Basid Rahman’s Post

Excited to delve into the world of data modeling with you, Ravena O! Your breakdown of different modeling types is insightful and helpful for anyone interested in this field.

Excellent Breakdown of different data modeling types along with their basic features!!

Excited to dive into the world of data modeling? Let's explore the diverse landscape of data modeling types! 💡 1️⃣ Conceptual Data Modeling: Lay the foundation with high-level, business-focused models to capture entities and relationships. 2️⃣ Logical Data Modeling: Get into the nitty-gritty of database design, crafting detailed structures using entities, attributes, and relationships. 3️⃣ Physical Data Modeling: Time to bring your models to life! Design databases, tables, and indexes, optimizing for performance and storage efficiency. 4️⃣ Relational Modeling: Harness the power of relational databases, organizing data into tables with defined relationships, enforcing integrity constraints, and enabling SQL querying. 5️⃣ Entity-Relationship Modeling: Visualize the relationships between entities in a system, mapping out the connections and cardinalities to understand data dependencies. 6️⃣ Object-Oriented Modeling: Bridge the gap between software engineering and data modeling, representing data as objects with properties and behaviors for object-oriented databases. 7️⃣ Star Schema: Dive into dimensional modeling for data warehousing, organizing data into a central fact table surrounded by dimension tables, enabling efficient analytical queries. 8️⃣ Dimensional Modeling: Navigate the complexities of data warehousing with star schemas, snowflake schemas, and fact tables for analytical insights. 9️⃣ NoSQL Data Modeling: Embrace the flexibility of NoSQL databases, designing schemas tailored to document, key-value, column-family, or graph data models. Each type of data modeling brings its unique strengths to the table, empowering organizations to effectively manage and derive value from their data assets. 💼💻 #DataModeling #DataManagement #DatabaseDesign #Analytics #NoSQL #TechTrends

Easily Transit your role from data engineer to solution architect by mastering the Art of Data Modeling Data modeling is the art of turning raw data into structured, usable insights. It’s like designing a map that guides your data from chaos to clarity. Here are a few key concepts to consider when diving into data modeling: 🧩 Entity-Relationship Diagrams (ERD): These visual maps define how data entities are related. It’s the blueprint of your data structure. 📊 Normalization: The process of organizing data to avoid redundancy. Think of it like cleaning up your workspace—removing clutter to make everything easier to find and use. 🔄 Denormalization: Sometimes, for performance reasons, you deliberately add redundancy to simplify query performance. It’s the opposite of normalization—think of it like storing extra copies of important documents for quick access. 💾 Star Schema & Snowflake Schema: These are design patterns for data warehouses. • Star Schema: Simplifies queries with a central fact table and surrounding dimension tables. • Snowflake Schema: A more complex version where dimensions are broken down into multiple related tables. 🔑 Primary and Foreign Keys: They’re the foundation of any relational database, ensuring that data links correctly and consistently between tables. 🧠 Dimensional Modeling: This is about organizing data for easy reporting and analysis—think of it as organizing a library with sections like “fiction,” “non-fiction,” and “reference.” The goal of good data modeling is to make data more efficient and intuitive to work with, so the decision-making process is faster and more accurate. What’s your favorite data modeling concept? Let’s discuss! #DataModeling #DataScience #CloudData #DatabaseDesign #DataWarehouse #Azure

🚀 Dive into the essentials of data modelling with Mike Shakhomirov MBA's article "Advanced Data Modelling" on Towards Data Science. Data modeling is crucial for any data engineer, and this piece breaks down key techniques for building effective SQL transformation pipelines. From naming conventions to structuring data for user-friendly navigation, Mike offers practical advice for designing robust data platforms and warehouses. In his article, Mike emphasizes the importance of maintaining a well-designed naming convention. By implementing clear and unambiguous names for database objects, developers can ensure that their data warehouses are mature, organized, and easy to navigate. This practice not only enhances the user experience but also supports the overall development process by providing a consistent and logical framework. Mike also explores the benefits of adopting the standard Kimball techniques for naming tables. By prefixing dimension tables with "dim_" and fact tables with "fact_", data engineers can create a coherent and user-friendly semantic layer. This solid foundation is crucial for robust data modeling, enabling the development of accurate and efficient data platforms. If you are looking to refine your data modeling skills and learn from a seasoned expert, this article is a valuable resource. Read the full article here: https://lnkd.in/dReSawXd #DataEngineering #DataModelling #SQL #DataWarehousing #Techniques #BestPractices

🏗️ Data Modeling: The Blueprint for Scalable Analytics 🏗️ In the world of data engineering, data modeling is the backbone of a robust and scalable analytics infrastructure. Yet, it often gets overlooked in favor of more visible tasks like building pipelines or designing dashboards. But without the right model, everything built on top of it will suffer. 📉 Here are some key principles to keep in mind when building your data models: 1️⃣ Understand Business Requirements Before diving into modeling, collaborate with stakeholders to gather all necessary business requirements. This ensures your data model supports the right metrics and decision-making processes. 2️⃣ Choose the Right Schema For transactional systems, normalized schemas (like 3NF) prevent redundancy and maintain data integrity. For analytics, denormalized schemas (like star or snowflake schemas) help optimize query performance, especially for BI tools. 3️⃣ Balance Flexibility and Performance Design models that can handle changing business requirements without compromising on performance. This often involves a mix of flexible design (for future needs) and efficient queries for current reporting. 4️⃣ Document Everything Always document your models, including entities, relationships, and assumptions. Clear documentation will help others understand the model's purpose and design decisions. 5️⃣ Test Early and Often Just like code, data models need testing. Perform data validation checks to ensure the model functions as expected, especially when changes are made. Remember, a well-designed data model not only improves query performance but also empowers teams to make faster, data-driven decisions. 🚀 What’s your approach to data modeling? Let’s discuss! #DataEngineering #DataModeling #DataArchitecture #SQL #BigData #Analytics #ETL #BusinessIntelligence

📢 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐝𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠? ✨ Data modeling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures. 🎯 𝐆𝐨𝐚𝐥? The goal of data modeling to illustrate the types of #data used and stored within the system, the relationships among these data types, the ways the data can be grouped and organized and its formats and attributes. 📎 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐥𝐬: > Conceptual data models > Logical data models > Physical data models 📜 𝐃𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐩𝐫𝐨𝐜𝐞𝐬𝐬: 1- Identify the entities. 2- Identify key properties of each entity. 3- Identify relationships among entities. 4- Map attributes to entities completely. 5- Assign keys as needed, and decide the degree of #normalization. 6- Finalize and validate the data model. 🖇 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠: - Hierarchical data models - Relational data models - Entity-relationship (ER) data models - Object-oriented data models - Dimensional data models (#Star and #Snowflake) In summary, #DataModeling makes it easier for developers, data architects, business analysts, and other stakeholders to view and understand relationships among the data in a database or #datawarehouse. #DataEngineering #DataScience #Microsoft #Azure #Model #OOP

Many data engineering teams lack the core skills of data modeling. It is time to rediscover the lost art of data modeling. Dave Wells latest article provides a quick and high-level look at data modeling for relational data. It is the most mature of today’s data modeling techniques, and it is important because it is the foundation for more recent techniques. Read to know why ER modeling knowledge and skill is something that is needed in every data engineering team: https://lnkd.in/er5mBpQV #datamodelling #lostart #datamanagement

Data modeling should start before you write code. I know we all want to start writing code and dbt models. But if your application and data warehouse aren’t modeled well. It’s only going to get worse, not better. If you'd like to learn more about data modeling here are a few great places to start. 1. Data Modeling Where Theory Meets Reality - How Different Companies Modeled Their Data https://lnkd.in/ekZvqqfe 2. Data Modeling - Building Slowly Changing Dimensions (SCDs) - Day 2 Lab- with Zach Morris Wilson https://lnkd.in/g7PAWd8M 3. The Challenges You Will Face When Data Modeling https://lnkd.in/gd5CQrZs 4. Key Tenets of the Kimball Method by Joy Mundy https://lnkd.in/eDW9nVmY 5. Data Modeling - Why Data Engineers Need To Understand It https://lnkd.in/gCvpMYhP 6. Beginner's Guide to building data models with dbt by Jeffrey Richman https://lnkd.in/gUditV98 7. There's No Free Lunch in Data Modeling by Joe Reis https://lnkd.in/ezN5-Ep9 What tips do you have for people looking to learn more about data modeling?

Database Normalization for Data Engineering: From No Normalization to 3NF What is Database Normalization? Database normalization is the process of organizing your data to reduce redundancy and improve integrity by breaking down large, complex tables into smaller, more structured ones. This is done by applying a series of rules called Normal Forms (NFs), which minimize dependencies between tables and lead to fewer data inconsistencies. Normalization typically involves dividing tables into smaller ones and linking them using foreign key constraints. The most common Normal Forms are 1NF, 2NF, and 3NF, though more advanced forms exist for specific use cases. Why is Normalization Important for Data Engineers? As a data engineer, whether you’re modeling your data as One Big Table (OBT), using a star schema, snowflake schema, or even a Data Vault, understanding normalization is crucial. Each of these approaches follows different degrees of normalization: • OBT (One Big Table) is the least normalized, often in 1NF, and combines both fact and dimension tables into one. • Star schemas and snowflake schemas typically keep fact tables in 3NF for data integrity, while dimension tables are often in 2NF. • Data Vault is highly normalized and can follow even stricter rules. Understanding these differences allows you to apply the correct normalization form depending on your use case, balancing complexity with performance: 1. Unnormalized Form (UNF): Contains duplicate data and repeating groups, not divided into separate tables. 2. First Normal Form (1NF): Ensures that all values are atomic (indivisible) and each record is unique. 3. Second Normal Form (2NF): Removes partial dependencies, meaning attributes should only depend on the entire primary key (not a subset of it). 4. Third Normal Form (3NF): Removes transitive dependencies, where non-key attributes depend only on the primary key and not on other non-key attributes. Lets look at a simple example on how to take a table from no normalization to 3NF: #dataengineering #datamodelling #analytics #databases #dataanalytics