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What data modeling trends should you know?

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Data modeling is the process of designing and organizing data structures to support various data analysis and business objectives. It is a crucial skill for data scientists, as it affects the quality, efficiency, and scalability of data solutions. In this article, we will explore some of the current trends and best practices in data modeling that you should know.

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Darsh Murarka

Senior Data Analyst | PowerBI Expert | Snowflake | Auto-ETL | Auto-ML

1 Conceptual data models

A conceptual data model is a high-level representation of the data entities, relationships, and rules in a domain. It is independent of any specific database or technology, and focuses on the meaning and purpose of the data. Conceptual data models help you understand the business requirements, communicate with stakeholders, and validate the scope and scope of the data solution. Some of the common tools and techniques for creating conceptual data models are entity-relationship diagrams, business glossaries, and data dictionaries.

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Darsh Murarka

Senior Data Analyst | PowerBI Expert | Snowflake | Auto-ETL | Auto-ML
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Conceptual Data modelling helps identify any remaining gaps within the existing architecture that could be leveraged to build deeper and better analytics around the business model

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2 Logical data models

A logical data model is a more detailed and structured representation of the data entities, attributes, types, constraints, and relationships in a domain. It is still independent of any specific database or technology, but it reflects the logical structure and organization of the data. Logical data models help you design the data solution, optimize the performance, and ensure the consistency and integrity of the data. Some of the common tools and techniques for creating logical data models are normalization, denormalization, dimensional modeling, and data vault modeling.

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3 Physical data models

A physical data model is a specific implementation of the data entities, attributes, types, constraints, and relationships in a domain. It is dependent on the database or technology that you use, and it reflects the physical storage and access of the data. Physical data models help you create the data solution, configure the database parameters, and tune the performance. Some of the common tools and techniques for creating physical data models are SQL, NoSQL, schema-on-write, schema-on-read, and schema evolution.

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4 Data modeling tools

Data modeling tools are software applications that help you create, manage, and document data models. They can automate some of the tasks and processes involved in data modeling, such as generating diagrams, scripts, reports, and documentation. They can also facilitate collaboration, version control, and validation of data models. Some of the popular data modeling tools are ER/Studio, ERwin, PowerDesigner, Toad Data Modeler, and SQL Developer Data Modeler.

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5 Data modeling standards

Data modeling standards are guidelines and best practices that help you ensure the quality, consistency, and interoperability of data models. They can cover various aspects of data modeling, such as naming conventions, data types, formats, patterns, and metadata. They can also vary depending on the industry, domain, or organization that you work for. Some of the common data modeling standards are ISO/IEC 11179, DAMA-DMBOK, CDM Framework, and FIBO.

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6 Data modeling trends

Data modeling trends are the emerging and evolving developments and innovations in data modeling. They are influenced by the changes and challenges in the data landscape, such as the growth of big data, cloud computing, artificial intelligence, and data governance. Some of the current data modeling trends are graph data models, multimodel databases, data fabric, data mesh, and data lakehouse.

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Darsh Murarka

Senior Data Analyst | PowerBI Expert | Snowflake | Auto-ETL | Auto-ML
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Having a top down understanding of the use case of data lakehouses brings about better and efficient data marts versus a bottom up approach where the data marts are created based on a problem statement presented

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What data modeling trends should you know?

1

2

3

4

5

6

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1 Conceptual data models

2 Logical data models

3 Physical data models

4 Data modeling tools

5 Data modeling standards

6 Data modeling trends

7 Here’s what else to consider

Data Science

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What data modeling trends should you know?

1

2

3

4

5

6

7

1 Conceptual data models

2 Logical data models

3 Physical data models

4 Data modeling tools

5 Data modeling standards

6 Data modeling trends

7 Here’s what else to consider

Data Science

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