Database vs Data warehouse: a comparison

If your company deals with a lot of transactions and has years of data stored, then you need a database and a data warehouse to store that large volume of information.

But do you really understand these concepts?

Databases and data warehouses are complementary systems that store data but serve different purposes and objectives.

In this post, we will discuss what data warehouses (DW) and databases are, point out their main differences, and explain the importance of these concepts for those who want to expand their business in the age of data.

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What is a database?

A database is the place where real-time information about a specific area of a given company is stored.

They are, therefore, ideal operating environments to perform large volumes of simple and fast queries on an organization's major daily transactions.

Here are some examples of queries performed against conventional databases:

• Room reservations at a hotel
• Sales Records
• Payroll Processing
• Inventory Control
• banking transactions, among others

In other words, the most routine activities of the company are the objects of analysis of the databases.

What is a data warehouse?

Data warehouses are databases that are optimized for high-volume analytical queries. They are made up of the datasets collected and imported from the databases.

Unlike a database, the primary purpose of a data warehouse is to store historical data. That is, this system does not store current information, nor is it updated in real time, like databases.

It is these sets, integrated in one place, that enable analysis and strategic decision-making.

The Differences Between Databases and Data Warehouses

While conventional databases are operational environments, data warehouses are informational environments.

What does that mean?

In the case of databases, it is possible to conduct quick queries in order to carry out daily activities of a business.

Data warehouses, on the other hand, allow for broad and thorough queries with a more strategic goal of understanding the bigger picture of the company.

To make it easier for you to see, we've compiled the main differences between databases and data warehouses in the table below. See:

Comparison Table: Database vs. Data Warehouse

Explain

Each area of the company has its own database for storage and point-in-time querying, while the data warehouse is an integrated database, that is, a place where all business data is stored: a single source of truth.

It is quite common for both to use the same relational database, with the data architecture being the main difference between them (how the entities, tables, relationships are arranged, etc.).

In addition to the differences already discussed, we will explain in greater depth the following distinctions between data warehouses and databases:

1. Processing types: OLTP and OLAP
2. Structure types: normalized and denormalized

1. OLAP vs OLTP

The most significant difference between databases and data warehouses is how they process data.

Routine processing within a database is done by the online transaction processing (OLTP) system.

It records operations, supporting those who perform them to delete, insert, replace, and update a large number of transactions quickly.

For example, if a user wants to register a sale on a company form, the process is performed with OLTP.

Data warehouses, on the other hand, use online analytical processing (OLAP) to analyze large volumes of data quickly.

This process allows analysts to examine data across its entire historical line, meaning it is possible to add up a given piece of data in time since the beginning of that process.

For example, if your database has recorded a sales data per minute and you want to know the total amount sold each day, OLAP shows that.

It is specifically designed to make these general queries. And its data processing is much faster than that of OLTP to perform the same calculation.

2. Structure types: normalized and denormalized

As databases are used in daily operational activities, data is generated from different sources, where it is possible to edit and modify them at will.

As a result, redundancies arise and data integrity suffers.

To reduce these issues, which can be critical to day-to-day analysis results, data models for transactional applications should be normalized.

What does that mean?

The goal of normalization is to reduce and even eliminate data redundancy. This allows for greater consistency and therefore more accurate data, as well as ensuring that the database takes up minimal disk space.

On the other hand, in the case of data warehouses, the data is denormalized.

Why?

Querying a normalized database can be a slow and complex task.

Denormalization comes with the goal of easier access and improving the efficiency of queries in a data warehouse.

Although this generates some redundancy of information, the applications will benefit from the performance gain, since it is not necessary to join several tables, as happens in standardized databases.

Data analysis: databases vs. data warehouses

When analyzing the data, the place of consultation will depend on the objective.

Even though databases only process transactions, it is also possible to perform data analysis with them. However, these are simpler analyses, such as booking a hotel room or a customer's current balance.

This is because the normalization of databases makes more in-depth queries difficult. For this reason, they can only provide a snapshot report of the data at a specific point in time.

To create and execute complex queries in a database would require a lot of time, as well as computing resources and skilled developers.

Thus, data warehouses are designed to perform complex analytical queries on large multidimensional data sets in a straightforward manner.

As a result, DWs provide faster analysis and useful results for the development of company strategies.

In addition, in DW, you can dive deep and see how your data changes over time, while instant reports from databases only provide information for immediate actions.

Do you want to implement a data warehouse in your company?

Here at Indicium, we use the best and most advanced data warehouse tools available on the market, as well as the best professionals who are experts in the field.

Do you want to build a data warehouse with all the data relevant to your business strategy?

Get in touch today and get help from our team.