How can you balance data structure flexibility and maintainability?

To balance data structure flexibility and maintainability: 1. Clearly define requirements. 2. Use standardized formats. 3. Adopt a modular design. 4. Implement version control. 5. Thoroughly document the structure. 6. Allow flexibility through parameters. 7. Conduct robust testing and validation. 8. Establish feedback mechanisms. 9. Regularly review and refine the structure.

1. Analyze data requirements to determine the necessary flexibility and maintainability. 2. Implement modular design principles to facilitate scalability while maintaining structure. 3. Utilize standardized data formats to enhance interoperability and ease of maintenance. 4. Maintain comprehensive documentation and version control for tracking changes. 5. Schedule regular reviews and refactoring to optimize the balance between flexibility and maintainability.

using standardized formats where possible, and establishing clear data governance policies. Implementing flexible schemas and allowing for data evolution while maintaining documentation and version control aids in adaptability. Regular code reviews and automated testing help ensure maintainability without sacrificing flexibility.

The agility to support of business capacity evolution and operativation inside of the data architecture depend how well the team can handle change of the data modeling. Issues like development agility, deployment of new schema, tracking of evolution schema, rollback, technical debt can be influenced by change of schema.

Can you easily add new columns to the data without requiring significant schema changes or data reprocessing? Parquet excels in this aspect due to its schema evolution capabilities. Does the format enable efficient filtering and querying of data based on specific conditions? Parquet's columnar storage facilitates efficient filtering and querying operations, especially when targeting specific columns. Does the format maintain compatibility with older versions of the format or related tools? Parquet offers good backward compatibility, ensuring older tools can still read newer Parquet files with minimal modifications. Is the format's structure and usage relatively easy to understand and implement for developers?

There are 2 points to evaluate: 1) Design of data structure: what the blocks are used to build the data structure in table and relationships? For example; the data vault is more agility and flexible than star for data warehousing cases. 2) Technical platform: How data storages and technologies give theirs functionality to implement and manage change? Use parquet in data lake is a great options to support schema evolution, backward compatibility, reading operations and efficient in storage. These points help to balance your data structure.

Schema Flexibility: Parquet, ORC, and Avro excel due to schema evolution capabilities, while CSV requires schema changes and data reprocessing for adding new columns. Query Efficiency: Columnar formats (Parquet, ORC) offer better performance for filtering and querying specific columns compared to row-based formats (CSV) or Avro's data organization. Backward Compatibility: All three columnar formats generally maintain good compatibility with older versions or related tools. Ease of Use: While Parquet, ORC, and Avro have learning curves, their popularity translates to wider documentation and community support compared to CSV.

Graph models excel at representing intricate connections between entities, making them ideal for scenarios like social networks, recommendation systems, and knowledge graphs. They can model diverse relationship types, unlike relational databases with limited relationship representation. Adding new nodes and edges is straightforward, allowing the graph to adapt to new information or changing relationships without major structural modifications. Graph models can capture a wide range of data types and relationships, reducing the need for complex data manipulation or transformation compared to other models improving maintainability.

Mais simples e prático possível para facilitar todo processo de manuten??o, sempre que criar uma nova fun??o pensar se ela realmente é importante e fundamental para melhorar a leitura do código ou se irá atrapalhar no processo de manuten??o. Por tanto, o melhor tanto para garantir a seguran?a, escalabilidade e agilidade do ETL é garantir que o código seja limpo, padronizado e simples de atuar, no caso de ferramentas vale o mesmo, as ferramentas precisam estar devidamente configuradas e documentadas para que todo processo de usabilidade seja prático para todo o time.