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:
A data warehouse is - or should be - administered and managed by a team of information technology and data science professionals.
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 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.
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.
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.
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.
That said, let's take an in-depth look at what differentiates the two systems:
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.
Databases have specific structures for more specific purposes. Whereas data warehouses have more complex structures for more complex global processes.
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 data into a warehouse is a complex process that requires:
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.
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 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.
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.
The data warehouse collects data from various sources, such as operating systems, transactional databases, servers, computers, enterprise applications, sensors, IoT devices, and other systems.
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.
The storage structure is composed of dimensions and facts.
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.
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 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.
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:
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:
This allows you to better understand your customers and improve the shopping experience.
Financial institutions use Data Warehouses to store and analyze large volumes of financial data, including transaction records, credit histories, market data, and more.
This allows them to identify patterns and trends, detect fraud and improve business decision-making.
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:
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:
Just to mention a few examples.
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:
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.
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.
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.
Looking for something specific?
