Here's how you can learn and grow in your career as a database developer by embracing your mistakes.

COMMIT is final. Make sure you don't AUTOCOMMIT :) If a query takes too long use EXPLAIN (access plan) and Index Advisors are for free (normally). If you're getting the wrong results, it's not the DBMS that's at fault, 99.9999% it's your query that's at fault. If no code has changed and you are getting errors or wrong results, maybe the data has changed :) If you have a complex query, brake it down in steps to test and see which part causes the problem. Learn the tracing, performance monitoring tools, DBMS tables that contain details of executed statements, threads etc. Use AI where possible to analyze your problem ... I asked AI My SQL query " ..." is performing badly, what should I do? MAKE MISTAKES IN DEVELOPMENT NOT IN PROD!!!!

First thing is practical, then only we can become best in our field. Before write any query, first analysis how the query actually work, do rough work on paper first & try to write query by own, then do the Google, don't directly jump any AI tool. We should have the base knowledge of all reserve keyword, which we are going to use for writing the query. AI tool directly give statement, some time it's gives wrong answer for some usecases or complex For e.g. while write join queries to add condition in where clause OR add it in join...on...and clause (this example some time gives same result/ return duplicate records / some time wrong result too) It doesn't mean that DB gives wrong result, it all depends how we write the query

Peeking under the hood pays... An enough good understanding of how the internals are structured, helps big time in connecting the dots. For NoSql databases, understand BASE and why eventual consistency is there in the first place - it is a trade-off. Ideas like log structured merge tree - versus BTrees helps big time. Knowing distributed systems design is crucial. You don't need ACID always. Understanding full table scans, application of key value pairs, object relational impedance, caching, replication, hinted handoff - these themes drive the implementations. Understanding how to best model the data - documents versus Graphs - is invaluable. There Is More Than One Way To Do It (TIMTOWTDI) is a timeless design principle.

As a database, Its good to learn and write small small query to fetch the data as exptced. Where you hould know the output before you fire a query then you can be able to judge the query correctness. If issues occur try to minimize your query in part and exeute them in steps to analyze your outcomes. More errors more opporunity to become expertise.

Maybe this sounds like something old fashioned, but the pair review is always a good way to find blind spots in our analysis, this perspective do not only apply for coding on databases, but I find this helpful for many other fields during software development processes

Seek Feedback: Don't suffer in silence. Actively seek feedback from mentors, senior developers, or even colleagues on your work, especially after encountering an error. Their insights can reveal blind spots and provide different perspectives. Struggling with complex database joins? Ask a senior developer to review your code. They might suggest alternative approaches or point out optimization opportunities.

Data exploration is one of the most interesting processes when working with data. Not only it will reveal issues with the database and your code, it will give you a better understanding about the nature of the data you're working with. That understating may provide insights on relationships between parameters, anomalies, trend and seasonality (in the case of time series), and plenty of other useful info.

To be an excellent database developer, it is almost required to understand the underpinning data. This means that you also wear the badge of a business analyst, where you understand the core data, and what the business user is ultimately trying to achieve. In this way you will understand the flow and direction of data that is needed to provide the business user with what they need. On the other hand, a DBA who does not know the data well will likely not fully understand the ramifications of their coding. One should attempt to learn as much about the data as possible, as this is the best way to apply the necessary DBA tools needed to provide the best solution possible. It's irrelevant if you do not give the users exactly what they need.

In my experience, actually I advised this to one of my team members who is in charge of delivering data to our end users, you do not only need to understand data, but first of all you need to understand the business behind the requirements, sometimes having a conversation with a SME or a key user will help you more to understand data than hours of reviewing databases diagrams, dictionaries and reading data records.

New tools are constantly appearing. This is specially true for open source technologies, such as Python. It's important to explore those possibilities to keep yourself relevant in the market. For Python, a good place to start would be Pandas, Matplotlib & Seaborn (for visualization) and sklearn. These libraries provide you with plenty of tools to run statistical analysis. If you're already a little more advanced and want to explore big data, PySpark maybe interesting for you.

Continuous Learning: The database landscape is constantly evolving. New technologies, security practices, and optimization techniques emerge regularly. Make continuous learning a habit through online courses, conferences, or attending meetups focused on database development. Example: Perhaps you're working with a new NoSQL database. Participating in online tutorials or workshops can equip you with the knowledge to avoid common pitfalls associated with that specific technology.

Practice Diligently: The more you practice writing queries, designing database models, and working with data, the less prone you become to errors. Dedicate time to build personal projects, contribute to open-source database projects, or participate in coding challenges specific to database development. Example: Feeling shaky on writing efficient SQL queries? Set aside some time to practice writing complex queries against sample datasets or by participating in online coding challenges focused on SQL.

Reflect Regularly: Take time to reflect on your mistakes and learning experiences. Journal your learnings, track the errors you encounter, and analyze what went wrong. Over time, you'll identify patterns and develop strategies to avoid repeating them. Remember, embracing mistakes is a growth mindset that will propel you towards becoming a more confident and skilled database developer. By following these tips and adding your own personal experiences, you can turn setbacks into stepping stones for a successful career!

I can't express how important looking back at old work is as you learn. One of my favorite interview questions was to ask candidates to think back on a project from the past and ask if there is anything they would do differently. If the candidate answered "No." it told me a lot about whether they reflect on past work or are truly learning. After two decades building databases, there was never a data model that couldn't use improvement.

Here's What Else to Consider: 1. Share Your Learnings: Don't keep your valuable insights to yourself! Share your learnings from mistakes with colleagues through blog posts, internal presentations, or discussions. This fosters a collaborative learning environment and helps others avoid similar pitfalls. 2. Automate Testing: Implementing unit testing for your database queries and procedures can help catch errors early in the development process. Consider using database-specific testing frameworks to automate this process. 3. Stay Up-to-Date on Security: Database security breaches can be catastrophic. Stay updated on the latest database security vulnerabilities and best practices to protect sensitive data.