The concept of Big Data modeling includes two software terminologies within it, namely ‘Data modeling’ and ‘Big Data’. The term ‘Big Data’ refers to the large amounts of data produced by companies in the modern contemporary world. This kind of data is devoid of any constant pattern and is complex by nature. It becomes impossible for companies to analyse this data in traditional methods.
These complex data types can be analyzed using high-quality data modeling methods. In this context, it has to be understood that the term ‘Data modeling’ implies the method of organizing the data in such visualized patterns that the process of data analysis can be performed with ease. The techniques include the process of making visual representations of the whole or part of the datasets.
Thus, It employs a certain data modeling method. It is different from the traditional methods and consists of a process of organizing big data for the use of companies.
The benefits of using big data modeling methods within organizational working can be classified as follows:-
- Low-cost and efficient method – The user company saves a lot of time in the process of developing the core values and principles that control the process of business functioning. In this way, the business plans can be developed within half the time previously required to create business plans. Along with this, the data modeling method also reduces the company expenditure. It can easily recognize faults in the data sets.
- Improvement in the business procedure -The method of data modeling helps the companies to recognize the kinds of data stored within the modern data warehouse and its purpose in the business processes. In this way, this process helps the user to better understand his data and the functioning patterns of the business.
- Reducing the risks – Big data is big in volume as well as its complexity. The growth of the business also increases the amounts of data and that increases the need for organizing such data. The method of taming the raw data and organizing it within various models increases the company’s visibility into the functioning patterns. This in turn is capable of reducing the risks and improves the database management systems.
- Increased collaboration – The major issue within organizations of the contemporary world lies in the communication gap between the software technicians and the non-technician staff of the company. But data modeling techniques increase the collaboration within the two departments.
What are different types of Data modeling:
The kind of data modeling approach that an organization adopts rests on the purposes that the company wishes to fulfil through the process.
The kinds of data modeling can be illustrated as follows:
- Conceptual Data modeling – As the term suggests, this kind of data modeling is functional in an organization that is in the conceptual stage of its formation. In this situation, the user company attempts to organize the raw data to establish the goals. You can also set achievement standards that you wishes to actualize. The conceptual data modeling function is performed by business analysts to give the investors a brief idea of the relevant datasets and their relation to one another.
- Logical Data modeling – This term refers to the process of creating a graphical representation of the information depicted within the conceptual data models. The difference between the conceptual and logical patterns of data modeling is that in the latter, there is an addition of definitions, illustrated concepts and others which increase the understanding of the data within these models.
- Physical Data modeling – The choice of physical data modeling method is made by a company based on the functional requirements that are dependent on the data. The data models built using this method have a basis in a specific database management system.
What are different concepts in data modeling:
Certain important concepts need to be illustrated in the context of data modeling. They are as follows:-
- Data Marts – The term Data Mart refers to the specific data that has been transformed into a format that can simplify the process of data modeling. Generally, these data marts contain information that is relevant to data analysts and discovery. This specific information may not be present in the raw dataset or at least not in the format that can be used with ease. A data mart can be modelled using two kinds of tables, namely, i) Data or the facts and ii) Definitions, that provide the various information found in the facts.
- Star Schemas – The star schemas are a part of the multi-dimensional schemas in which the data warehouses are modelled for the simplification of the data and to ready the data for the analysis process. In specific, the star schema is a design in which the center is occupied by the fact table and surrounding that there are several dimension tables. This is the simplest kind of data warehouse schema. The star schema is used for query performance and is also known as the Star Join Schema.
As per the recent trends, 33% of big corporation will focus on big data modeling for decision making. source – Gartner
- Snowflake Schema – The snowflake schema is usually used in multidimensional data warehouses. This is a logical arrangement of tables and is an extension of the star schema. The basic purpose of this schema is that it provides the space of adding more dimensions to the existing arrangement. The dimension tables are designed such that the data is split between the tables.
- Surrogate Key – The term ‘Surrogate key’ is relevant in the database management system. Unlike the natural key, the surrogate key is not derived from the application data. This key is used as a unique identifier for information in the data model or an object in the database. The significance of the surrogate key lies in its use as a natural key in database management processes.
- Snapshot Dimensions – This is also known as the snapshot tables. These kinds of tables are designed to capture a large quantity of periodic data within their rows and columns. A snapshot periodic table generally has a large capacity of assimilating data within its fields. These are used to store the basic periodic data of an organization from time to time.
- Complex Data Types – In relational database management systems, the complex data types are understood as the data types that do not fall within the traditional structures of data, for instance, date, numerics and others. Some instances of complex data types include word processing documents, images, videos and others.
What are different methods of Data Modeling:
As understood earlier, the term data modeling implies the organization of the raw data into models to simplify and transform data for analysis. This process can be performed mainly in two kinds of systems, namely, OLTP and OLAP.
The term OLTP is the abbreviated version of the term Online Transaction Processing. On the other hand, the term OLAP implies the term Online Analytical Processing. As the terms suggest, the data modeling patterns are supposed to be different for the two systems. A competitive analysis of the two methods would be required to understand the differences clearly.
To begin with, the data operating system in the OLTP is a random read or write procedure. On the other hand, the basic data operation system in the OLAP follows the batch read or write procedure. While the OLTP system focuses on the entity-relationship model, the OLAP system concentrates on data integration.
Finally, the OLTP system focuses on solving issues like data redundancy, transactional inconsistency. The OLAP on the other hand focuses on big data query performance.
Power BI approach towards Data modeling:
The Data modeling technique in the Power BI system involves a process of connecting multiple data sources based on a relationship. This modeling feature enables the user organization to build custom calculations on the existing data set within the columns. These calculations can be presented directly in the Power BI Visualizations.
This process enables businesses to recognize new subjects or metrics and further calculate those metrics.
To create a data model in Power BI, the datasets need to be added to the new report option. The data modeling technique within power BI essentially performs the function of simplifying the raw datasets. This, in turn, facilitates the process of data analysis within Power BI.
Power BI and OLAP methods of Data modeling:
Power BI uses the OLAP system of data modeling. This implies that the services process the formation of data models in the online analytical processing system. In such a system, the data is transformed into simpler formats for data analysis.
Along with this, the classification model accuracy is a feature of this data modeling pattern. This method is also used for performing big data queries also.
Benefits of Data Modeling in Power BI:
As per the Power BI Consultant There are certain benefits that the Power BI user organizations can reap by data modeling within the Power BI services.
Some of these advantages of data modeling through Power BI include the following:-
- The Power Pivot and Analysis services provide the best possible data modeling experience within the Power BI services.
- The Power Query and the M language within the service remove the issues like excess tables within the data models, irrelevant data within the tables and others.
- Within the Power BI Desktop, the user is permitted to navigate directly into the data models. It simplifies the process of data analysis.
- The Power BI service transforms the data while creating the models. It creates the data as unambiguous and simpler to analyze. These models can also be used for predictive analysis and decision making.
- The data modeling pattern in Power BI also includes the process of performing recursive queries.
Tips for creating successful Data Models:
There are certain tips for creating successful data models.
These include the following:-
- Refrain from imposing traditional data modeling techniques on big data – The traditional datasets are stable and controlled in their volume. So, if an attempt is made to apply traditional data modeling techniques to the big data of the organization then the data models will fail substantially.
- Designing a system– In traditional data modeling patterns, the data models are designed in the form of schemas as the data being modelled is controlled. But, to model big data successfully, the data model must be in the form of a system and not a schema. This is because big data is usually built on systems and not on databases.
- Using big data modeling tools– In the process of data modeling, the companies must search for modern tools that are designed to perform big data modeling effortlessly. The commercial tools include Hadoop while there are certain big data reporting software like Tableau, that can perform the function of creating big data models easily.
- Emphasizing the Data– The user organizations must focus on the specific big data se. If a company attempts to create data models of all the data that it acquires, the relevant information may be lost in the modeling process.
- Delivering quality data – The IT professionals of a specific company need to know the various datasets within the database. This will facilitate the process of placing the data in the relevant column within the data model.
- Recognizing entry points – The IT departments of the companies need to recognize the entry points into the data. This can then result in the creation of successful data models for the company.
EPC Group for Big Data modeling:
The EPC Group is dedicated to providing one-stop solution to the user companies. Any troubles they face while deploying or using the Power BI services is priority. They also provide consultancy services from the point of deployment of Power BI within an organization’s on-premise system. Gives support till final usage of the services by the staff of the company. In between, all the services offered under the Power BI services are covered by the group during their training and assistance programs.
The main purpose of the Power BI service is to assist organizations to derive data from multiple sources for generating automated business reports. Later transform it into a simpler format and analyze it for acquiring relevant information. The data modeling technique is a part of the data transformation process within Power BI. EPCGroup provides a proper Power BI training program related to these methods. At the end of the Power BI consultancy services, the employees of the user organization can create successful data models on their own.
Conclusion:
Thus, the data modeling techniques of Power BI are suitable for organizations of contemporary times, which perform functions based on big data
