Conversion Functions is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly.
SELECT UPPER(name), ROUND(price, 2), CURRENT_DATE FROM products;Use the smallest correct statement, test it with representative data, and verify constraints and performance before production use.
A sales manager needs a monthly report showing revenue, order counts, rankings, and changes over time without exporting raw data to a spreadsheet.
BEGIN;\n-- Preview the exact target set first.\nSELECT id FROM orders WHERE status = 'pending';\n-- Apply the Conversion Functions operation, verify affected rows, then commit.\nCOMMIT;The query returns only the intended rows and columns, with deterministic ordering where order matters.
This lesson reuses picostore. Relevant tables: customers, products, orders, order_items. Keep the starter rows from the Introduction lesson so results remain comparable.
SELECT category,\n COUNT(*) AS products,\n ROUND(AVG(price), 2) AS average_price,\n SUM(stock) AS units_available\nFROM products\nGROUP BY category\nHAVING SUM(stock) > 0;Run the verification query, compare the returned rows with the starter data, and explain why every included or excluded row is correct.
This catalog covers the practical public SQL surface. Check the exact server-version documentation before using a vendor-specific item.
CAST(value AS type) — standard explicit conversionCOALESCE — replace NULL with the first available compatible valueNULLIF — turn a sentinel value into NULLCONVERT(value, type) — convert data typeCONVERT(text USING charset) — convert character setBINARY — convert to binary stringDATE / TIME — extract or convert date/time partsSTR_TO_DATE — parse formatted date textFORMAT — format a number for displayvalue::type — PostgreSQL cast shorthandTO_CHAR — format dates and numbersTO_DATE / TO_TIMESTAMP — parse formatted textTO_NUMBER — parse formatted numeric textENCODE / DECODE — binary-text encoding conversionCONVERT_FROM / CONVERT_TO — character encoding conversionStart with a small customer table and retrieve active customers in a predictable order.
SELECT customer_id, name, email\nFROM customers\nWHERE status = 'active'\nORDER BY name;Use a CTE and a window function to rank customer revenue while keeping the query readable and testable.
WITH customer_revenue AS (\n SELECT customer_id, SUM(total_amount) AS revenue\n FROM orders\n WHERE order_status = 'completed'\n GROUP BY customer_id\n)\nSELECT customer_id, revenue,\n DENSE_RANK() OVER (ORDER BY revenue DESC) AS revenue_rank\nFROM customer_revenue\nORDER BY revenue_rank, customer_id;Prefer standard SQL functions when portability matters. MySQL and PostgreSQL differ most in date formatting, type conversion, string aggregation, and error handling for invalid values.
Conversion Functions is a practical SQL concept used to design, query, secure, operate, or analyze relational data correctly.
SELECT UPPER(name), ROUND(price, 2), CURRENT_DATE FROM products;Use the smallest correct statement, test it with representative data, and verify constraints and performance before production use.
A sales manager needs a monthly report showing revenue, order counts, rankings, and changes over time without exporting raw data to a spreadsheet.
BEGIN;\n-- Preview the exact target set first.\nSELECT id FROM orders WHERE status = 'pending';\n-- Apply the Conversion Functions operation, verify affected rows, then commit.\nCOMMIT;The query returns only the intended rows and columns, with deterministic ordering where order matters.
This lesson reuses picostore. Relevant tables: customers, products, orders, order_items. Keep the starter rows from the Introduction lesson so results remain comparable.
SELECT category,\n COUNT(*) AS products,\n ROUND(AVG(price), 2) AS average_price,\n SUM(stock) AS units_available\nFROM products\nGROUP BY category\nHAVING SUM(stock) > 0;Run the verification query, compare the returned rows with the starter data, and explain why every included or excluded row is correct.
This catalog covers the practical public SQL surface. Check the exact server-version documentation before using a vendor-specific item.
CAST(value AS type) — standard explicit conversionCOALESCE — replace NULL with the first available compatible valueNULLIF — turn a sentinel value into NULLCONVERT(value, type) — convert data typeCONVERT(text USING charset) — convert character setBINARY — convert to binary stringDATE / TIME — extract or convert date/time partsSTR_TO_DATE — parse formatted date textFORMAT — format a number for displayvalue::type — PostgreSQL cast shorthandTO_CHAR — format dates and numbersTO_DATE / TO_TIMESTAMP — parse formatted textTO_NUMBER — parse formatted numeric textENCODE / DECODE — binary-text encoding conversionCONVERT_FROM / CONVERT_TO — character encoding conversionStart with a small customer table and retrieve active customers in a predictable order.
SELECT customer_id, name, email\nFROM customers\nWHERE status = 'active'\nORDER BY name;Use a CTE and a window function to rank customer revenue while keeping the query readable and testable.
WITH customer_revenue AS (\n SELECT customer_id, SUM(total_amount) AS revenue\n FROM orders\n WHERE order_status = 'completed'\n GROUP BY customer_id\n)\nSELECT customer_id, revenue,\n DENSE_RANK() OVER (ORDER BY revenue DESC) AS revenue_rank\nFROM customer_revenue\nORDER BY revenue_rank, customer_id;Prefer standard SQL functions when portability matters. MySQL and PostgreSQL differ most in date formatting, type conversion, string aggregation, and error handling for invalid values.