How can you encourage continuous improvement in a database automation team?

Database Autonmy Architecture - Auto sensing of bugs and cross with oracle cloud native service, download the fix and push to non-prod instance to re-play woroads - Autonomy of Peformance issues to be logged as incident and known issue, leverage Real Application Test use case to be tested on auto-play if possible on one of the cascading standby instance.

- Review time to time results and fix. - Predictive Analysis on loads, memory leaks, data loss - Add sensible test data - Implement alerts for critical tests

To ensure continuous improvement within a database automation team, it is important to embrace automation, support continuous learning, offer feedback, foster collaboration, and recognise accomplishments.

1. Create a Learning culture: Encourage team members to share their knowledge and best practices. 2. Provide Opportunities for Training and Development: Encourage certifications relevant to their roles to improve their skills and credibility. 3. Regular Feedback and Performance Reviews: - Recognise and reward accomplishments in order to foster a positive and motivated team. 4. Promote Experimentation: Allow team members to try out new tools, techniques, and methodologies.? 5. Establish Specific Goals and Metrics: Establish key performance indicators (KPIs) to track progress and identify areas for improvement. 6. Adopt agile methodologies, like Scrum or Kanban, to encourage iterative development and continuous improvement.

Creating a culture of Total Quality Management and a Lean approach is essential in this case. It might be overwhelming initially, but it becomes a habit after a while. The database automation team likely built some of it by adopting the agile methodology. However, extra principles must be taken care of, like implementing methodologies for creating customer focus, leadership, engagement of people, process approach, continuous improvement, evidence-based decision-making, and Relationship management. The team should identify the practices to create the previous principles because they are interrelated. Implementing the Japanese principle, JIDOKA, is also crucial, which means automation with a human touch.

First and foremost (doesn't matter what methodology you use) — collect the data. Set up observability stack, ensure you have the most metrics in place (they might be application specific as well as general). Decisions should not be a guess work when it comes to automation. Do not hesitate bring tools like PgTune / OtterTune or learn from them — they do help to tune the db server if the workload changes.

Being open to revisit and reevaluate previous goals, metrics and assumptions is important. Being willing to admit that efforts were made towards the wrong direction and killing off these features requires courage, and is important too.

Integrating database changes to our DevOps or Agile processes can be game changing. It can leverage database continuous delivery. When we hear "treat the database code same as like app code" some people might not agree and immediately reject the idea. And it is true at some extent but they can be put into the source control and can be treated same way as app code changes.

Feedback loops are a must at the time to detect errors and fix them quickly. Agile and DevOps principles are a must at the time to deploy changes rapidly in a dynamic and daily changing environment.

In theory these practices and standards look very flashy on paper, but in reality it's a vertical climb to get the teams to adhere to these terms. In any case start sampling with an use case to see the results to improve the process efficiency.

There are many solutions readily available for testing and reviewing codes. Some preferred solutions are Selenium Web Driver, TestNG, JUnit, and Robot Framework, however you might consider other solutions to suits your needs more. The best solution will be able to help you allow to write and execute test scripts automatically, reducing chances of human errors and increasing the testing speed.

One thing I've found useful is creating a code review checklist. A code review checklist is a predetermined set of questions and rules your team will follow during the code review process, giving you the benefit of a structured approach to necessary quality checks before you approve code into the codebase. Your checklist may include: Readability: Are there any redundant comments in the code? Security: Does the code expose the system to a cyber attack? Test coverage: Is there a need to test more cases? Architecture: Does the code use encapsulation and modularization to achieve separation of concerns? Reusability: Does the code use reusable components, functions, and services?

I'm actually disagreeing a bit with this: code reviews highly increase bureaucracy and reduce actual business productivity, which is fundamental in small, compact, high-performance teams. Nowadays, we have awesome things (plus LM models) to **automate** code reviews, and only disturb POs or managers whenever something gets flagged. Use it, please: your peers will thank you lots for the time they'll save doing something an AI does much better, and you'll get way better and cohesive reports. Testing is also fundamental, but not so much as "testing mindset": a seasoned senior or principal developer knows how to code *thinking* of tests, and does not need them to know his code will pass them. This mindset must always be transferred ASAP.

Code Review and Testing both are the key points of any Development Life Cycle. there are many code reviews and testing methods are available. However, we need to choose the options that give full flexibility and solution-specific outputs. Check a few of them are from below. - Automate repetitive and time-consuming tests. Use testing frameworks (e.g., JUnit, NUnit for unit tests). - Implement continuous integration (CI) for automated testing on each code commit - Regularly update and expand test suites as the codebase evolves. - Integrate Continuous Testing into the development pipeline. Leverage CI/CD tools to automate testing at various stages. - Use version control systems e.g., Git, CI/CD tools like Jenkins, Travis CI, or GitLab CI.

Adopting Agile can enhance your database automation team's code review and testing processes. Agile emphasizes iterative development and continuous feedback, making it perfect for integrating regular code reviews and testing into each sprint. This approach helps detect issues early, improve code quality over time, and ensures alignment with coding standards. Moreover, Agile promotes collaboration, allowing for more interactive and frequent review sessions. Techniques like pair programming further enrich this process by providing immediate feedback and boosting code reliability. Adopting Agile practices will streamline your continuous improvement efforts effectively.

I agree with what others have said here. Doing this better requires practice and observation. All data is not meaningful all the time. Knowing the core drivers/values/goals of your company helps shine the spotlight on the data/metrics/outcomes in need of improvement/consideration. Engineering teams who take an interest in the company and its customers naturally tend to care about the technical aspects affecting those outcomes. This also helps you make better choices about what to improve, enhancing the team’s ability to pick out the correct data sets or metrics to observe. All too often teams are “improving” their systems as if ticking a box. Try your best for a lasting change. That’s where the real value lies.

Continuous improvement requires continuous feedback, and it requires architects, administrators, and data engineers to listen to that feedback. Feedback comes from two primary sources: human users and process monitoring. Human users (e.g., Data Analysts) are end users of the database and should be treated as clients of the data architecture team (as well as part of the team). Process monitoring requires implementation of audit processes, ongoing analysis of performance and usage, and resource monitoring, especially in a cloud environment where inefficiency directly increases usage costs. The data architecture team should have an evaluation and collaboration process in place to review feedback and make improvements.

As long as we are not intentionally being biased and blind to the data supporting the opposing viewpoint, a data-driven approach is almost always convincing.

Neverthless to say IT solutions are never done , they are always to be finetuned with respect to the end goal required for the problem to be solved

Encourage team members to have a growth mindse where they see challenges as opportunities to learn and improve. Organize regular training sessions and workshops to enhance team members' skills and knowledge in database automation tools, techniques, and industry trends. Provide access to relevant online courses, certifications, and conferences. Promote collaboration between team members and other departments (developers, operations, etc.) to gain diverse perspectives and share knowledge. Cross-functional teams can lead to a more holistic approach to problem-solving and innovation. Hold regular team meetings to discuss ongoing projects, improvements, and areas for growth.

Fostering a culture of learning and sharing within a database automation team is vital. It empowers team members to stay at the forefront of their field, fostering both personal and professional growth. By tapping into the collective wisdom of peers and the wider community, they not only solve problems more effectively but also drive innovation in database automation. This culture not only propels careers but also contributes to the broader advancement of the field, inspiring others to embrace continuous learning and knowledge-sharing.

The learning and sharing culture should have a part dedicated to performance. An environment where team learns from Production issues and create a culture around it. Performance optimization requires specific skills. So there should be clear expectation on performance, and clear strategy to get there. All the activities that are involved will create the performance culture.

Having shared team workloads is a good driver for improving processes and the learning required for automation. Solving complex or difficult tasks as a team is a the best way for less experienced individuals to learn.

Setting aside 20% of time for experimentation and free-form projects will generally lead to a much higher return in productivity. Business leaders critically need to understand that communication bottlenecks and over-simplifications must exist. Database teams are dealing with information densities absurdly larger than what natural language is designed for. That is their role. Therefor, while constraints are the lifeblood of tech teams - being 100% constrained spells certain doom. Cultures of learning make room for this reality through training opportunities and workshops, but most of all through brainstorming, collaboration and creating time for unfocused sandboxing. Beware of hyper-focus on immediate needs. This debt catches up fast.

Introducing code review metrics can be really helpful. You can’t correct someone’s code quality without measuring it. Objective metrics help determine the efficiency of your reviews, analyze the impact of change on the process, and predict the number of hours required to complete a project. Some commonly used review metrics include: Inspection rate: The speed at which your team reviews a specific amount of code, calculated by dividing lines of code (LoC) by number of inspection hours. Defect rate: The frequency with which you identify a defect, calculated by dividing the defect count by hours spent on inspection. This metric helps determine the effectiveness of your testing procedures.

Feedback comes from all directions. Human users can provide feedback on usability, flexibility, and observability. Audit logs provide feedback on usage and operations. Performance analytics should be developed to make monitoring and alerts an integral part of the process through intuitive insights. Data architecture should receive internal feedback from data engineering, data science, data analytics, database admins, and DevOps. Each of these teams has a unique perspective and useful insight.

Sometimes corners can be cut in case of limited budget and high throughput demand -- for instance constraint checks can be turned off and data integrity checks can be automated with a separate software.

Do team programming to improve the outcome of the project especially when you have a mix of experience. The senior and junior members learn from each other as they work .

It is important to keep in mind neural as an LLM it can be utilized as an operational system, in terms of extending database automation That may take the form of the Data Steward or Data Governance operations automation, and within in context training proactive monitoring of training, which would involve things familiar to such as data profiling and data quality from BI we are exploring these capabilities now.