brings out many differences between the two. However, both have advantages, making it that much harder to choose the right one. To simplify it for you, we’ve outlined some factors to consider, as well as use cases.

Factors to Consider

To choose dbms vs. data warehouse, you’ll have to consider multiple factors. Some essential criteria for making this decision are:

  • Available sources
  • Storage locations
  • Queries and analytics features
  • Data types

For example, if you operate with multiple sources, a data warehouse is a better alternative than a database. On the other hand, if you want a decentralized storage system and a few locations to store your business-relevant data, a database would be a better option. 

As we mentioned, a data warehouse is the best solution for data analytics and queries, while a database is great for application development, accessibility, and real-time information processing.

Use Cases and Scenarios

You can determine whether your business needs a database or a data warehouse based on the various use cases and scenarios. A sheer example of this would be the case when you want to optimize and automate your business process and keep track of customers’ info and applications. In this situation, the better option for you would be a database.

Another use case scenario would be evaluating your teams’ performance or the success of the various marketing campaigns. Here, the best practice would be to implement a warehouse because it can generate multiple queries and enable thorough data analysis.

Decision-Making Framework

Other crucial factors for choosing the most appropriate system, making a difference between database and data warehouse, are the decision-making frameworks. An example of this would be the OLTP vs. data warehouse frameworks. 

Databases use OLTP (Online Transaction Processing), which allows them to delete, change, or update huge numbers within small online transactions. On the contrary, data warehouses usually use OLAP (Online Analytical Processing, which is efficient for rapidly analyzing huge amounts of data.

Data Warehouse and Database Best Practices

As mentioned, both data warehouses and databases can be beneficial. But to get the most out of them, it’s important to know what you’re using them for.

Data Modeling and Design

Some of the best data modeling and design you can apply on both your data warehouse or database include thorough acknowledgment of the business requirements, finding a single criterion for evaluation of one source of truth, starting simply, and then expanding the model later and more. 

Regarding databases, the best data modeling and design practices are making the data transparent, choosing the right data type, prioritizing privacy, and identifying constraints for data integration purposes.

On the other hand, the best data warehouse modeling and design practices start with Solid Master Data Management (MDM). They require you to analyze the frequency of load data and frequently use ELT (Extract-Load-Transform) tools

Data Security and Privacy

Some of the best practices for data warehouse security and privacy are intelligent user access control, high and deep encryptions (such as FIPS-40-2), and imposing security protocols for all system variables.

The universally-accepted best practices for database security and privacy are performing data discovery, encryption of data, implementing data usage policies, and applying the Principle of Least Privilege (PoLP).

Data Integration and Transformation

The best data integration and transformation practices for databases are minimizing the divergence of analytics and tools, resilience to accepting new systems and applications, and decoupling the business system and the processing logic.

Opposite to them, the best data integration and transformation practices for data warehouses are incorporating data governance, using cloud ETL warehouses, and implementing iterations and executable test scripts.

Performance Tuning and Optimization

The best tuning and optimization practices for using databases are the following: optimization of queries, defragmentation of data, increasing and boosting database memory, enhancing database indexes, and CPU overhauling.

When it comes to data warehouses, the best tuning and optimization practices include indexing, creating or building various data blocks and views, sharding, data compression, and partitioning.

The Future of Data Warehouses and Databases

Constantly, new technologies are emerging, and they have a great impact on various business sectors. It’s important to be in step with them as most will surely use databases or data warehouses. 

Emerging Trends and Technologies

In recent years, cloud data warehouses are becoming ever more popular. You can utilize their features and optimizations and instantly sync the data from the existing database to the cloud warehouse. 

Other emerging trends and technologies are the data warehouse and the data lake convergence, zero-copy data sharing, and augmented DMBS.

Impact on Businesses and Industries

The impact of data warehouses and databases is already huge on various businesses and industries, and this will only grow. For instance, the cloud and real-time warehouses now allow you to anticipate business and marketing trends. 

The data warehouses and databases will also improve in helping businesses minimize production and decision-making errors, become more flexible, build trust, or tailor efficient and effective business policies.

Future Outlook

With all this said, we can expect a rapid increase in the use of databases and data warehouses in the upcoming years. Technologies like Artificial Intelligence (AI) and Machine Learning will only boost their potential while giving them a competitive advantage for each business.

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