Data Warehouse vs. Database: key differences

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For companies of all sizes and industries, the world of big data keeps growing. More and more companies are looking at new analytical tools to tackle a range of business goals, but turning a lot of raw data into state-of-the-art information is no easy task.Perhaps the two most frequently used forms of data storage for data management are data warehouses and databases. But what is it, you’re likely to ask – and we have the answer here!Databases and data warehouses are systems that store data, but their uses are very different. In this post, we will discuss how they function, the key distinctions between them, and also why using them effectively is critical for your business’s growth. We’ll start with broad definitions, and then we’ll get into more detail. 

What is a Data Warehouse?

A data warehouse is a system that gathers data from multiple different sources within an organization. It stores your business’s historical data and enables it to be used for reporting and analysis. Reports created based on complex queries in a data warehouse are used to make operational decisions. However, it is important to note that the data warehouse does not store up-to-date information and is not updated in real time.

What is a Database?

A database is a logically arranged collection of data that makes it more convenient to find, retrieve, process, and analyze data. It usually stores real-time information and captures data from a single source, such as a transactional system. The primary task of the database is to process the day-to-day transactions that your business makes, for example, registering items that have been sold. It can handle a massive volume of simple queries very quickly, however, it is not suitable for more in-depth analysis.

Data Warehouse vs. Database

Let’s look at the fundamental distinctions between data warehouses and databases. 
PropertyData WarehouseDatabase
UseData analysisData collection
Processing MethodOnLine Analytical Processing (OLAP)OnLine Transaction Processing (OLTP)
OptimizationAnalyze huge amounts of data quickly and give analysts several perspectives.Deletes, replaces, updates and inserts a huge number of brief online transactions in a short period of time.
Data structureThe data structure is denormalized, with just a few tables storing repetition data.As a result, data may be less precise yet more quickly retrieved.The data structure is highly normalized, with several separate tables having no duplicated data.As a result, data is more precise yet slower to access.
Data timelineHistorical data across all aspects of the project.Real-time data regarding one aspect of the project.
Data analysisAnalysis is efficient and simple given the limited amount of table joins required and the vast time frame of available data.Analysis is slow and unpleasant due to a large amount of table joins required and the limited time frame of available data.
ACID complianceSometimes it is not ACID-compliant even though some companies offer it.Always ACID-compliant to ensure the highest levels of integrity.
Concurrent usersA few concurrent users.Thousands of concurrent users supported. But, only one user can modify each piece of data at a time.
UptimeDowntime is included to allow periodic data uploads.99.99% uptime
StorageAll data sources from all business functionsLimited to a single data source from a particular business function
Query typeComplex queries for in-depth analysisSimple transactional queries

Processing Types: OLAP vs OLTP

The biggest difference between databases and data warehouses is how they deal with data.Databases use Online Transactional Processing (OLTP) to quickly delete, insert, replace and update many short online transactions. This type of processing immediately answers users’ requests, and is therefore used to process a company’s daily activities in real time. For example, if a user wants to book a hotel room using an online booking form, this is done with OLTP.Data warehouses use online analytical processing (PALO) to quickly analyze massive volumes of data. It allows analysts to look at your data from multiple perspectives. For instance, even if your database records sales data for every minute of every day, you may simply want to know the total amount sold each day. This is done by collecting and summing sales data for each day. OLAP is specifically designed for this purpose and its use to store data is considerably faster than using OLTP to perform the same calculation.


A database is designed to update (add, alter, or delete) data as quickly and efficiently as possible. To properly handle transactions, database response times should be exceedingly fast. The most important thing about a database is that it logs every operation into the system. A company simply won’t be in business for long if its database hasn’t made a record of every purchase!Data warehouses are optimized to quickly execute a small number of complex queries over large, multi-dimensional data sets.

Data Analysis

While databases usually only process transactions, it is also possible to conduct historical data analysis with them. However, deep scanning is difficult for both the user and the computer due to the predefined data structure. Complex searches in a database management system (DBSM) demand the expertise of a qualified developer or analyst, along with more time and resources. Moreover, the analysis is not thorough – the best you can get is a one-time static report, as the databases only provide a snapshot of the data at a specific time.Data warehouses are designed for performing complex analytical queries on large multidimensional data sets in a simple way. It does not require any particular education or additional resources to operate, and the analysis is straightforward to perform. In addition, you can delve deep and see how your data evolves over time, rather than checking the snapshot that databases provide. 

Data Structure

Database information is standardized. The purpose of standardization is to reduce or even eliminate data redundancy, in other words to store the same data more than once. This double data reduction leads to greater consistency and therefore more accurate data as the database stores in one place.Data standardization divides them into a bunch of tables. Each table has a unique piece of information. A database documenting BOOK SALES, for example, may have three tables indicating information on the BOOK, the TOPIC covered within that book, and also the AUTHOR.Data normalization ensures that the database takes up minimal disk space and is therefore memory efficient. However, it is not query efficient. Because businesses want to perform complex queries on data in their data warehouse, these data are often unstandardized and contain repeated data for easier access.

Data Timeline

Databases process routine operations for a part of the business. As a result, they generally contain current data as opposed to historical data on a business process.Data warehouses are employed in operational analytics and reporting. Data warehouses typically store historical data by combining copies of transactional data from various sources. Real-time data flows can also be used in data warehouses for reports which use the most up-to-date embedded information.

Concurrent Users

Because databases are updated in real-time to reflect the business’s transactions, they should be able to handle thousands of concurrent users. As a result, a large number of users must be able to interact with the database at the same time without compromising its performance.However, only one person can alter a piece of data at a time; it would be extremely damaging if two users simultaneously overwrote the same data in different ways!Data warehouses, on the other hand, can only accommodate a limited number of concurrent users. A data warehouse is autonomous from front-end apps, and using it necessitates the creation of complicated queries and their execution. Since these inquiries are resource-intensive, only a limited number of people may use the system concurrently.

ACID Compliance

Database transactions are typically executed according to ACID (Atomic, Consistent, Isolated, and Sustainable) standards. This compliance guarantees that data updates are made in a secure and dependable manner. As a result, even in the event of errors or other malfunctions, it may be relied upon. Since the database is a register of business operations, it must ensure that the data are recorded accurately.ACID compliance is less mandatory in data warehouses as they focus on reading, rather than modifying, historical data from a variety of sources. On the other hand, top cloud service providers like Redshift and Sage Data go to great lengths to guarantee that their queries are ACID compliant. This is always true while using MySQL and PostgreSQL, for example.

Data Warehouse Use Cases

Use cases include:
  • Segmenting customers into distinct categories depending on their previous purchases in order to present them with more personalized content.
  • Predicting client retention using sales data from the previous ten years.
  • Developing demand and sales estimates to determine which areas to concentrate on in the upcoming quarter


Database Use Cases

Some examples of database applications include:
  • An online sales site places an order for a product that has already been sold.
  • An airline that uses an online reservation system
  • A patient is being registered at a hospital.
  • An ATM withdrawal operation is added to an account by the bank.


Integrate your Data Warehouse today!

Now you understand the difference between a database and a data warehouse and when to use which one. Your business needs both an effective database and data warehouse solution to truly succeed in today’s economy.Sage Data is a secure place to store, sync, and access all your business data. Your account with Sage Data can be set up in minutes, requires minimal on-going maintenance, and provides online support, including access to experienced data architects. Free 60-Day Proof of Value.Getting started is easy! Get all your data in one place in minutes.time and data are your biggest assets
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