Data warehouse is essential to further improve the efficiency and profitability of your business.
With the wealth of information available online at any given time, companies are developing a culture of data to improve decision-making and develop more effective actions.
To enable this, you need to collect, organize and analyse this information. Before you can do this, you need a system to store and aggregate the data collected, such as a data warehouse, but what is it?
What Is A Data Warehouse?
A data warehouse is a data management system designed to enable and support business intelligence activities, in particular advanced analytics. They are used exclusively to perform advanced queries and advanced analytics, but they can also store more historical information about the business and can include process logs, among other things.
This allows all information to be organised in a way that provides companies with very useful data to develop better strategies to improve business performance.
The data warehouse is therefore one of the largest sources of information within the company.
What Are The Main Characteristics Of A Data Warehouse
A data warehouse is characterized by being an active system of data mining and processing to meet specific purposes.
It is different from data lakes, which are repositories of unstructured data at low cost and without a particular application.
Among its main features, we highlight:
In a Data Warehouse, relational data from transactional systems, business-oriented applications and operational databases are compiled:
- The data must be of quality and organized
- Enables faster queries, thanks to local storage technology
- Can generate reports in batch, as per the Business Intelligence (BI) concept
- End users are generally data scientists, business analysts or data developers.
- The elementary architecture of a data warehouse is based on different online or networked data sources.
From these, a so-called “datastage area” is implemented, in which information is collected and filtered – and also where redundancies are eliminated.
This area is interconnected to a data mart, whose function is to perform a new data filtering to send it to the tools used by the end user.
What Are The Types Of Data Warehouses
Although the structure of data warehouses varies from company to company, they can be broadly classified into four types.
In other words, depending on the intended use of the data, they can be organised as a data warehouse into one of the following types – some even combine these four models.
1- Integrated
The primary function of an integrated data warehouse is to create consistent relationships between data from different sources.
They can consolidate information from different systems so that it can be further processed in a single system.
2- By Subject
On the other hand, data warehouses organised by subject are those that meet business objectives in a given context.
For example, an accounting department that has to register and record various customers and taxpayers, as well as the taxes to be calculated and collected.
2- Variable Over Time
For data characterised by variable over time, data mining sources that use one or more time periods as a baseline are used.
Therefore, data mining is not used in real time, like OLTP (online transaction processing) banks.
4- Non-Volatile
Data in data warehouses is always ready for further processing.
This means that they must go through deletion and retrieval processes where they are modified before being used by the end user.
This makes them static, i.e. non-volatile.
But What Exactly Is A Data Warehouse For?
Like a warehouse, a data warehouse helps to bring together or integrate data from different sources for easy use by business managers and data analysts.
These main sources include ERP, spreadsheets, CRM and others. Information can be extracted from these sources in a variety of formats, including database languages such as SQL, XML, TXT and many others.
Once extracted, this information is stored in a repository that is reserved exclusively for data standardization and even business quality assurance processes, which brings many benefits to the business organization.
What Are The Main Advantages Of Using Data Warehouses In Businesses?
Now that you understand what the data warehouse is and its types, and what it is used for, we will point out the main advantages of having a data warehouse in businesses.
See what they are:
- Agility in queries: data warehouse systems are not only capable of storing data, but are a complete solution for companies that frequently deal with information.
- Increased data processing capacity: with the expansion of cloud computing, the storage and processing capacity of data warehouse systems has been greatly increased.
- Access to historical data: when it is necessary to have a historical reference to perform an online operation, data warehouses prove to be even more valuable as they work with OLTP systems.
- Gives a competitive advantage: With data warehouses, companies can get a holistic view of the current situation and assess opportunities and threats, giving them a competitive advantage.
- Centralization of data: another important advantage is that they operate from centralized data compiled in a single repository.
- Facilitates the flow of information:Data warehouses allow information to flow through a network that connects all connected and disconnected parties.
Basic Elements Of The Data Warehouse
Below we can see the basic elements that make up the architectures of a Data Warehouse.
Data Stage
Composed of a storage area and a set of processes. Its function is to extract data from transactional systems, proceed to cleaning, transformation, combination, duplication and preparation of data for use in the Data Warehouse. This data is not presented to the end user.
Presentation Server
Environment where data is organized and stored for direct consultation by end users. Typically data is available on these servers in relational databases, but can also be stored in OLAP technology (OnLine Analytical Processing) since many data marts work only with data in the dimensional model.
Data Mining
Also known as data mining, Data Mining works on large masses of data where there are many correlations between the data that are not easily noticeable. Data warehouses usually consist of huge amounts of data, there is a need for a tool to automatically scan the data warehouse in order to search for trends and patterns through pre-defined rules that would hardly be found in a common search.
Data Source
Transactional systems of the company can be composed of various forms of data.
Data Mart
Logical subset of the Data Warehouse, usually divided by department or views needed by users.
What Is The Difference Between Data Warehouse And Database?
From everything we have seen so far, we can say that the data warehouse is an information system that stores historical and relational data from single or multiple sources.
It is designed to analyze, report and integrate transaction data from different sources.
DW facilitates the analysis and reporting work of a company and is also the primary source to guide the decision-making and forecasting process.
The database is a collection of related data that represent some aspects of the real world and is designed to record such elements.
So, can point to some differences between these two resources:
- The database is designed to record data, while the data warehouse is designed to analyze them.
- The database is an application-oriented data collection, while the data warehouse is the subject-oriented data collection.
- The former uses Online Transactional Processing (OLTP), while DW uses Online Analytical Processing (OLAP)
- The database is designed using Entity Relationship Diagram (ERD) modeling techniques, while the data warehouse uses data modeling techniques to design.
What To Expect From The Data Warehouse?
With the increasing integration of business intelligence, machine learning and artificial intelligence solutions and functions, the future trend of data storage will become more intuitive.
This can be expected from the new concept of Data Warehouse 2.0, where the most advanced architecture treats data as if it were in a lifecycle.
The growing use of cloud computing is also a very strong trend.
Enterprises are turning to cloud storage technologies for efficiency, security, scalability and ease of use.
In the future, data warehouses are expected to become true integrated analytical ecosystems.
Analytics processes and projects will be based on different types of data (transactional, event and reporting data) from business systems and databases, as well as from big data sources.
Therefore, in the future, data from data warehouses will need to be integrated into the analytics ecosystem and work with the data warehouse to provide the complete data set required for analysis.
Conclusion
In this article, we have learned about the uses, advantages, types, definitions, features, and key elements used to build data warehouses for business.
Want to put this knowledge into practice but don’t know how? We can help.
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