What is the most effective approach to data modeling trade-offs?

Before engaging stakeholders, ensure your data engineering team has a clear list of questions. Collaborate to align on each question's wording for stakeholder clarity. Given the high demand, use a well-designed front end to organize and manage requests. In Data Mesh architectures as an example, different domains handle challenges, but backend solutions often share commonalities. Diverse data engineers gain expertise by addressing various company needs. It's also important to have a clear vision of the costs of each technology documented to explain it based on the stakeholders's needs.

Effectiveness: The most effective data modeling approach begins with a comprehensive understanding of business requirements. Engage with stakeholders to identify key metrics, reporting needs, and user expectations. Trade-offs: The trade-off involves balancing the depth of understanding the business requirements with the speed of model development. Rushing through this phase may lead to a model that doesn't fully address business needs.

This is the very first and the most crucial step! Understanding the business needs is essential to any data related exercise and it's a reason I always advocate for business people and not data scientists to lead data projects in a business environment (Academia might be different). Data is a mean and business needs is the end; failing to clearly define and identify those needs will only result in getting an overwhelming volume of data and reports with which we have no idea what to do.

Having a grasp of the business objectives and needs is essential for a data engineer, as it enables the alignment of technical strategies with the overarching organizational goals. This understanding ensures that data systems are custom-fir to meet specific business demands, like data sources, formats, quality, the expected data volumes, velocity.

Prioritize Prioritize Prioritize! Always give priority to high-value queries, the rest can follow, this should be the dogma to follow whenever there is a trade-off issue. Thanks!

here a real-life example: When analyzing website clickstream data, we initially used a relational database. But the sheer volume and interconnectedness of clicks overwhelmed it. Switching to a graph database was a game-changer, revealing hidden patterns and user journeys we wouldn't have seen otherwise.

Effectiveness: Select a data model that aligns with the identified business requirements. For structured data, relational models might be appropriate, while NoSQL models could suit unstructured or rapidly changing data. Trade-offs: The trade-off lies in balancing the advantages of a chosen data model (e.g., simplicity, scalability) against potential limitations (e.g., query complexity, normalization challenges). Consider factors like query patterns, data volume, and growth projections.

💡 Data engineers know that data modeling and database modeling are connected and separated at same time: data modeling is conceptual structured representation from real world that will be translated into a database model to give non-functional features and manage it. 🔥 You can implement data modeling in any database modeling (document, tabular, graph, time-serie, etc) if last one support the structure of data modeling. ✨So understand the properties of database modeling to check which can match with non-functional requirements for business requirements.

To choose the right data is what makes the difference in trading. If you use an ML approach you have 2 options: -To use i.i.d. data -To use non-i.i.d. data Know how and when to use the right one will increase or decrease your PnL

Data engineers need to get a grasp of data modeling best practices like normalization, denormalization, abstraction and consistent naming conventions. These methodologies streamline database structures, ensuring efficient storage, optimize query performance and enhance scalability. Incorporating these practices not only simplify database management but also fortifies system integrity, contributing to better data systems.

i will give you a real real-life example from my experience as head of data modeling at the a Bank: By applying proper data normalization in a financial data model, I eliminated redundancy and reduced storage space. This not only saved costs but also speed up queries, allowing analysts to answer critical financial questions faster.

Effectiveness: Rigorous validation and testing ensure that the chosen data model performs well in real-world scenarios. This involves checking data integrity, assessing query performance, and confirming that the model meets reporting and analytics needs. Trade-offs: The trade-off involves allocating time for thorough testing versus expediting the implementation. Rushing through validation may lead to overlooked issues, impacting data quality and system performance.

When we talk about validating and testing the ML model in the field of trading: -Prediction != Performance -ML metrics != Performance -Backtest != ML metrics

💥 When data team have non-functional and functional requirements, use case that business will apply and how data modeling is supported from database system to achieve this functional requirements: emulate workload! 📋 Maybe you can several data modeling schemas for differents database modeling and want to check which one fix to business requirements better; this is the best way to verify it. 📏 Reemember have for each features, a range of value to generate a integral evaluation based in impact categories.

For data engineers, the continuous iteration and evolution of data models are necessary, it ensures these data models can flexibly adapat to evolving business needs and intricate data landscapes. This iterative approach maintains models responsiveness, aligning them closely with the projects requirements. Engineers, through persistent iteration, craft adaptable frameworks that consistently uphold relevance, accuracy and effectiveness within the ever-evolving business sphere.

In my opinion; there are 3 points that help to balance evolution of data modeling while preserve flexibility and adaptability properties as possible: ✔️ Separate immutable data from mutable data. ✔️ Normalization form. ✔️ Handle time aspect for records well. Any business requirements could change data modeling; but these points can avoid great negative impact on it

Yes compromises are daily decisions most managers are forced to make and I personally always prefer to form committee in charge of such projects instead of a one man show; having colleagues from different perspectives will certainly help make those comprises whilst at the end strategic milestones are met and key objectives are achieved.