Data Warehouse definition: what is it?

Data Warehouses or data warehouses are databases that store structured, non-volatile, subject-oriented historical data for analysis. Learn more about the definition, benefits, and use cases of Data Warehouses.

A Data Warehouse is a relational database designed and conceived for data query and analysis, decision making and business intelligence activities more than for transaction processing or other traditional database uses.

The information stored in the Data Warehouse is and provide an overview of the various transactions that have taken place in the past. that have taken place over time. Redundant data is often included in Data Warehouses to provide users with multiple views of information. For this reason, data stored in the Warehouse is often aggregated to provide users with easier access.

In addition to a relational database, a Data Warehouse environment integrates a data extraction, transport, transformation and loading (ETL) tool. There is also a Online analytical processing engine (OLAP)The data is then processed using a variety of tools, including a database, customer analysis tools, and other applications to manage the processing of the data collected.

One of the main features of a data warehouse is that the information is classified by subject (customers, products…). In fact, what really defines a Data Warehouse is the type of data it contains and the people who use it.

What types of data are stored in a Data Warehouse?

Data stored in a Data Warehouse presents various characteristics. They are separate from operational systems, but also accessible and available for queries.

These data are subject oriented, and integrated. They are also consistently named and defined. In addition, these data are associated with defined periods of time. I mean, they’re static (non-volatile)which means that no updates are made to this data.

data warehouse type data

The four characteristics of Data Warehouses according to their inventor William H. Inmon

According to William H. Inmon, the inventor of the term, Data Warehouses feature four specific characteristics. They must be subject-oriented, integrated, non-volatile and time-variant..

Data Warehouses must be subject orientedwhich means that it must be possible to define them by their subject. For example, a warehouse can be deployed specifically to analyze sales-related data for the company. This Data Warehouse will be used to answer questions such as “who were the best customers for this product in the previous year”.

In the same logic, the Data Warehouse must be able to assemble data from different sources in a consistent format. It must be able to resolve problems such as name conflicts and inconsistencies in units of measurement. We’re talking about integration.

Third, the data warehouse must be non-volatile. This means that once a data has been entered in the Warehouse, it must not change. The user is thus able to analyze the data as it has been stored in the Warehouse.

Last characteristic of Data Warehouses, they must be time-varying. This means that they allow analyses to focus on changes over time from large datasets in order to discover trends. This is the difference between Data Warehouses and OLTP systems, where operational data is atomic and only reflects the current value of the last transaction.

What are the different types of Data Warehouses?

data warehouse types

We can distinguish four types of Data Warehouses The following are the most common types of data warehouses: traditional relational database management systems (RDBMS), specialized relational RDBMS, Data Warehouse Appliances and Data Warehouse Clouds. RDBMS are the most common data warehouses.

In most cases, a relational DBMSbut it should be noted that any type of DBMS can be used. In most cases, a Data Warehouse DBMS offers several additional features and functionalities that can be used effectively for Data Warehousing activities and operations. A Data Warehouse platform can also embed additional software. These are DBMS from vendors such as IBM, Microsoft, Oracle or SAP.

The specialized DBMS The difference between them and traditional DBMSs is the way they have been enhanced to support Data Warehouse workloads. These products are offered by vendors such as HP and SAP.

The Data Warehouses Appliances are designed to deliver turnkey Data Warehousing services directly. Typically, these products combine software and hardware with a relational or analytical DBMS pre-installed and configured on the required hardware. The server is configured with the right amount of memory and storage, and the DBMS is installed by the vendor. The user simply plugs in the hardware and turns it on. Examples are products from Teradata, IBM and Oracle.

In the last category, that of Data Warehouses Cloudthe DBMS is not installed on site. The user accesses it via the Internet. This is called “Data Warehouse as a Service”, from the XaaS family. The user does not need to have a DBMS or dedicated hardware on site. The market leaders in cloud data warehouses are Microsoft, Amazon and IBM.

It should be noted that there are also more and more hybrid solutions integrating various data storage and access capabilities for structured and unstructured data. Examples include the vendors Actian and Pivotal.

In addition, these different types of Data Warehouse platforms can be deployed in a variety of ways : Enterprise Data Warehouse (EDW), Data Mart, or a combination of the two.

A EDW is used throughout an entire companyA data mart is smaller and focused on individual or department-specific needs. Also, depending on the size of the company, it is possible to create several Data Marts and integrate them with an EDW.

How and why do companies use Data Warehouses?

data warehouse enterprise use case

Thanks to Online Analyticial Processing (OLAP), companies are able to gain insights from their operations through interactive and iterative access to stored data. This enables business managers to improve decision making by performing queries to examine processes, performance, and trends in their organizations.

A Data Warehouse can be used to monitor, manage and improve a company’s performance. It can be used to monitor and modify a marketing campaign. It can be used to review and optimize logistics and operations, or to improve the efficiency of product development.

Companies also use Data Warehouses forr Link and access information from multiple sources. These solutions also help to manage and improve customer relationships. Data Warehouses can be used to predict future trends and needs, and to improve data quality.

Data Warehouses: what are the advantages?

data warehouses advantages

Data Warehouses have many advantages. For IT managers, they make it possible to separate analytical processes from operational processes to improve performance in these two areas.

For companies, a Data Warehouse platform is a convenient way to view the past without affecting daily operations. By querying and analyzing data within the Data Warehouse, companies can improve their operations and efficiency, thereby increasing revenues and profits.

Data Warehouse vs. Data Lake: What are the differences?

data warehouse vs data lake differences

Data Warehouses are used for nearly 30 years. Recently, however, Data Lakes have become so popular that some people think they will replace Warehouses. In reality, it should be kept in mind that both types of systems have important differences and are not used in the same way.

A Data Warehouse brings together a large amount of data accumulated within a company from different data sources. It is used to make decisions. The Data Lake is a storage bank used to hold a huge amount of raw data in its original format until the company needs it. Data Lakes and Data Warehouses differ in a number of ways: the data, data processing, storage, agility, security and users.

Data Warehouses cannot accommodate that structured data. A Data Lake is capable of storing structured, semi-structured, or unstructured data. With regard to data processing, the data loaded into a Data Warehouse must pass through the schema-on-write which consists in giving them a form and a structure (a model). In the case of Data Lakes, the data are stored in their raw form and the user gives them form when needed. This is called schema-on-read.

Storage in a Data Warehouse can be expensiveespecially if the volume of data is large. Data Lake storage is often cheaper because most Big Data technologies are based on open source software designed to be installed on low-cost hardware.

A data warehouse is a structured database. It is therefore not technically difficult to redesign. However, this process can take time depending on the business processes involved. Unlike the Data Warehouse, the Data Lake has no structure. In fact, Data Developers and Data Scientists can easily configure and reconfigure data models, queries and applications. The Data Warehouses are therefore less agile.

On the other hand, in terms of security, Data Warehouses benefit from their age and maturity. The are less secure within a Data Lake.even if progress is being made in this area. The final difference between Data Warehouses and Data Lakes is the users they are intended for. Data Warehouses are mainly used by company managerswhile Data Lakes are generally used by Data Scientists in scientific fields.

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