Data Warehouse and Database are two commonly used terms in the field of data management, but they serve different purposes.
Here are 7 critical differences between data warehouses vs. databases:
- Online transaction process (OLTP) solutions are best used with a database, whereas data warehouses are best suited for online analytical processing (OLAP) solutions.
- Databases can handle thousands of users at one time. Data warehouses generally only handle a relatively small number of users.
- Databases are most useful for small, atomic transactions. Data warehouses are best suited for larger business queries that require a higher level of data analytics.
- Databases need to be available 24/7/365, often for business-critical daily tasks, meaning downtime is potentially costly. Data warehouses aren’t as affected by downtime, and even scheduled downtime doesn’t necessarily impact ROI directly.
- Databases are optimized to be lightning-quick for CRUD operations (create, read, update, and delete). Data warehouses are optimized for a smaller number of more complex queries over multiple large data stores.
- Databases are structured as efficiently as possible, with no duplicate information in multiple tables. Data warehouse information is typically denormalized, prioritizing read operations ahead of write operations.
- Databases typically contain only the most up-to-date information, which makes historical queries impossible. Data warehouses have been designed from the ground up for reporting and analysis purposes using as much historical business data as is relevant.
In summary, while databases are used to manage transactional data for operational purposes, data warehouses are used to store and analyze large amounts of data for strategic decision-making.
Every piece of data, from website clicks to sales and inventory reports, can inform decision-making and drive business growth. Understanding the distinctions between databases and data warehouses can help you make an informed decision on how to manage your data, positioning your organization for success.
Table of Contents
- What Is a Database?
- What Is a Data Warehouse?
- Major Differences Between Databases and Data Warehouses Explained
- Data Warehouse vs. Database Comparison Chart
- Those 7 Key Differences Explained
- Turning Raw Data Into Useful Information
- Data Warehouses & Databases vs. Data Marts & Data Lakes
- Create Better Database & Data Warehouse Integrations With Integrate.io
The average person generates about 1.7 MB of data per second. With a world population of about 7.753 billion people, that means humans make at least 13 billion MB of data every second of the day. Since that’s virtually impossible to imagine, you might think of it as enough information to fill 13,000 terabyte drives. That’s 13,000 TB storage hard drives filled every second. If you want your mind blown again, try to think of it as 1.123 billion TB drives per day.
Most people simply can’t fathom this much information, and certainly can’t find meaningful patterns within such an enormous dataset. Your business’s success relies on exactly these huge volumes of big data being analyzed, though. So, what can you do?
Turning this data into cutting-edge insights doesn’t come easy. It requires businesses to master enterprise data management so employees can easily create, store, access, manage, and analyze the information they need to excel at their jobs.
Perhaps the two most common forms of data storage in enterprise data management are data warehouses and databases. What’s the difference between a database and a data warehouse, and which one is best for your situation?
Here, we’ll break down the differences between databases and data warehousing so you can determine which is best for your data structure situation.
What Is a Database?
A database is an organized collection of information stored in a way that makes logical sense and facilitates easier search, retrieval, manipulation, and analysis of data. Databases are essential for storing information, often on similar topics or similar types of data, that can be retrieved as needed to perform business tasks for sales, HR, marketing, customer service, and a range of other requirements. Databases use various different schemas to organize or structure data in ways best suited to the task at hand.
SQL vs. NoSQL Databases
Perhaps the most common way of classifying databases is SQL vs. NoSQL (also known as relational vs. non-relational).
A SQL database uses Structured Query Language and is a type of relational database. Relational databases organize information within formal tables that codify relationships between different pieces of data. Each table contains columns and rows, similar to the structure of a spreadsheet in Microsoft Excel. When using a relational database, you can create a conceptual, logical, or physical schema that defines relationships between the data in your database.
To search through a relational database, users write queries in Structured Query Language (SQL), a domain-specific language for communicating with databases.
The four most popular SQL database products as of February 2023 are Oracle, MySQL, Microsoft SQL Server, and PostgreSQL.
On the other hand, a NoSQL, or non-relational database, uses any paradigm for storing data that falls outside the relational table-based data model. NoSQL databases use dynamic schema, which can provide businesses with a more flexible way of storing and accessing data.
Some common types of NoSQL databases are key-value, document-based, column-based, and graph-based stores. Popular NoSQL offerings include MongoDB, Cassandra, and Redis.
In terms of SQL vs. NoSQL question, both approaches have their pros and cons. SQL databases tend to be easier to scale vertically by adding more resources, while NoSQL databases tend to be easier to scale horizontally (by adding more machines). The use of SQL to write queries can be a significant advantage for performance and ease of use, but relational databases are also less flexible and more rigid in terms of data hierarchy.
Cloud Data Warehouses and Databases
Some cloud databases offer a mixture of SQL and NoSQL features. For example, Amazon Redshift is built on technology developed by a data warehouse company that wanted a solution capable of moving large-scale data sets quickly. This makes it resemble a NoSQL database; however, as a cloud-based data warehouse solution, Redshift also has a Postgres-compatible query layer. Redshift can organize data by relational schema, which makes it resemble a SQL database.
Whether they fit into the SQL or NoSQL category, cloud databases usually offer the advantage of rapid scaling. Traditionally, businesses had to maintain on-site equipment and infrastructure to house a database. Doing so means you only have access to the amount of space your hardware can handle. On top of this, when equipment wears out or operational systems become redundant, the cost has to be shouldered by the business. Cloud databases have so much space that you can practically scale indefinitely. Depending on your contract agreement, you should find that you can scale as needed without paying excessive fees.
Prices for both data warehousing and databases can vary significantly from service to service, so make sure you compare your options before choosing a cloud-based data management provider.
Related Reading: Which Modern Database Is Right for Your Use Case?
What Is a Data Warehouse?
A data warehouse is a system that aggregates and stores information from a variety of disparate sources within an organization. Data warehouses are often the hub for business intelligence (BI) and are connected to BI tools for in-depth data analysis and reporting.
How To Use Data Warehouses
The goal of a data warehouse is explicitly business-oriented: it is designed to facilitate decision-making by allowing end-users to consolidate and analyze information from different sources. Data is usually collated by one of several processes:
- ETL stands for extract, transform, load, and takes data from various sources, holds it in a staging area for cleansing and transformation to a common format, then loads the data into the data warehouse.
- ELT is similar but needs no staging area as the data transformation is done at the end of the process
- CDC means change data capture and is essentially the same process as ELT, often automated to pull data through whenever changes occur, such as a customer updating their contact details.
- APIs can be used to link to very specific sources of data, including individual tables within existing databases.
Integrate.io’s innovative data integration platform provides access to all these methods of data integration, making it easy for you to connect virtually any business data source to your data warehouse. Talk to an expert today to learn more about how Integrate.io helps you focus on insights instead of spending time and money on tasks like data processing and manually coding data pipelines.
Major Differences Between Databases and Data Warehouses Explained
The main difference when it comes to a database vs. data warehouse is that databases are organized collections of stored data whereas data warehouses are information systems built from multiple data sources and are primarily used to analyze data for business insights.
Below are some more distinctions that further differentiate databases and data systems at a high level.
Data Warehouse vs. Database Comparison Chart
Some scheduled downtime
For CRUD operations
For complex analysis
Real-time detailed data
Summarized historical data
Those 7 Key Differences Explained
We’ve provided a broad overview of databases and data warehouses, but how exactly do they differ in the specifics? Below, we’ll discuss seven of the biggest differences between data warehouses and databases.
1. OLTP vs. OLAP
OLTP (online transaction processing) is a term for a data processing system that focuses on transactions. This is usually the dominant paradigm for databases that contain information used by a business on a day-to-day basis. Employees need information that’s up-to-date and accurate to deal with day-to-day business queries fast, which OLTP is specifically designed to enable.
OLAP (online analytical processing) is a term for a data processing system that focuses on data analysis to power decision-making rather than performance and day-to-day use. Many OLAP systems are connected with business intelligence solutions that make it easier for non-technical managers and executives to get answers to their questions or provide in-depth reports to business stakeholders.
Businesses that need an OLTP solution for fast data access typically make use of a database. Meanwhile, data warehouse systems are better suited for an OLAP solution that can aggregate current data as well as historical information for data scientists, BI tools, and various large-scale analytics use cases.
2. Number of Concurrent Users
Because databases are OLTP systems, they have been designed to support thousands of users or more at the same time without any degradation in performance.
OLAP data warehouses, on the other hand, can support only a relatively limited number of concurrent users. Because a data warehouse solution uses more complex queries circulating over many different data stores, it necessarily requires more resources and therefore is not as scalable as an enterprise-class database.
3. Use Cases
In terms of their use cases, data warehouses and databases are also quite different.
Databases are most useful for the small, atomic transaction data required for the day-today-functioning of an organization. Some examples include a hospital entering new data about a new patient, a customer purchasing tickets via an online website, and a bank transferring money between two accounts.
Data warehouses are best suited for larger questions about an organization’s past, present, and future that require a higher level of analysis: for example, mining information from multiple databases to uncover hidden insights about customer behaviors and buying trends.
4. Service Level Agreements
As a consequence of their OLTP transactional nature, databases generally need to be available almost 24/7/365, somewhere upward of 99.9% of the time. Downtime for OLTP databases can be extremely costly and even bring the business to a standstill.
However, downtime is not such a major concern for data warehouses because they are used primarily for back-end analysis. In fact, most data warehouses have regularly scheduled downtime windows when more information is uploaded. The opportunity for downtime benefits everyone because it increases the speed of uploads during hours when users would rarely need access to information. You get a faster, more precise process by shutting down everything other than essential tasks.
OLTP databases are optimized to be lightning-quick for CRUD operations. However, more complicated analytical queries can rapidly bring down their performance.
OLAP data warehouses are optimized for a smaller number of more complex queries over multiple large data stores. Although response time remains an important metric, the more important concern for a data warehouse is the quality of the analyses that it performs.
In order to achieve their goal of rapid queries, OLTP databases are structured as efficiently as possible, with no duplicate information in multiple tables. This lowers both the disk space (or cloud storage space) needed and the response time required to execute a transaction.
Redundant information is far less of a concern with OLAP data warehouses since they devote less attention to the speed of a given query. Data warehouses typically denormalize their data, prioritizing read operations ahead of write operations.
7. Reporting and Analysis
Some limited reporting and analysis are possible on OLTP databases, but the normalized structure of the data makes it more difficult to perform. In addition, databases typically contain only the most up-to-date information for maximum efficiency, which makes historical queries impossible.
Data warehouses, on the other hand, have been designed from the ground up for reporting and analysis purposes. Users can pull from both current and historical data, enabling a wider range of insights.
Turning Raw Data Into Useful Information
Databases and data warehouses serve as reliable destinations where you can store information from numerous sources. Simply putting information into a storage system doesn't give you insights into your business, though. How do you go about turning raw data into useful information that improves workflows, business processes, conversions, and other KPIs?
Most organizations reach these goals by connecting their databases and data warehouses to business intelligence (BI) applications. Integrate.io makes it easy for you to build a business intelligence system with ETL. The platform's super-fast change data capture (CDC/ELT) features also help ensure that you have up-to-date information, utilizing automation to draw data whenever relevant changes occur. This combination of no-code methods for data pipeline creation empowers businesses to achieve complete data observability and complete data integrity, unifying all insights for a single source of truth.
Whether you need to process large amounts of data to improve your app's performance or employ a data science expert who wants to use data mining to predict future trends in your industry, you get better results when you rely on Integrate.io.
Data Warehouses & Databases vs. Data Marts & Data Lakes
If you thought that the question of databases vs. data warehouses was all there was to know in enterprise data management systems, think again. In this section, we’ll quickly go over two other alternatives to databases and data warehouses that may be of interest to your organization: data martsand data lakes
Data Mart Definition & Uses
A data mart is a database that is oriented toward storing information of a particular type or for a particular set of users within an organization: for example, marketing, sales, finance, or human resources.
Data marts may be their own entity, or they may be a smaller partition as part of a larger data warehouse. In either case, the goal is to pare down an organization’s data into a more manageable size, usually less than 100 gigabytes.
Data Lake Definition & Uses
A data lake is similar to a data warehouse but without strict requirements for organizing the contents. Data lakes are a method of centralized data storage that does not necessarily structure the information in any type of way. Both structured and unstructured data can be stored together, and the data lake can use information from any source or data type.
Since data lakes are a bit of a “dumping ground” for both current and historical information, they are generally more flexible and adaptable than a structured database. However, this comes at a cost later on when developers and analysts want to process and use these large volumes of information.
Create Better Database & Data Warehouse Integrations With Integrate.io
The question of data warehouses vs. databases (not to mention data marts and data lakes) is one that every business will consider when it comes to managing big data. As we’ve seen above, databases and data warehouses are quite different in practice, and most businesses will use multiple databases plus a reliable data warehouse. Deciding to set up a data warehouse or database is one indicator that your organization is committed to the practice of good enterprise data management.
If you’re suffering from any kind of data integration bottleneck, Integrate.io automates ETL processes (extract, transform, load), reverse ETL, and ELT to help you bring all your business data together, no matter what data warehouse solution you choose. Scale effortlessly with our cloud-based, visual, and no-code interface that integrates with all your data sources and destinations.
Book a demo to try it for yourself, and remember to schedule a call with our friendly team so you get a tutorial and any assistance to get the most out of your 14-day free trial. Get better data warehouse insights by connecting to all the data that matters for your business.