You’re starting a new database development project. What are the most important methodologies to consider?

Gather the requirements from the business in a detailed fashion. Discover the business rules on data as much as you can. Create a logical data model of your database showcasing the linkage between different entities. Review it with stakeholders. After the go-ahead prepare the physical database model, create necessary relationship between entities. Develop an Agile Methodology, deliver the functionality sprint-wise by designing the database tables in iterations, provide demonstration and get feedback from the audience. Make necessary changes from the feedback received and continue further.

With new database development projects, it is best for the model to fit the majority of business use cases. Important factors to include is how it will be accessed and updated while keeping performance in mind. Lastly the development cycle is important - one must always factor in the continuous improvement part of the development effort.

From my experience in the field, it can be said that when combining artificial intelligence and marketing skills, the software engineering process includes several main stages. These requirements include assembly, analysis, design, implementation, testing, and maintenance. Each step contributes to building a strong and efficient database system

Begin by learning project objectives from the product owner. Is it workflow, transactional, BI, or some combination? This will influence your platform and design choices. Where is data coming from and flowing to? How large will it be? Retention requirements? Single-tenant or multi? Next is conceptual modeling. This may mix NoSQL and relational databases like Hadoop or Spark. You need to understand the impact of conceptual design decisions. During logical modeling you'll define entities, relationships, cardinality, and mapped object relationships to the application. The model may include datasets or documents. Begin implementation, with a Minimum Viable Product to validate your design early and often, along with its interfaces and ETL.

At the start of a complex design project, I like to go an inch deep and a mile wide to ensure that the design elements fit together. Two week sprints work well for this. I create sprint reports/demos - just screen shots early on - that I label as “Value Delivered.” I make these available to as wide an audience as possible to get feedback. This time-boxed engagement process, I find, builds a foundation of trust that opens the door to innovation.

Data design happens before a single line of code or scripting takes place. Represent the business relationships in a logical data model, then generate the physical model from the logical, resolving all many to many relationships.

Embrace Agile and DevOps methodologies for your database development project. These approaches promote collaboration, continuous integration, and early error detection, which are critical for successful database development.

There is no single way of data modeling. The model heavily depend on the type of data, the velocity of data arrival if any, data variety, veracity, and data volume. In some domains, object oriented databases are the one to go to. If the data velocity is limited and data can by described in primitive types, relational databases are the best choice as it is simple, clear, and it has a huge community support. We should never forget about the NoSQL that is taking over. In all cases, when modeling data, it is not enough at all to model how data is going to be stored. Triggers, stored procedures, stored functions, timed processes, accessibility and data views are very important components of the model that are usually overlooked.

Data modeling lays the foundation for our database design. Through entity-relationship modeling, we define the structure and relationships of our database schema, ensuring that it accurately reflects the business domain and supports efficient data operations.

Set up your foundation. I always start with a rough diagram (draw.io, milanote, figma) drawing out my tables. I then map out the relationships and save this as a living document for my team to access. If and when the database evolves it's nice to have a tool like SSMS that can generate an entity-relationship diagram for you. If you are using a tool on top of the DB like Entity (SQL) or Mongoose (NoSQL) you can make your Models (in code) very extensible if you take your time planning. On top of typical DB Constraints, you can extend custom methods, apply middleware and validation directly on your models.

Always normalize unless you are building reporting. Reporting requires faster access, sometimes this requires flattening structures and decoding lookup values.

It is important to do a schema with the metadata needed and the type of input the DB is going to feed on. If the input is going to be manually one strategy can be to set drop-down lists, check boxes. Also, implement thesaurus and dictionaries. For text inputs to implement hints to guide the user on how to correctly use a standard format.

Database normalization is a critical process for organizing data in a relational database to minimize redundancy and dependency, thereby improving data integrity and reducing anomalies. Understand the principles of normalization (e.g., First Normal Form, Second Normal Form, Third Normal Form) and apply them appropriately during the database design phase to optimize data storage and maintainability.

Normalization {3rd normal form or higher) is considered one of the basic requirements for data consistency and correctness. However, nothing comes for free. Normalization does affect the efficiency of query execution. That is why I have seen many companies avoid 3rd normal form if they have any time constrains on query handling and execution. This requires database architects to make sure consistency and correctness of data, which should be automatically handled by 3rd normal form, to be handled by triggers and stored procedures. In this case the modeling and design phase of the database does take sometime to make sure that normalization is mimicked through the workflow implementation.

The approach to the project depends upon the size of the project. Is the database project for a small subset of business functionality or is it for an enterprise application? The developer should have thorough understanding of the business process or business in general that they are representing in their schema. The data structures become the "ground truth" for the application being built. It is a nightmare is to develop an application against a database that doesn't lend itself to intuition! Degree of normalization is important to consider and is a balancing act between organization and bloat. Other things to be aware of: performance and scalability, disaster recovery, security, uniformity and standards/naming convention.

Need start first with ensuring you have the right people and know their strengths. The data itself needs fully understood and what is needed from it. Start listing different tasks needed done to progress the build then start sprinting in an Agile manner to begin the work. Monitor the progress and alter to suit as needed as there may well be sequential like tasks initially until development and data becomes available to work with.

I would never send my SQA team a new feature without it going through very capable hands. One thing is to have a "core" testing script. After my testing, UNIT testing with 1 or more other devs, I would send it to SQA. If it was an enhancement I would say HOW is this feature supposed to work, the expected inputs and outputs, and just let them play and try to break it. Some folks might find this annoying, but I would much rather have an internal colleague discover a possible flaw or "clunkiness" than a client/user.

Just as builders stress-test structures, database testing is critical to ensure your system operates under various conditions without failure. This involves conducting systematic tests to validate the integrity, performance, and security of the database. Effective testing identifies potential issues before going live, safeguarding against data loss, corruption, and unauthorized access.

Documenting the database design, schema, metadata, and business rules is essential for facilitating understanding, communication, and maintenance of the database solution. Create comprehensive documentation that describes the purpose of the database, its components, data definitions, relationships, and dependencies. Documentation serves as a valuable reference for developers, administrators, and stakeholders throughout the project lifecycle.

Thorough documentation is often undervalued, yet it’s crucial for the longevity and scalability of your database system. Comprehensive documentation ensures that anyone who interacts with the database, from developers to database administrators, can understand its design, functionality, and maintenance procedures without needing to decipher the original developers’ intentions from the code alone.

Choosing a database isn't just a checkbox on a to-do list. It's critical to ensure your database aligns with your project's needs and performs well. There are many database options, each with its strengths and weaknesses. One may think a data warehouse is ideal for complex data analysis. But after evaluation, might discover that data size is manageable, and a simpler relational database would suffice. This saves money and avoids unnecessary complexity. A good database evaluation goes beyond just procedures. It's a strategic step that optimizes data management for cost and maintainability. Evaluation should consider user feedback, performance benchmarks, and best practices to ensure the chosen database meets everyone's needs.

Regular evaluation of the database’s performance and scalability is vital as it grows and adapates to increased loads or evolving business needs. This ongoing assessment ensures the database continues to meet performance benchmarks and service level agreements, and remains aligned with the organization's objectives.

Most places do this, but whiteboard everything with your peers on both your area (DB) or UX/UI before even putting a single finger on that keyboard. We may have great ideas but other folks may see flaws in your design or execution that you might have missed.

Beyond these methodologies, consider the broader business and technological environment in which the database will operate. This includes planning for data security in compliance with relevant regulations, integrating with existing IT infrastructure, and considering future scalability needs. Also, engage with end-users early and often to ensure the final product effectively meets their needs and expectations.