What are the most important data modeling trends, and how can you stay up-to-date?

The same it has always been: Learn 3NF and dimensional modelling. It is just like cooking: Learn how to fry a steak and boil an egg. Only when you master the basics can you break the rules. Once you master the basics, you will also realise that a lot of the "modern" rule breaking is just misguided cooking mixing up random ingredients trying to be fancy.

The rise of Data Mesh architecture, Data Lakehouses, and AI is poised to revolutionize how we approach data modeling. This feels as significant as the transition from procedural to object-oriented programming, and ultimately, to AI-powered code development. While AI unlocks incredible possibilities for data analysis, it also redirects our focus. Governance and data quality become paramount, surpassing the pure act of structuring and modeling. This paves the way for a shift towards granular, business-oriented data models rather than monolithic ones.

Surprise, surprise - as a Certified Data Vault CDVP2 Instructor, I believe that the model is only 20% of the entire solution. EVERYTHING in the Data Vault Methodology is designed for 100% auditability, scalability, reliability, adaptability, near-zero refactoring, based on repeatable, consistent patterns for true agility across People, Process, and Technology. The model is an optimized enabler for the Methodology and Architecture; and it can be implemented on any platform. Want to achieve CMMI Level 5 optimization out of the box? This is the methodology that can help a team succeed - AFFORDABLY. Happy to share more about this passion of mine.

Data governance and compliance: Day to day there are lot many changes in regulations around data privacy and security, so we must focus on implementing data governance practices to make sure compliance. Unified Data modelling: We should decentralise the data ownership and access to enable data capabilities more efficiently. Stay up to date Following news letters, blogs and tech conferences and online courses.

Data Vault Modeling, a hybrid approach promising best of both world 3NF Operation Data Store and Dimensional Model (E.g. Star Schema) to cater the Operational reporting, and BI & Analytics needs. This modeling technique is good for multi-source environment needing agility and fast adaption to the changes. In my experience, this modeling technique is mostly useful in the Core Layer or Silver Layer (Medallion Architecture) as Gold Layer / Serving Layer et.al best serves the Analytics needs as De-normalized (may be Star Schema) as read-optimized data models with fewer joins.

Data Fabric Modeling is a relatively new approach to data management that aims to unify and simplify data access and governance across an organisation. Data is no longer siloed in different departments or applications. Instead, it's brought together into a single, unified platform accessible to authorised users across the organisation. Tasks like data ingestion, transformation, and cleansing are automated for efficiency and accuracy. Data Fabric Modeling can lead to cost savings for organisations. It helps optimise resource utilisation, reduce infrastructure costs, and maximise the return on investment in data management technologies.

Data Fabric Modeling is a crucial aspect of modern data governance strategies. It provides a comprehensive framework for managing and integrating data across an organization. I emphasize the importance of establishing clear data fabric models that define how data is created, stored, processed, and shared. This not only enhances data quality and accessibility but also ensures compliance with regulatory requirements. Effective data governance, in conjunction with robust data fabric modeling, lays the foundation for informed decision-making and sustainable data management practices within an organization.

Data fabric modeling transforms data management by integrating metadata, semantics, and AI across all data sources, enhancing discovery, integration, and governance. It breaks down silos, ensuring data consistency and quality while supporting diverse data types and use cases. This model adapts to new data requirements swiftly, leveraging AI for automation and optimization. By providing a unified data layer, it simplifies access and analysis, fostering innovation and agility in decision-making. Data fabric modeling stands as a strategic approach to leveraging data as an asset, enabling efficient data use and compliance across organizations.

Data fabric modeling involves designing a unified architecture that seamlessly integrates and manages data from various sources and formats within an organization. It includes data integration, management, virtualization, governance, scalability, flexibility, analytics, APIs, microservices, and automation to enable organizations to leverage their data assets effectively for decision-making and innovation.

The goal of Data fabric modeling is to create a unified and integrated view of data across an organization. A well defined data fabric model allows seamless data flow and interactions between different systems and sources. You have to ensure that it is flexible and interconnected with the the rest of the source systems in your environment. Data fabric modeling is only as good as the value it provides! Last thing to consider access management. Security will always be necessary and ensuring that the right people have the right access positions your team for success!

Data modeling is the process of creating a representation of data structures, relationships, and constraints for a specific purpose, helping analysts, designers, and engineers to meet their business intelligence needs by: Leveraging AI engineering. Identifying changes to existing data models. Doing model-driven development. Model-driven development (MDD). Modeling big data. Adopting small and wide data models. An increasing role in data governance. To keep up with these trends: Learn new data modeling techniques and languages. Use modern data modeling tools Some data modeling best practices and standards are: normalization, denormalization, naming conventions, and metadata management.

Graphs take the data model into the boardroom. This is one of the primary strengths of a graph database from my personal experience. Business users can easily understand a graph model, and can work with you to clearly define the correct business relationships between entities. It’s a huge advantage over the relational model. While it is true that there are fewer tools on the market for graph databases, the understandability of a graph as well as the flexibility it creates from a design and development standpoint, in my opinion, outweigh the lack of tools. It may not be the right direction for every project, but I would consider it at least for every project because of the advantages it brings.

Semantic data modeling is an exciting and critically important trend. Besides enabling data integration by mapping silos to a common conceptual model it can also enhance data science modeling efforts. This is an umbrella term that includes ontologies, taxonomies, even the traditional Entity Relational models, etc. But the sub capability that I am most interested in within the context of semantic modeling is Knowledge Graphs. This can help accelerate business value from data. These are most often implemented as property graph databases. Data is physically represented in graph databases as nodes and edges. This form of representation is more versatile than the traditional relational. Note, semantic models are different from ML models.

Graph data modeling comes with its own set of challenges compared to traditional relational modeling. Understanding how to translate real-world relationships into a graph structure can be challenging, especially for those comfortable with the rigid tables and columns of relational models. It requires a different way of thinking about data and focusing on connections rather than entities alone. While the flexibility of graph models is often seen as a strength, it can also be a challenge. The lack of a predefined schema can lead to inconsistency and potential data quality issues if not managed properly Finding suitable development tools, libraries, and frameworks for building and managing graph data applications can be challenging.

Entre ces différentes types de modélisation. La modélisation de coffre de données gagne de plus en plus de popularité , du faite qu'il existe des références de mise en place dans différents secteurs d'activités, ainsi que la disponibilité de plus en plus d'expertise sur le marché.

Staying current in the fast-paced domain of data modeling necessitates a proactive approach to learning and engagement with the community and latest advancements. I usually do some strategies to ensure to remain at the forefront of data modeling trends and practices like, Engage with Professional Literature,Leverage Multimedia Resources,Pursue Continuous Education and Experiment with New Tools and Technologies.

Follow relevant hashtags, accounts, and communities on social media platforms, such as Twitter and LinkedIn. Engaging in discussions, sharing your insights, and staying connected with professionals in the field can provide real-time updates.

Na minha vis?o , para se manter atualizado de maneira a continuar atendendo as necessidades do mercado é suma importancia o uso de arquiteturas de referência para facilitar o posicionamento dos nossos conceitos e tecnologias dentro dos ecossistemas já existentes. Dessa maneira fica mais fácil visualizar o "tamanho do salto", impacto de maneira a selecionar as tecnologias que fa?am sentido dentro na nossa realidade no momento.

Well, no one can be 100% up to date in every type of modelling technique. But yes one can always choose their specialty and try to up to date in that one technology. Since most of the updates are always related to each other. Same like the updates and features present in AWS and Azure, the name is different but the meaning and functionality are almost similar.

To stay current in data modeling, explore emerging technologies like machine learning, contribute to open-source projects, and delve into industry-specific applications. Engage with professionals on platforms like LinkedIn for continuous networking, while also participating in online forums and communities. Additionally, keep up with the latest through webinars, podcasts, and events, embracing a holistic approach that combines hands-on experience, specialized insights, and continuous learning opportunities.

It is important to choose data modelling techniques that are aligned with your overall data strategy for the organisation. Important points are Data Model should be able to 1. Organise data as per your strategy 2. Support performance & governance requirements 3. Support diverse analytical needs 4. Make data accessible, support scalability 5. Support various integrations

Integrating the concepts of Data Fabric, Data Lake, Data Warehouse, and Data Mesh into a cohesive data strategy can significantly enhance an organization's ability to manage, process, and extract value from its data.

First and foremost ensure you data strategy is aligned with your business strategy. Choose data architecture based on your data strategy, business needs and rules and regulations.

When creating data products (dashboards, reports), leveraging SaaS as data source, the balance between flexibility and reusability is crucial. The challenge lies in designing a data product that can adapt to various client needs while maintaining a consistent and efficient codebase. For data engineers and analysts, it’s essential to invest time in understanding different business models to ensure the data product remains both adaptable and sustainable. Success hinges on strategic planning and a deep commitment to versatility.

It is important to think about modeling at the beginning of a data initiative. Each method mentioned here has its individual advantages and disadvantages. I always recommend weighing these up in the respective company context and making a decision accordingly. This is not possible without a great deal of experience, so involve experienced people in the project. AI, data lakehouses, data mesh and other newer approaches make the topic even bigger and more complex.