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PySparkSnowflakeDatabricks

Snowflake Data Types Explained with Real Use Cases

A simple, story-style guide to understanding data types the way humans think — not machines.

✨ A Simple Story — “Why the Right Data Type Matters”​

Imagine you're building a new system for a company that sells coffee online.

You receive customer names, order amounts, locations, timestamps, and even JSON payloads from mobile apps.

If you use the wrong data types, you’ll run into problems like:

  • Numbers stored as text (breaking analytics)
  • Dates stored incorrectly (breaking reports)
  • JSON stored as VARCHAR (becomes unusable)
  • Precision loss in currency values
  • Slow queries due to inefficient data structure

Choosing the correct Snowflake data type is like choosing the right container in a kitchen:

  • Use a bottle for liquids
  • Use a box for grains
  • Use a jar for sauces

Snowflake gives you the right “containers” — you just need to know when to use them.

🧩 Snowflake Data Types (Explained with Real Use Cases)​

Below is a simple, human-friendly explanation of Snowflake’s main data types and when to use each.

🔤 1. VARCHAR — Text & Strings​

Best for:
Names, descriptions, addresses, comments, product codes.

Example:

customer_name VARCHAR(200)

Real-world use case: Customer full names from signup forms.

Why it's useful: Flexible size, stores any character, Snowflake automatically handles UTF-8.

🔢 2. NUMBER — Precise Numbers (Integer or Decimal)​

Snowflake’s NUMBER type is extremely powerful.

Two variations:​

  • INTEGER: whole numbers
  • DECIMAL / NUMERIC: numbers with decimals

Example:

order_amount NUMBER(10,2)

Real-world use case: Storing money — where precision is critical.

Why it's useful: No rounding issues. Perfect for finance, pricing, taxes, billing.

🌊 3. FLOAT — Approximate Numbers​

Best for: Sensor readings, scientific computations, machine learning inputs.

Example:

temperature_reading FLOAT

Use case: IoT data where values can be approximate.

Important note: Never use FLOAT for money — precision loss is inevitable.

🎚 4. BOOLEAN — True / False​

Best for:

  • Flags
  • Status fields
  • Feature toggles
  • “Has user paid?” checks

Example:

is_active BOOLEAN

Use case: Marking if an account is active or not.

📅 5. DATE — Calendar Date Only​

Stores only the date (no time).

Example:

signup_date DATE

Use case: Birthdates, signup dates, scheduled events.

🕒 6. TIMESTAMP — Date + Time​

Snowflake supports multiple timestamp variations:

  • TIMESTAMP_NTZ (no timezone) → most common
  • TIMESTAMP_LTZ (local timezone)
  • TIMESTAMP_TZ (with timezone data)

Example:

order_timestamp TIMESTAMP_NTZ

Use case: Tracking order creation time across global regions.

📦 7. VARIANT — Snowflake’s Magic Box (Semi-Structured Data)​

This is Snowflake’s superpower.

VARIANT can store:

  • JSON
  • XML
  • AVRO
  • Parquet
  • Complex objects

Example:

event_data VARIANT

Use case: Storing website clickstream events or mobile payloads exactly as they come in.

Why it matters: You don’t need a strict schema upfront. Snowflake lets you query JSON with dot notation:

SELECT event_data:user_id FROM events;

🧺 8. ARRAY — A List of Items​

Best for:

  • Lists of tags
  • Lists of products in an order
  • Lists of items from an API

Example:

tags ARRAY

Use case: Storing multiple categories assigned to a single product.

🧱 9. OBJECT — Key-Value Structure​

Similar to JSON objects.

Example:

attributes OBJECT

Use case: Storing product metadata or dynamic properties.

🧠 How to Choose the Right Data Type (Simple Rules)​

✔ Use NUMBER for money​

Never use FLOAT for financial values.

✔ Use TIMESTAMP_NTZ unless you specifically need timezone​

Most companies don’t.

✔ Use VARIANT when schema is flexible or unknown​

Perfect for logs, third-party API data, clickstreams.

✔ Use VARCHAR only when it is truly text​

Don’t store numbers or dates as VARCHAR — ever.

✔ Use BOOLEAN instead of “Y/N”​

Cleaner and faster to query.

🧪 Small Real-World Example Table​

CREATE OR REPLACE TABLE orders (
  order_id NUMBER(20,0),
  customer_id NUMBER,
  order_timestamp TIMESTAMP_NTZ,
  amount NUMBER(10,2),
  products ARRAY,
  metadata VARIANT
);

This table handles:

  • IDs
  • Money
  • Time
  • Lists
  • JSON

All efficiently and professionally.

🧘 The Secret Behind Snowflake’s Data Types​

Snowflake stores everything in micro-partitions and automatically optimizes:

  • compression
  • type handling
  • indexing
  • pruning

Choosing the right data type makes Snowflake smarter about:

  • query speed
  • cost
  • storage efficiency
  • time travel
  • clustering

Good data types = good performance.

📘 Summary​

  • Snowflake offers flexible data types designed for structured and semi-structured data.
  • NUMBER is best for money and precision.
  • FLOAT is best for scientific or approximate data.
  • TIMESTAMP_NTZ is the standard for event logs.
  • VARIANT, ARRAY, and OBJECT handle JSON and modern app data beautifully.
  • Choosing the correct type improves performance, reduces cost, and keeps analytics clean.

Understanding Snowflake data types helps you build professional, future-proof pipelines.

👉 Next Topic

Clustering Keys — Why, When, How & Real Company Examples