Free SQL Table Generator Online — No Signup Required | SabTools

A freelance developer in Indiranagar takes on a ₹80,000 contract to build a billing system for a CA firm. Day one, before a single line of Python or PHP gets written, she has to design the database schema — invoices, clients, line items, GST splits, payment status. She opens her editor, types CREATE TABLE, and pauses. Is it AUTO_INCREMENT or SERIAL? Does SQLite even support DECIMAL(10,2) the way Postgres does? Do foreign keys need ENGINE=InnoDB? Twenty minutes later she's three Stack Overflow tabs deep, copy-pasting syntax that may or may not match her target database.

This is the boring tax that every database project pays. The SQL Table Generator on SabTools collapses that twenty minutes into roughly ninety seconds — you define columns, pick data types, set primary and foreign keys, choose your database flavour (MySQL, PostgreSQL, or SQLite), and download a clean, runnable CREATE TABLE statement. No syntax hunting, no dialect mistakes.

Why the same CREATE TABLE syntax doesn't run on three databases

SQL is one of those technologies that pretends to be standardised but isn't. ANSI SQL exists on paper; in production, MySQL, PostgreSQL, and SQLite each speak their own slightly different dialect. A schema that runs cleanly on a developer's local SQLite can throw three errors on the staging Postgres box at AWS Mumbai. A few of the friction points:

• Auto-incrementing IDs. MySQL uses INT AUTO_INCREMENT. PostgreSQL prefers SERIAL or BIGSERIAL (or GENERATED ALWAYS AS IDENTITY on newer versions). SQLite uses INTEGER PRIMARY KEY AUTOINCREMENT — and quietly does the wrong thing if you write INT instead of INTEGER.
• Boolean columns. PostgreSQL has a real BOOLEAN type. MySQL aliases it to TINYINT(1). SQLite has no boolean at all — you store 0 or 1 in an integer column.
• String types. VARCHAR(255) works everywhere, but PostgreSQL devs typically use TEXT with no length limit, while MySQL still rewards you for picking precise lengths because of how rows are stored.
• Timestamps and defaults. CURRENT_TIMESTAMP is standard. But ON UPDATE CURRENT_TIMESTAMP is a MySQL-only convenience; in Postgres you write a trigger.
• Foreign keys in SQLite. They're declared but not enforced unless you turn on PRAGMA foreign_keys = ON per connection — a footgun that bites every junior developer at least once.

The generator handles each of these quietly. Pick MySQL and it emits AUTO_INCREMENT with ENGINE=InnoDB DEFAULT CHARSET=utf8mb4. Switch to PostgreSQL and the same column becomes SERIAL PRIMARY KEY. Switch to SQLite and you get the INTEGER PRIMARY KEY AUTOINCREMENT form. The schema you design is portable; the SQL you copy is dialect-correct.

Designing a real schema: a GST-compliant invoice table

The fastest way to understand the tool is to build something most Indian developers eventually build — an invoice table for a small business under the GST regime. Imagine you're consulting for a Surat textile trader doing roughly ₹35L turnover annually. The invoices table needs to capture: invoice number, customer GSTIN, invoice date, taxable value, CGST, SGST, IGST, total, and payment status.

Here's how you'd structure it column by column:

• id — INTEGER, primary key, auto-increment. Every table needs a synthetic key, even when you have a "natural" invoice number.
• invoice_number — VARCHAR(20), NOT NULL, UNIQUE. Something like INV-2025-00142. The UNIQUE constraint prevents accidental duplicates if the application logic ever misfires.
• customer_gstin — VARCHAR(15). GSTIN is always 15 characters — two digits for state code, ten for PAN, one entity code, one Z, one check digit. Fix the length and you get free validation.
• invoice_date — DATE, NOT NULL, default CURRENT_DATE.
• taxable_amount — DECIMAL(12,2). Twelve digits with two after the decimal handles invoices up to ₹99,99,99,999.99, which is more than enough for a small trader. Never use FLOAT for money — floating point will quietly turn ₹118.00 into ₹117.99999999 and your reconciliation will be off by paise for years.
• cgst, sgst, igst — DECIMAL(10,2), default 0. For intra-state sales CGST and SGST are populated; for inter-state IGST takes the full tax amount.
• total_amount — DECIMAL(12,2). The sum of taxable + taxes.
• payment_status — VARCHAR(10), default 'PENDING'. Better still, on Postgres or MySQL 8+, use an ENUM-style CHECK constraint limiting it to PENDING/PAID/CANCELLED.
• created_at — TIMESTAMP, default CURRENT_TIMESTAMP. Always log when a row was inserted; you will thank yourself during an audit.

Feed these into the generator, pick MySQL, and you'll get back a fully-formed statement you can paste straight into your migration file. For an 18% GST sale of ₹10,000 taxable value within Gujarat, CGST and SGST would be ₹900 each, totalling ₹11,800 — exactly the kind of figure you'd cross-check using the GST calculator before storing it.

Picking the right database for your project

The three options aren't interchangeable. A quick decision matrix that holds up for most Indian developer use-cases:

SQLite is the default for anything that doesn't need a network database — mobile apps, desktop tools, single-user billing software for a Kirana shop, prototyping. The entire database is one file. You can email it. A small business owner in Jaipur running an Electron-based inventory app for a single PC is best served by SQLite. It comfortably handles tables with a few lakh rows on commodity hardware.

MySQL (or its open-source twin MariaDB) is the safe choice for typical web applications — WordPress sites, Laravel/Django apps, the e-commerce store a Pune freelancer ships for a local boutique. Shared hosting almost always offers MySQL. Most Indian managed hosts — Hostinger India, BigRock, GoDaddy India — give you a phpMyAdmin-fronted MySQL by default.

PostgreSQL is where you go when correctness, JSON handling, full-text search, or analytical queries matter. Most Indian fintech startups — and anything dealing with money at scale — sit on Postgres. If your schema involves JSON columns (storing UPI transaction payloads, webhook responses from Razorpay/Paytm, or address blobs), Postgres's native JSONB type is unmatched. AWS RDS Mumbai, Supabase, and Neon all make managed Postgres trivial to provision.

The generator lets you flip between all three with one click, so you can prototype on SQLite locally, then regenerate the same schema as MySQL for a client's shared hosting or Postgres for a more serious deployment.

Data types that bite Indian developers

A handful of column choices cause more bugs in Indian projects than any others:

1. Storing phone numbers as integers. Don't. Indian mobile numbers start with 6, 7, 8, or 9 — that's fine — but the moment someone enters a country code (+91), a space, or a leading zero, your INT column truncates or rejects. Use VARCHAR(15) and validate at the application layer.
2. Storing amounts as FLOAT or DOUBLE. Already mentioned, worth repeating. EMI values, SIP corpus projections, GST splits — all of them must live in DECIMAL columns. If you're persisting EMI calculations done via the EMI calculator — say a ₹50L home loan at 8.75% for 20 years producing an EMI of ₹44,186 — you want that 44186.00 stored exactly, not as 44185.99999998.
3. Storing PIN codes as integers. A Delhi PIN code like 110001 loses its leading zero if you store it as INT and the front-end ever serialises it. Always VARCHAR(6) for PIN codes.
4. Storing PAN/GSTIN/Aadhaar as TEXT with no length cap. These have fixed lengths — PAN is 10 characters, GSTIN is 15, Aadhaar is 12 digits. Use VARCHAR with the exact length and a CHECK constraint where supported.
5. Storing dates as VARCHAR. Tempting because Indian date formats vary (DD/MM/YYYY versus DD-MM-YYYY versus the ISO YYYY-MM-DD). Resist. Use DATE or TIMESTAMP and format at the presentation layer.

Primary keys, foreign keys, and indexes

The generator supports declaring primary keys, foreign keys, NOT NULL, UNIQUE, and DEFAULT — the constraints that do 90% of the real work in a schema.

A few rules of thumb that translate directly into how you configure each column:

• Every table needs a primary key. Even a junction table linking, say, students to courses for a Kota coaching centre's database — give it an auto-increment ID. It makes deletes, updates, and ORM mappings vastly cleaner.
• Foreign keys are a contract, not a suggestion. If your order_items table references orders(id), declare the foreign key. Yes, it's a small performance cost on insert; it's a vast saving on debugging when your data goes orphaned.
• Index every column you'll filter or join on. If you'll query invoices by customer_gstin, index that column. If you'll order them by invoice_date, index that. Indexes are cheap on read-heavy tables and expensive on write-heavy ones — most billing tables read 10x more than they write, so err on the side of more indexes.
• Use NOT NULL aggressively. A nullable column is a column that will eventually contain NULL, and your application has to handle that everywhere. Decide early: is this field required or genuinely optional?

Working backwards from data you already have

A common Indian developer scenario: a client hands you an Excel sheet — say a customer list, a product catalogue, or a year's worth of expense entries from a homemaker tracking household budgets across grocery, electricity, school fees, and Swiggy. You need to turn that into a database table.

The workflow that actually works:

1. Convert the Excel sheet to JSON (most spreadsheets export to CSV; small scripts or online converters take CSV to JSON).
2. Paste the JSON into the JSON to table viewer to inspect the columns, spot inconsistent types, and identify which fields are actually populated.
3. For each column you see in the viewer, decide its SQL type — text becomes VARCHAR, numbers with decimals become DECIMAL, dates become DATE.
4. Open the SQL Table Generator and recreate the schema with proper constraints.

This sequence — explore in JSON, design in SQL — saves you from the classic mistake of mirroring an Excel sheet column-for-column without thinking about what's actually a primary key, what should be normalised into a separate table, and what's just noise.

Sketching the schema before you generate it

For anything more complex than three or four tables, draw it before you build it. An e-commerce schema with users, products, categories, orders, order_items, payments, and addresses has at least six foreign-key relationships you need to keep straight. A flowchart for the entity relationships or a mind map of the schema with one branch per table makes the dependency order obvious — you create the users table before orders because orders.user_id references it.

Designers who skip this step end up running their migrations in the wrong order, hitting foreign-key errors, and patching the schema with ALTER TABLE statements that should have been part of the original CREATE.

From CREATE TABLE to documentation

Schema design isn't done when the SQL runs. A schema that nobody but you understands is technical debt. Two cheap habits make a big difference:

First, write a one-line comment under each column explaining what it stores in business terms — not "varchar 15" but "Customer GSTIN; 15 chars; first 2 digits are state code". Both MySQL and PostgreSQL support column comments inline.

Second, keep a human-readable table of the schema in your project README. A markdown table works well — generate one using the HTML/Markdown table generator, paste it into the repo, and update it whenever you alter the schema. New developers onboarding to the project will read this before they touch the database.

A quick checklist before you ship a schema to production

• Every table has a primary key — usually an auto-increment ID.
• Every foreign key column has an index. Most databases don't index them automatically.
• Money columns are DECIMAL, never FLOAT. Phone numbers, PIN codes, GSTIN, PAN are VARCHAR with fixed lengths.
• created_at and updated_at TIMESTAMP columns are present on every business table for audit trails.
• Sensitive columns — Aadhaar, PAN, bank account — are either encrypted at the application layer or masked at the column level. RBI and SEBI guidelines treat these as personal data; storing them in plaintext is a real compliance risk.
• Character set is utf8mb4 on MySQL so emojis and Devanagari/Tamil text actually store correctly. The default utf8 in older MySQL versions silently truncates 4-byte characters.
• You've tested the schema against representative data — not three rows, but a few thousand — to spot performance issues before they hit production.

For Indian developers building anything from a SIP-tracking dashboard that mirrors the kind of returns the SIP calculator projects, to a full GST-compliant ERP for a Gujarat manufacturer, the schema is the foundation. Get it wrong and every layer above leaks. Get it right and the rest of the application practically writes itself.

Design your tables visually, pick your database flavour, and grab clean dialect-correct SQL — open the SQL Table Generator →