SQL and Databases: How AI Helps Master the Profession of the Future

## Introduction The world of data is the new oil. Companies of all sizes accumulate gigabytes of information: from visit logs to customer transactions. But raw data is useless without tools to process it. This is where SQL comes into play—the structured query language that remains the de facto standard for working with relational databases (PostgreSQL, MySQL). However, traditional learning is often disconnected from reality: boring textbooks, abstract examples, and a lack of feedback. The "SQL and Databases" course on the ASI Biont platform solves this problem with AI. Artificial intelligence doesn't just generate lessons—it adapts the material to your pace, creates personalized tasks, and helps you understand the nuances of database design. And all of this is completely free, with no hidden fees. In this article, we'll explore why SQL remains a key skill in 2026, how AI accelerates learning, and what practical tasks you'll be able to solve after the course. ## Why SQL and Databases Are a Must-Have According to LinkedIn and Glassdoor reports, database specialists are among the top 10 most in-demand professions. Even if you don't plan to become a DBA (database administrator), knowing SQL is essential: * **Data analysts** — to extract insights from tables. * **Developers** — to write efficient code that interacts with databases. * **Product managers** — to independently test hypotheses without developer assistance. Key advantages of mastering SQL: 1. **Versatility.** MySQL, PostgreSQL, SQLite—dialects differ slightly. Learn one, and you'll easily switch to another. 2. **Speed.** SQL queries execute in milliseconds, processing millions of records. 3. **Reliability.** Relational databases ensure ACID transactions (atomicity, consistency, isolation, durability). ## How AI Transforms SQL Learning The course on ASI Biont uses generative AI models to create dynamic educational content. Unlike static video lessons or PDF files, the system adapts to you. ### Content Personalization AI analyzes your test answers and query errors. If you often confuse JOIN and UNION, the system will suggest more exercises on that topic. If, on the other hand, you easily handle basic SELECT, it will immediately move you to the index optimization level. ### Generation of Practical Cases You don't just write queries in a void. AI creates simulations of real databases: online stores, banking systems, logistics platforms. For example: ```sql -- Find the top 5 products by profit in the last month SELECT product_name, SUM(price * quantity) AS revenue FROM orders WHERE order_date >= '2026-05-17' GROUP BY product_name ORDER BY revenue DESC LIMIT 5; ``` This approach develops **analytical thinking**: you learn not just to write code, but to formulate business queries in the language of data. ### Instant Checking and Refactoring After submitting a query, AI not only tells you if the syntax is correct but also suggests an optimized version. You see the performance difference (via EXPLAIN ANALYZE). ## Key Course Topics The course covers the full data lifecycle—from first steps to advanced design. ### 1. Basics of Relational Databases * Tables, rows, columns, primary and foreign keys. * Data normalization (1NF, 2NF, 3NF)—how to avoid duplication. * Data types: INTEGER, VARCHAR, TIMESTAMP, JSON. ### 2. The Magic of SELECT * Filtering (WHERE, LIKE, IN). * Aggregation (COUNT, SUM, AVG, GROUP BY). * Table joins (INNER JOIN, LEFT JOIN, RIGHT JOIN). ### 3. Data Modification * INSERT, UPDATE, DELETE—with caution. * Transactions (BEGIN, COMMIT, ROLLBACK) for data protection. ### 4. Query Optimization * Indexes (B-tree, Hash)—when they are useful and when harmful. * Query execution plan (EXPLAIN). * Avoiding "Slow Queries." ### 5. Database Design * ER diagrams (entity-relationship). * Choosing between PostgreSQL and MySQL for specific

SQL and Databases: How AI Helps Master the Profession of the Future

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