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If your company keeps up with the trends in data management, you likely have encountered the concepts and definitions of data warehouse and big data. When your data professionals 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.
Data Warehouse Modeling Concepts
All data in a data warehouse are well-organized, archived, and arranged in a particular way. Not all the data that can be gathered from multiple sources reaches a data warehouse. The source of data is crucial since it impacts the quality of data-driven insights and hence, business decisions.
During the phase of data warehouse development, a lot of time and effort is needed to analyze data sources and select useful ones. It depends on the business processes, whether a data source has value or not. Data only gets into a warehouse when its value is confirmed.
On top of that, the way data is represented in your database has a critical role. Concepts of data modeling in a data warehouse are a powerful expression of business requirements specific to a company. A data model determines how data scientists and software engineers will design, create, and implement a database.
There are three basic types of 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 data 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 data. 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 data 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 Data 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, data should be 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 data extraction from multiple sources and pulling data into a single database to serve a business.
Source: matillion.com
At the first stage of the ETL process, engineers 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.
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 data analytics experts so much time.
ETL testing concepts are also essential to ensure that data 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.
Types of Data Warehouse for Your Company
Different data warehouse concepts presuppose the use of particular techniques and tools to work with data. Basic data warehouse concepts also differ depending on a company’s size and purposes of using data.
Enterprise Data Warehouse enables 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 data 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 Data 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. Data 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 data science needs. Custom solutions in the field of data science and machine learning can provide businesses with opportunities unheard of before.
Having data 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 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 data, 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. It provides 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.
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