For which task is a data mart more useful or appropriate than a data warehouse?

Seven critical differences between data warehouses vs. databases:

1. Online transaction process (OLTP) solutions are best used with a database, whereas data warehouses are best suited for online analytical processing (OLAP) solutions.

2. Databases can handle thousands of users at one time. Data warehouses can only handle a smaller number.

3. Databases are most useful for small, atomic transactions. Data warehouses are best suited for larger questions that require a higher level of analysis.

4. Databases need to be available 24/7/365, meaning downtime is costly. Data warehouses aren’t as affected by downtime.

5. Databases are optimized to be lightning-quick for the CRUD operations (create, read, update, and delete). Data warehouses are optimized for a smaller number of more complex queries over multiple large data stores.

6. Databases are structured as efficiently as possible, with no duplicate information in multiple tables. Data warehouses typically denormalize their data, prioritizing read operations over write operations.

7. 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.

Every business needs an appropriate way to save and analyze data about its operations, customers, and performance. Knowing the differences between data warehouses and databases should help you make an informed choice that positions your organization for success.

You often have to look at a broad range of factors when deciding whether you need a database, data warehouse, or both. Insight from a professional can help. Schedule a consultation with Integrate.io to learn more about how our system can resolve your data storage and data analysis challenges.

Table of Contents

1. What Is a Database?
2. What Is a Data Warehouse?
3. Major Differences Between Databases and Data Warehouses
4. Data Warehouse vs. Database Comparison Chart
5. Key Differences Explained
6. Turning Raw Data Into Useful Information
7. Data Warehouses & Databases vs. Data Marts & Data Lakes
8. Database & Data Warehouse Integrations

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 impossible to imagine, you might think of it as enough information to fill 13,000 terabyte drives. That’s 13,000 TB drives filled every second. If you want your mind blown again, try to think of it as 1.123 billion TB drives per day.

You can’t fathom this much information, and you certainly can’t find meaningful patterns within such an enormous dataset. Your business’s success relies on it, 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.

How To Use Databases

Perhaps the most common way of classifying databases is SQL vs. NoSQL (also known as relational vs. non-relational).

A SQL, or relational database, organizes 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, in no particular order, are Oracle, Microsoft SQL Server, IBM Db2, and MySQL.

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 uses dynamic schema, so it gives you 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 the 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 the 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.

On the other hand, 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. You can 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. 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 can vary significantly from service to service, so make sure you compare your options before choosing a cloud database 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.

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. 

Integrate.io's tools make it easy for you to connect data sources to your data warehouse. Talk to an expert today to learn more about how Intrgrate.io helps you focus on insights instead of spending time on tasks like data processing.

Major Differences Between Databases and Data Warehouses Explained

The main difference is that databases are organized collections of stored data. Data warehouses are information systems built from multiple data sources — they are used to analyze data. 

Below are some more distinctions that further differentiate databases and data systems at a high level.

Data Warehouse vs. Database Comparison Chart

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 fast, efficient queries and information that’s up-to-date and accurate, which OLTP is specifically designed to enable.

OLAP (online analytical processing) is a term for a data processing system that focuses on data analysis and decision-making rather than performance and day-to-day use. Many OLAP systems are connected with business intelligence (BI) solutions that make it easier for non-technical managers and executives to get answers to their questions.

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 similar large-scale analytics.

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.

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 more 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.

5. Optimization

OLTP databases are optimized to be lightning-quick for the 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.

6. Structure

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 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 over write operations.

7. Reporting and Analysis

Some limited reporting and analysis is 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 capture data change (CDC) features also help ensure that you have updated information. You can't generate helpful insights with outdated data.

You can learn more about the benefits of reverse ETL, low-code ETL pipelines, fast CDC features, and deep e-commerce capabilities by scheduling a consultation with Integrate.io. 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 marts and 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.

Database & Data Warehouse Integrations

The question of data warehouses vs. databases (not to mention data marts and data lakes) is one that every business using big data needs to answer. As we’ve seen above, databases and data warehouses are quite different in practice. 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) and offers a cloud-based, visual, and low-code interface that integrates with data warehouses and databases. Schedule a call to arrange for a demo, a seven-day pilot, and a complimentary session with our implementation team.