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Data Warehouse Concepts: Types, Examples, and Link to Big Data

data warehouse concepts

If your company keeps up with the trends in data management, you likely have encountered the basic data warehouse concepts. When top data warehouse vendors try to implement data extraction solutions, they need a data repository. For this purpose, they can use a data warehouse and a data lake.

Roughly speaking, a data lake is mainly used to gather and preserve unstructured data, while a data warehouse is intended for structured and semi-structured data.

Top Data Warehouse Modeling Concepts

All information in a data warehouse are well-organized, archived, and arranged in a particular way. Not all the information that can be gathered from multiple sources reaches a data warehouse. The source of information is crucial since it impacts the quality of insights and hence, business decisions.

During the phase of data warehouse development, top data warehouse providers spend a lot of time and effort to analyze data sources and select useful ones. It depends on the business processes, whether a data source has value or not. Information only gets into a warehouse when its value is confirmed.

On top of that, the way it is represented by data warehouse vendors in your database, has a critical role. Concepts of modeling in a data warehouse are a powerful expression of business requirements specific to a company. A data model determines how data warehouse companies will design, create, and implement a database.

There are three basic types of best data warehouse modeling. Conceptual Data Model describes all entities a business needs information about. It provides facts about real-world things, customers, and other business-related objects and relations.

The goal of creating this model is to synthesize and store all the data needed to gain an understanding of the whole business. The model is designed for the business audience.

Logical Data Model suits more in-depth information. It describes the structure of data elements, their attributes, and ways these elements interrelate. For instance, the model can be used to identify relationships between customers and products of interest for them. This model is characterized by a high level of clarity and accuracy.

Physical Data Model describes specific data and relationships needed for a particular case as well as the way this model is used in database implementation. It provides a wealth of meta-data and facilitates visualizing the structure of a database. Meta-data can involve accesses, limitations, indexes, and other features.

ELT and ETL Warehouse Concepts

Large amounts of data sorted for warehousing and analytics require a special approach. Businesses need to gather and process data to retrieve meaningful insights. Thus, on-premise data warehouse vendors should make data manageable, clean, and suitable for molding and transformation.

ETL (extract, transform, load) and ELT (extract, load, transform) are the two approaches that have technological differences but serve the same purpose – to manage and analyze data.

ETL is the paradigm that enables information extraction from multiple sources and pulling data into a single database to serve a business.

Data warehouse scheme

Source: matillion.com

At the first stage of the ETL process, DWH companies extract data from different databases and gather it in a single place. The collected data undergoes transformation to take the form required for a target repository. Then the data comes to a data warehouse or a target database.

If to switch the letters “T” and “L”, you get the ELT process. After the retrieval, the data can be loaded straight to the target database. The cloud technology enables large and scalable storage places, and massive datasets can be first loaded and then transformed as per the business requirements and needs.

Data warehouse scheme

Source: matillion.com

The ELT paradigm is a newer alternative to a well-established ETL process. It is flexible and allows fast processing speed to work with raw data. On the one hand, ELT requires special tools and frameworks, but on the other, it enables unlimited access to business data, thus saving BI and analytics experts so much time.

ETL testing concepts are also essential to ensure that information is loading in a data warehouse in a correct and accurate manner. This testing involves data verification at transitional phases. And before data reaches the destination, its quality and usefulness are already verified by data warehouse companies.

Types of Data Warehouse for Your Company

Different data warehouse concepts presuppose the use of particular techniques and tools to work with information. Basic DWH concepts also differ depending on a company’s size and purposes of using data.

Top data warehouse vendors enable a unique approach to organizing, visualizing, and representing all the data across a company. Data can be classified by a subject and accessed based on this attribute.

Data Mart is a subcategory of a warehouse designed for specific tasks in business areas such as retail, finance, and so forth. Data comes into a data mart straight from the sources.

Operational Store satisfies the reporting needs within a company. It is updating in real-time, which makes this solution best-suited for keeping in all business records.

Concepts of Building Data Warehouse: Examples

Valuable data empowers business intelligence (BI) solutions and predictive analytics. For instance, health and fitness apps are premised on immense amounts of user data. Information gathered from multiple apps and via GPS comes into a BI data warehouse. And a well-designed data repository allows analysts to effortlessly access data. Then it becomes possible to visualize this data or use it for interactive dashboards. Information obtained in this way can fuel personalized recommendations or individual workout planning.

Reliable data stored in a single place can serve the needs. Custom solutions developed by data warehouse consulting in the field of data science and machine learning can provide businesses with opportunities unheard of before.

Having all information at your fingertips, you can build new business relationships and map out unique opportunities. For example, it paves the way to more profound automation of traditional activities and real-time monitoring of workflows within your company. To get and store data from whatever sources and in different formats is no longer an issue.

Documents, project data, schedules, risk data, and whatever else it can be, when combined, form big data. Data warehouse or data lake implementation solves the tasks of analyzing, processing, and visualizing big data. You сan get all data structured by data warehouse providers and use it to drive up the value of your business.

Big Data and Data Warehouse Ambiguity

A data warehouse is an architecture that has proved to be valuable for data storing over the years. It involves data that has a defined value and can be used from the start to solve some business needs. Everyone can access this information, and the features of datasets are reliability and accuracy.

Big data is a hyped field these days. It is the technology that allows retrieving data from heterogeneous sources. The key features of big data are its volume, the velocity of data streams, and a variety of data formats. Unlike a data warehouse, big data is a repository that can hold unstructured data as well.

Companies seek to adopt custom big data solutions to unlock useful information that can help improve decision-making. These solutions help drive revenue, increase profitability, and cut customer churn thanks to the comprehensive information collected and available in one place.

Data warehouse implementation entails advantages in terms of making informed decisions. Companies that use data warehouse in their organizations get comprehensive insights into what is going on within a company, while big data can be in the shape of massive but disorganized datasets. However, big data can be later used for data warehousing.

Running a data-driven business means dealing with billions of data on in-house and external operations, consumers, and regulations. Implementation of up-to-date data warehouse solutions facilitates data-related tasks and helps business owners forecast development perspectives and opportunities for growth. Click To Tweet

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