How do you balance performance and flexibility in your data models?

For managing big data analysis, I encounter that performance is more imprtant than flexibility. Minor model adjustment requires significant amount of time and data reprocessing.

When you are handling huge volumes of data, we should have a very clear understanding of contribution and consumption patterns of data. We might have to come up with various marts depending on consumption patterns and requirements. This will provide better handle on performance aspects. Majority of the times, not all downstream systems need a real time data, we can use this to our advantage in designing a robust system.

Flexibility may take precedence in situations where business requirements are dynamic and the data model needs to adapt to changing needs. This could be the case for businesses undergoing frequent transformations, adopting new technologies, or expanding into new markets. A flexible data model can accommodate these changes without requiring significant redesigns or re-architectures

Data profiling should be the step 0 to understand the characteristics, sources, and quality of your data. Business team should be involved right away so you can formulate business rules, objectives and expectations. Another dimension which is usually missed is volume, how much data is generated, updated, deleted and also the archiving strategy.

Must understand how systems will consuming your data on your model based in operations type. Take in mind these following principles to decide if apply normalization or denormalization techniques: > "Data will access togheter; should be store togheter". > "The data duplication is good, if it isn't have harm the consistency". > "Separate the immutable from mutable".

It is not always all the normal forms get applied while designing data models. One of the best practices is to allow denormalization. We can allow some non-key attributes to depend on columns other than the primary key. We do this, for performance improvement. Full normalization will cost on performance and results.

The partition give the performance and flexibility to use part of specific columns (vertical partition) or row (functional or horizontal partition, as well sharding) on specific and dedicated space to work your queries.

In addition to the foundational principles of data quality and governance, consider integrating automated data quality checks and self-healing mechanisms into your data models. Automated checks can continuously monitor data integrity and flag issues in real-time, reducing the manual effort required to maintain quality. Self-healing mechanisms, on the other hand, can automatically correct certain types of data anomalies, such as formatting errors or missing values, ensuring that your data remains clean and reliable without human intervention. These enhancements can significantly improve both the performance and flexibility of your data models by minimizing disruptions caused by data quality issues.

Data models need refinement for improving performance and flexibility of use. Adding a column in a table might prevent scanning another high volume table. We need to do the change. Over the span of time, two entities may have been identified having many-to-many relationship, which needs a new table introduced. We need to do the change. Adding a new constraint, establishing new relationship, restructuring columns (for nullability, for setting ranges, for setting defaults, for changing data types and formats): all these need changes in the model first.

As an assistant professor in atmospheric modeling, balancing performance and flexibility in data models is crucial. This involves continuously testing, monitoring, refining, and gathering feedback to keep models aligned with current and future needs. By regularly assessing performance, identifying gaps, addressing issues, and exploring opportunities for optimization, we make our models both efficient and adaptable. This ongoing process allows us to maintain alignment with our objectives and deliver valuable, reliable data for informed decision-making.

When constructing the logical data model (LDM), aim to maintain a high level of normalization to ensure flexibility. During the implementation of the Physical Data Model (PDM), it is important to choose a database capable of handling complex multi-way joins. This approach reduces the need for excessive denormalization to enhance performance, which would otherwise compromise flexibility. When denormalization is necessary, it is advisable to leverage the underlying database structures such as indexes and partitioning instead of creating separate denormalized structures. This allows the DBMS to handle additional layers on top of the data elements, which optimizes performance while retaining the flexibility to accommodate future requirements.