Everything you need to know about data warehouse + real-world examples

A data warehouse is a centralized database used to store huge amounts of business information and is accessible for analysis and decision-making. 

In short: it’s a database system on steroids for managing and storing data on a large scale, especially at the enterprise level.

In this system, data from different sources are extracted, transformed, and loaded into a data warehouse to make the data accessible and easy to manage. Or to translate it into useful information for a variety of possibilities, both business and technological.

Great, isn’t it?

Now, with the unimaginable amounts of information that there are now, data warehouses are more important than ever, both for businesses and professionals. Let’s take a look at what data warehousing is all about, its various uses, and its differences from the database.

And first of all:

Who runs the data warehouse?

A data warehouse is – or should be – administered and managed by a team of information technology and data science professionals. 

Data architects

Architects design, build, rebuild, and modify the structure of the data warehouse and work on the integration of data from different sources.

In addition, they work on application integration as well as integrations between databases and data warehouses.

Data Engineers

Data engineers are those who work directly with the data, from its integration to its transformation. All are based on the ETL (Extract, Transform, and Load) process.

Data engineers are also in charge of creating and optimizing pipelines, understanding and modifying patterns within the data structure, and executing large-scale analysis. They also create, develop and test prototypes. 

Data analysts

Analysts are the ones who take all the information collected within the data warehouse and its integrated databases. 

They are the ones who convert data into accurate information for business strategies through analysis, reporting, A/B testing, and quality control testing, among other tasks.

Database administrators 

Administrators have the best of both worlds. They are responsible for both software and hardware maintenance. They are the guardians of the data stores and all the information they hold.

In turn, they oversee the tasks inside and outside the data warehouse to ensure its smooth operation, as well as the proper utilization of the data.

Project managers 

Yes, project managers are also indispensable in this area, especially when it comes to global-scale data warehouses.

Project managers are often the link between data scientists, executives, senior management, stakeholders, customers, and other departments within the company. 

As you will see, managing a data warehouse requires a dream team of professionals, all equally important for the data warehouse to work like a well-oiled machine that is impossible to stop. 

Everything is done together to make the most of the data for decision-making in the company.

Data Warehouse vs. Database: differences between Data Warehouse and Database

First of all:

• A data warehouse differs from an operational database because it’s designed to support complex analytics and queries
• Databases are geared towards online interactions and transactions.
• A data warehouse includes historical and consolidated data and is used to identify patterns and trends over time.

That said, let’s take an in-depth look at what differentiates the two systems:

Purpose 

The database collects real-time information for immediate use and for an application’s records. The data warehouse collects, stores, and analyzes huge amounts of historical data for the medium and long term.

In other words, the warehouse is the database of databases. 

However, the purpose of the data warehouse is not just to integrate databases, but to create a super business intelligence system where information sources work in perfect synchrony.

By collecting much more information, warehouses are the right place to make large-scale business decisions for the long term.

Structure 

To simplify:

• Structure of a database: based on individual applications, transactions and interactions.
• Structure of a Data Warehouse: oriented towards a specific business process, such as the transactional system, the server network, and cybersecurity. 

Databases have specific structures for more specific purposes. Whereas data warehouses have more complex structures for more complex global processes.

Volume

The data warehouse collects colossal amounts of information, in contrast to the database. 

The latter is limited to application data, while the Data Warehouse can collect and analyze historical data from all applications and enterprise systems.

Loading process 

Loading data into a warehouse is a complex process that requires: 

• The integration of data from multiple sources.
• Transforming the data to ensure that it is useful for analysis. 

Loading data into the warehouse is complex and requires a well-integrated system with multiple sources of information. In this system, the data is not entered in real-time, but when analyzing all the systems.

Otherwise, the data collected for analysis could be, at best, inaccurate and unreliable.

On the other hand, databases collect and analyze information from your applications in real-time.

Design

Warehouses are designed to read and document complex analyses, but not to manipulate the data collected; this is the job of databases.

Because they are designed for complex analytics, data warehouses are more secure for storing information. And much more accurate for business analysis and decision-making.

Users

Users of a data warehouse are typically analysts, data scientists, engineers, executives, and developers. 

Databases, on the other hand, are accessible to developers, programmers, and even embedded applications.

Scalability 

Data warehouses can scale horizontally. Simply put, you can add servers and server networks to increase the size and capacity of the warehouse.

Conversely, the database expands within the same server, in what is known as a vertically scalable system.

Structures of a data warehouse

Data sources

The data warehouse collects data from various sources, such as operating systems, transactional databases, servers, computers, enterprise applications, sensors, IoT devices, and other systems.

ETL Processes

All information must be processed, transformed, and cleansed before moving on to data analysis. 

This is where engineers perform extract, transform, and load (ETL) processes to extract data from different sources, transform it into a standard format and load it into the Data Warehouse.

In other words, this is where they translate the data and convert it into information that is easy to understand, manage, analyze, and exploit when making business decisions.

Data Warehousing

The storage structure is composed of dimensions and facts. 

• Dimensions: are attributes that describe the context in which an event occurred, such as date, location, product, customer…  
• Facts: these are numerical measures that describe an event, such as sales, revenue, quantities… 

This structure guarantees you a fast and accurate analysis of all sources of information. Although the data are not analyzed in real-time, you can read and consult them whenever you prefer. 

Analysis tools

To analyze the information in the Data Warehouse, you need advanced tools for data analysis, such as data visualization software, business intelligence (BI) software, and languages such as SQL and R, two of the preferred languages for large-scale data science.

Metadata

Metadata is the data used to explain the data – quite a tongue-twister.

In other words, it’s the data that explains the origin, structure, modifications, correlation, and other details of the data within the warehouse and its component systems. 

Metadata is indispensable for understanding and analyzing all warehouse documentation, as well as its relevance within the business strategy.

Who uses Data Warehouse?

Now, we’ve talked enough about technical definitions. Let’s now see what data warehouses translate into in the real world.

Here are 4 business models where data warehouses are indispensable:

1. E-commerce

Let’s say you own an e-commerce site.

In this case, the Data Warehouse would be essential to collect and analyze transaction data, purchase histories, and customer preferences, among others. 

This way, you can understand:

• Which products are generating the most money.
• Which items are not selling well compared to others. 
• Which strategies generate better results.
• What are the trends of your shoppers before, during, and after they make a purchase.

This allows you to better understand your customers and improve the shopping experience.

2. Financial Services 

Financial institutions use Data Warehouses to store and analyze large volumes of financial data, including transaction records, credit histories, market data, and more. 

This includes:

• Consultancies.
• Finance departments.
• Banks.
• Investment funds.
• Government agencies.
• Ministries.

This allows them to identify patterns and trends, detect fraud and improve business decision-making.

3. Health

Medical records, insurance data, test results, epidemiological studies… everything goes through Data Warehouses. And in a sector as important as healthcare, these systems are vital. 

Thanks to data warehouses, healthcare specialists can:

• Improve diagnosis and treatment.
• Preserve reliable records for each patient.
• Identify health risks and improve patient care.
• Study disease outbreaks, epidemics, and pandemics.
• Conduct drug and treatment research.

4. Marketing 

In the world of marketing, information is absolutely everything, from consumer behavioral data to KPIs. Without this information, understanding your target audience, your market position, and your efficiency rates is an impossible task.

Data warehouses are essential in this area to analyze:

• The performance of marketing campaigns and strategies.
• Customer behavior.
• Market statistics.
• Positioning in networks and search engines.
• Productivity indexes.
• Social media data.
• Data from previous years (essential for internal benchmarking).

Just to mention a few examples.

Examples of Data Warehouse in the real world

Now, let’s look at examples of companies that use this model for their operations and would collapse without them – and that also offer data warehousing services: 

1. Amazon – Amazon Redshift

Amazon Redshift is Amazon’s cloud-based Data Warehouse service. It’s Amazon Web Services’ largest cloud storage system, and one of the largest data warehousing systems available today.

This system is completely SQL-based and is preferred by thousands of companies around the world.

2. Google – Google BigQuery

Because Google could not be left behind.

Like Amazon Redshift, this is a cloud service for colossal data analysis via SQL, among other tools. 

BigQuery is a favorite not only for data analysis applications, but for Machine Learning, Deep Learning, and complex real-time data analysis.

3. IBM – IBM Db2 Warehouse

IBM has always been a pioneer in computing, but also in large-scale data warehousing. Therefore, it offers one of the best and most complete services in this area.

IBM Db2 also works with SQL and can be integrated with other database tools. Like Amazon RedShift, it’s in high demand by mid to large sized companies for enterprise applications, complex analytics and visualization, and global reporting.

Data warehouses are the difference between small businesses and global enterprises. If you have the resources and are looking to increase your company’s efficiency to stratospheric levels through data, data warehouses are the right choice. 

And if you are looking to specialize in this field, there are endless career opportunities awaiting you. 

Data Warehouses are already indispensable in business intelligence, which is why it’s already considered one of the sectors with the brightest future. 

Did you want to know more? Leave us your comment and we will gladly answer your questions. 

And feel free to explore our blog for more information about business intelligence and digital transformation.