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Written by Umesh Palshikar » Updated on: May 07th, 2024
The greatest asset to many companies is their information. That is only when it can be collected, processed, analyzed, and effectively utilized. It is essential to store the data structured in a manner that is compatible with the application, puts it into the flow, and offers valuable data even when the data remains in Slack. Data modeling is vital as it allows businesses to see these processes clearly and develop, create, and then deploy data assets of high quality.
Additionally, because businesses utilize data design schemas based on business requirements, all data modeling models should conform to the schemas. Data Engineering Modelling Solutions is of great value for virtually every company; there's also an enormous need for data models and people who can model data. This blog highlights the most exciting data modeling projects. It is designed to help you understand the different uses and build your skills in data modeling for big data-related projects in the future.
"Data modeling" refers to creating an image of a complete information system or portions that communicate the relationships between data points and structures. It aims to demonstrate the kinds of data stored in the system, the relationship between the various types of data, the different ways data may be classified and organized, and its types and characteristics. Models of data are developed around the business requirements. Requirements and rules are defined before feedback from stakeholders in the business. So that they can be integrated into the development of a brand-new model or added to the creation of a new model.
Data is modeled on several degrees of abstraction. It begins with collecting details about the business needs of the involved and end users. The business requirements are transformed into data structures for a model for a database. Data models can be used as a reference point for a road map, such as an architect's sketch or any diagram of a formal nature, that aids in gaining a better understanding of how the database is created. Data modeling is based on standard models and formal methods. It provides a standard, regular, reliable, and Predictive Modeling for managing and defining data resources within the organization.
In the ideal scenario, data models are evolving documents that adapt to the changing needs of business. They are crucial in supporting the business process and designing IT design and strategy. Models of data can be shared with partners, vendors, or industry colleagues.
If you're preparing for an interview on data modeling and are looking for a unique example of data modeling that could be added to your resume or portfolio.
This project can use Postgres to perform data modeling and build the ETL pipeline. An online music service provider of the future is looking to analyze the data that they are collecting on music and user behavior. They collect information in JSON format, and their analysts are curious to understand the music their users have been listening to. Utilize this Million Song dataset available on Kaggle for this purpose.
Data warehouses are central databases of information from various sources, which are specifically designed to be used for analysis. Star schemas are an effective data modeling method to create a data warehouse that organizes data tables and dimension tables. Fact tables store quantitative indicators of the business, such as revenues, sales, or profit. Dimension tables contain the characteristics of a business's description, including customers, products, or even the time. Fact and dimension tables are connected via foreign keys, forming an elongated star shape.
Star schema projects can demonstrate your ability to create a data warehouse to support diverse BI reports and query types. Tools such as SQL and ETL (extract, transform, and load) and data modeling software design tables, populate them, and document the data dictionary. You can also explain how to utilize the star schema to generate diverse kinds of BI outputs, including joining, aggregations, filters, and calculations.
This project will track the costs associated with Uber Rides and Uber Eats by leveraging techniques for data engineering that utilize tools such as Apache Airflow, AWS Redshift, and Power BI. Both services' costs are divided into distinct fact tables as part of the data model proposed because both data sets share dimensions; the model is a cluster model. The process involves creating an ETL pipeline with Airflow and deploying it to Docker using Redshift. In the final stage, join Power BI Desktop to AWS Redshift information, build an Uber Eats and Uber Rides dashboard, Uber Eats and Uber Rides, and upload the report via Power BI. Power BI service.
This project aims to archive events generated by sensors and provide them to be analyzed. Data comes from many sources using industrial equipment sensors. Additionally, some users create thousands of events per second. Additionally, the strict SLAs for many clients require that each event be transferred across multiple machines in less than ten milliseconds. The project uses Cassandra's key capabilities, including supporting writing-heavy SLAs and storing Time Series data.
This initiative aims to save personal information for users of various online shopping accounts. Client information like passwords, usernames, and usernames. Personally identifiable information (PII), individual contact preferences, and more. This makes it highly sensitive. Because it impacts how fast users sign on to the site, companies typically require access to customer data at a very low latency across numerous data centers. This project heavily uses reads and has latency SLAs under 50 milliseconds. This project analyzes Cassandra's capabilities, which include handling extensive-defined types, effective encrypted data handling, etc.
This project will develop a data model that can be used for the shopping cart of an e-commerce site. Users typically browse the site and then add items to their carts. This application leverages Cassandra's wide-row layout to keep all the contents of a shopping cart within one single compartment. This means that getting the content of shopping carts is more efficient and straightforward with one query. The item's ID is attached to a large row of users every time they want to add items to their list of items to purchase or add to their shopping cart. The key to partitioning the shopping cart's details can be the user's ID, while the key to clustering can be an item's ID.
Data modeling is an essential expertise for data architects because it allows you to design and describe your information assets' relationships, structure, and restrictions. Data modeling also helps improve the efficiency, quality, and accessibility of your data solution. What is the best way to approach the data modeling process for a new venture? Below are some guidelines for developing efficient and reliable data models.
First, you must know your business's requirements and the project's purposes. What data sources are there or users? What are the use scenarios? What are the most critical issues, metrics, and data insights the data you collect should be able to offer? What are the quality requirements, security, and data compliance requirements you must be able to meet? It is possible to use methods such as surveys, interviews, or workshops, as well as Advanced Statistical Analysis to discover and confirm the needs of the business in conjunction with key stakeholders.
The next thing to do is select a data modeling method appropriate for your specific project's scope, complexity, and types. A variety of data models, including physical, conceptual, and physical, provide various levels of abstraction and detail. Different terms, including dimensional or entity-relationship notation and UML, also describe the data model you created. Selecting the data modeling method that aligns with your data structure methods, tools, and methodology is essential.
The final process is establishing your data model's entities, attributes, and data. Data entities, like clients, goods, or orders, are the primary notions or objects you intend to store and alter in your data system. The attributes of data are the property or traits of every data entity. They include names and prices or quantities. The name and identification of the data entities and their attributes per the requirements of your business and data sources.
The final stage is to establish the data relations and restrictions that define your model of data. Data relationships refer to the connections or connections between data elements, for example, one-to-one, one-to-many, or many-to-many. Data constraints define the rules or limits that must be applied to data entities or the attributes they represent, for example, the primary key, the foreign key, or even uniqueness. It is essential to define the relationship between the data elements and constraints according to the business logic as well as the integrity of the data.
The fourth stage is to verify and further refine the data model based on feedback and tests. Reviewing and confirming the model's data with participants and stakeholders is essential to confirm that it aligns with the business's needs and requirements. It is also recommended that the model's performance be checked using the data model, mock data, or sample data to verify its accuracy, performance, and feasibility viability. Any modifications or adjustments to the model should be made in light of the test results and validation.
The next stage is to record and distribute the model to those involved. Documenting the data model in precise and consistent descriptions, definitions, and diagrams is essential. It is also important to communicate the data model to people who develop and analyze it and managers who utilize or apply it. It is necessary to explain the reasoning behind your data model, the assumptions you make, and the benefits of the data model. Also, it would help if you kept the model up-to-date and updated it as your project grows or evolves.
Data modeling is crucial for any Business Intelligence (BI) professional seeking to design credible and trustworthy dashboards, reports, and analytics. Data modeling involves creating and arranging data structures, like views, tables, and schemas, to help the company's analysis and visualization requirements. An effective data model will enhance BI tools' efficiency and accuracy, do Data Transformation, and improve secure data management and governance.
However, modeling data cannot be a standard procedure. Different kinds of information sources, business situations, and BI tools will require various approaches and methods to build a successful data model. This is why BI professionals need to showcase their creativity and versatility in data modeling by presenting a portfolio of their projects. The above-mentioned interesting data modeling project ideas will assist you in expanding your analytics skills beyond the creation of the typical ETL Pipelines.
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