A DBA Dissects SQL Generated by JPA — Execution Plans for N+1 Queries, Fetch-Join Pagination, Batch INSERTs, and Bulk Operations

What should you prepare in PostgreSQL ahead of a large-scale event, and what should you check first when it goes live? Practice the response sequence before, during, and after the event with hands-on experiments in educational Aurora PostgreSQL that trigger lock waits, deadlocks, VACUUM delays, failovers, and Reader CPU spikes—along with diagnostic SQL and playbooks for seven event types.

Ahead of a major event, what should you prepare in your database, and what should you check first when it goes live? Practice your pre-, during, and post-event response sequence using training Aurora logs of Reader CPU spikes, lock waits, deadlocks, and failovers, along with diagnostic SQL.

A hands-on project for office and operations staff who need to check weekly transaction totals and errors. Compare the 44 rows in the provided sample summary sheet against two CSV files, then use Google Sheets and Claude to preserve the source data, flag duplicates and errors for review, compile weekly totals, and verify charts. Includes cartoon-style guidance, actual app screens, male AI voice-over and subtitles, and step-by-step prompts.

Do you paste in the sales table every week, check shipping inquiries, and rewrite reports? Become the operator of the virtual shopping mall Moa Life and assign tasks to Claude in Korean. This 30-lesson hands-on course covers everything from connecting data to Google Sheets calculations, dashboards, artifact reports, customer and shipping verification, and reusing your own custom skills. You’ll learn not just to receive results, but also to cross-check the numbers, correct incorrect conditions, and run the same process again next week using the same standards. You won’t need to write code, SQL, or formulas yourself. The course is based on Mac Claude Desktop and Google Sheets; paid accounts and subscription fees are not included. Korean AI male voice is used.

Analyze Excel, CSV, and database files with Claude Code using natural language—no coding required—and check the AI’s numbers against the originals to catch errors yourself. Learn through actual execution logs, from the data dictionary and source fingerprints to metric definitions, aggregation, cross-checking through two independent paths, a one-page report with numbered evidence, and skills for next month.

I’ll learn SQLP practical tuning by reproducing the execution plans from the course on my own PC using Oracle 26ai Free. I’ll evaluate 30 hands-on practice problems I created myself using measured figures (blocks read and execution counts), then write out the answers as well.

A demo takes a day; production is another matter. We’ll build a Text-to-SQL chatbot with local Claude Code, measure its accuracy against 50 questions with known answers, and show with real-world measurements how to protect a production database using guardrails, read-only accounts, privacy protections, and permissions.

I hand my work folder over to Claude Code without writing any code. It pulls together 12 Excel files from our branches, completes a one-page report, drafts 20 emails, creates an ad performance table and a meeting action-items list, checks the numbers against the originals, then saves the whole process as a skill I can run again next month with a single line.

When faced with a single slow query, what should you look at, what should you fix, and how can you prove it? This hands-on, 55-lesson course teaches you by setting up a virtual online store with one million orders on your local MySQL 8.0 and working directly with EXPLAIN, EXPLAIN ANALYZE, and index design.

Why do requests wait when queries are fast? Locks. In a hands-on project using MySQL 8.0, Spring Boot, and JPA, reproduce record and gap locks, four types of deadlocks, long transactions and connection pool exhaustion, metadata locks during deployment, and lost updates with tests. Then fix them in code and verify the fixes with the same tests.

We validate AI-proposed SQL with row-level checks, refunds, time periods, denominators, and counterexamples. We connect the verified, cleaned data to Excel formulas, selection values, charts, and web dashboards, then practice applying design skills and rerunning the analysis with new data.

Review opinion classification drafts against the source text, compare packaging, minimum order quantities, lead times, and costs in PDF and Excel quotes, and verify the figures by defining the time period, refunds, and denominator. Correct errors in actual AI responses, create reports and supporting CSVs, and repeatedly apply the process to new materials.

After learning SQL tuning, automate recurring tuning tasks. This hands-on project connects everything from collecting MySQL queries to AI diagnosis, empirical validation, handoff to the person in charge, and tracking recurrence.