Como você equilibra desempenho e flexibilidade em seus modelos de dados?

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

Balance performance and flexibility in data models is key to my work in atmospheric modeling. We focus on optimizing performance for fast, efficient data processing while ensuring flexibility to adapt to new requirements. By designing scalable, modular models, I can achieve high performance without sacrificing the ability to adjust and intergrate with evolving systems. The goal is to meet current needs while staying open to future developments.

Query Assist tables! As a Data Vault modeler, I would advise to think around the lines of Query Assist tables for Performance, while keeping original model hubs, links and relationships continue following Enterprise Ontology for favoring flexibility!

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.

Knowing your data inside and out is crucial (its characteristics, sources, quality, and volume) along with your stakeholders' objectives, drive your modeling decision. By considering the entire data lifecycle, from creation to archiving, you can select the right modeling techniques (whether relational, dimensional, or NoSQL). This approach ensure the model aligns with both the data's structure and the project's goals, optimizing performance and meeting expectiations effectively.

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.

In my working with WRF, I used normalization to maintain data consistency and flexibility, especially when dealing with complex atmospheric data. however, it can slow down performance due to more complex data relationships. For faster simulations, I rely on denormalization, which simplifies queries and boosts processing speed, even though it adds extra data. the choice between the two depends on whether I need to prioritize data integrity or performance for specific tasks.

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 atmospheric modeling field, particularly when using large datasets, indexes and partitioning are essential for improving performance. Indexes allow faster access to key atmospheric variables, speeding up queries that filter or sort data, though they add storage overhead and need maintenance as data changes. Paritioning helps break down massive tables, such as those containing meteorological data, making queries faster by limiting the scan scope and enabling parallel processing. It also simplifies data management tasks like backups and archiving. Both strategies boost efficiency, but must be balanced with resource demands and system requirements.

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 quality and governance are critical for maintaining both performance and flexibility, especially in atmospheric modeling work. Ensuring data accuracy, consistency, and completeness allows for reliable simulations and accurate predictions, while governance frameworks help manage how data is stored, accessed, and secured. By integrating these principles, we can prevent issues, such as missing values or errors, which can impact performance, and enhance flexibility through tools, such as data lineage as well as metadata management. This approach ensures that the data remains trustworthy and compliant with stakeholder expectiations throughout the modeling process.

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.