What are the key strategies for delivering high-quality database solutions?

Designing any database solution on Azure. On a recent e-commerce project, i worked with BA team conducting extensive requirements gathering sessions with the client's business and technical teams to capture both functional needs like order processing workflows and non-functional needs like scalability for traffic spikes. We included security and compliance stakeholders to map out encryption, access controls and auditing needs upfront. Validating requirements across multiple sessions was key - as the project evolved, additional needs or conflicts arose. For example, the expected data volumes changed which led us to re-assess RDBMS vs NoSQL choices. Tracking requirements in Azure DevOps allowed unified understanding across squads.

Here are key strategies for delivering high-quality database solutions: Requirements Gathering: Start by thoroughly understanding the requirements of the database solution. Data Modeling: Design a robust data model that accurately represents the business domain. Choose an appropriate database schema (e.g., relational, NoSQL) based on the data requirements. Normalization and Denormalization: Normalize the database schema to minimize data redundancy and maintain data integrity. However, selectively denormalize for performance optimization when needed. Data Integrity and Constraints: Enforce data integrity through primary keys, foreign keys, unique constraints, and check constraints. Ensure that data quality is maintained at all times.

Delivering high-quality database solutions requires a combination of technical expertise, careful planning, and effective communication. Here are key strategies to ensure the delivery of high-quality database solutions: Understanding Requirements: Begin by thoroughly understanding the business requirements for the database solution. Engage with stakeholders to gather detailed information about data needs, access patterns, and performance expectations. Data Modeling: Use effective data modeling techniques to design a database schema that accurately represents the business entities, relationships, and rules. A well-designed data model serves as the foundation for a high-quality database solution. Normalization and Denormalization:

Delivering high-quality database solutions requires careful planning, attention to detail, and adherence to best practices. Here are some key strategies to ensure the quality of your database solutions: * Requirements Analysis: * Database Design: * Performance Optimization: * Security Measures: * Data Quality Assurance: * Backup and Recovery: * Scalability and Availability: * Version Control and Change Management: * Testing and Quality Assurance: * Continuous Monitoring and Optimization: Continuously monitor the performance, availability, and security of database solutions in production environments. Use monitoring tools to track key performance metrics, identify issues proactively, and optimize database configurations

Selecting the optimal database technologies is crucial for Azure projects. For a geospatial dataset, the combination of variety, velocity and volume of data ruled out traditional relational databases. After evaluating options, we chose Azure Cosmos DB for its robust NoSQL capabilities and geo-replication features. The data formats and querying needs also guide technology selection. For a cloud data warehouse, we opted for Azure Synapse over Databricks as its SQL analytics engine was ideal for complex BI queries. Databricks was better suited for our ML workloads. I build lean proof-of-concepts on Azure to validate technologies at small scale. These POCs helped us accurately gauge factors like query performance.

If your product supports multiple flavors of RDBMS, for example both SQL Server and Oracle, you need to understand that you can't just copy-paste code from one platform to another, with no regard for each platform's best practices and standards, and expect it to end well. Some ISVs that initially built a product to run on Oracle, and then decided to adopt SQL Server as well, tend to apply an Oracle mindset to SQL Server and then blame the RDBMS when it doesn't "behave" as expected. This while completely ignoring the fact that they're very different platforms with their own specific standards, best practices, features, and, not to mention, their own SQL dialect. For best results, be mindful of the specifics of the RDBMS you're working on.

I don't see any mention of what is the absolute most important thing to do in database project. It needs to be done even before the first requirement is envisioned. What is that thing? You MUST bring on a good "hybrid" systems/application DBA and you MUST do so before you even think of a requirement. I've seen too many projects that start without one and they quickly become a victim of really bad modeling, bad practices, poor performance, poor maintainability, inability to scale and much worse. And then, you need the second required "ingredient"... you actually have to listen and do what they say. DevOps, CI/CD, etc, won't save you here. Get a good DBA and do what they say. Period. A couple of good DB Developers would help a lot!

Embrace a DevOps culture: Integrate database development and deployment with the broader software lifecycle for seamless updates and continuous improvement. We configured database CI/CD to lower time to release and streamline the review process and keep track of the changes.

Usando bases de datos Oracle bien configuradas y con un seguimiento en cuanto a capacity planning es la mejor opción para bases de datos. Tenes escalabilidad, flexibilidad y versatilidad en un solo producto.