How to NORMALIZE in Snowflake

How do I normalize Unicode data in Snowflake?

NORMALIZE converts a Unicode string to a chosen normalization form (NFC, NFD, NFKC, or NFKD) so that visually identical characters are stored with identical byte sequences.

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Description

What does the NORMALIZE function do?

NORMALIZE standardizes Unicode strings so that characters with multiple binary representations (such as accented letters) become identical. This prevents hidden duplicates and makes JOINs, GROUP BYs, and text comparisons reliable.

Why normalize ecommerce data?

Customer names, addresses, or product titles pasted from different sources often carry mixed Unicode forms. Applying NORMALIZE before inserts or during ETL guarantees deduplication and consistent search behaviour.

How do I call NORMALIZE?

Use NORMALIZE(string_expr, form).Omitting form defaults to NFC, the most common choice for storage and comparisons.

Which normalization form should I choose?

NFC is safest for most applications. Pick NFKC or NFKD only when you need compatibility folding (e.g., converting superscript ² to regular 2). NFD is useful for stripping accents when combined with TRANSLATE.

Can I normalize an entire table column?

Yes.Update the column in-place or create a computed column that stores the normalized version to avoid repetitive calls in queries.

Example: bulk update

UPDATE Customers SET name = NORMALIZE(name, 'NFC');

Best practices for production

1. Normalize during data ingestion inside stages or COPY INTO statements. 2. Index or cluster on normalized columns instead of raw text. 3. Always normalize both sides of a comparison to avoid mismatches.

Common pitfalls

• Mis-matching forms across tables causes silent join failures. • Forgetting to cast non-VARCHAR types to VARCHAR before normalizing raises an error.

Why How to NORMALIZE in Snowflake is important

How to NORMALIZE in Snowflake Example Usage


-- Deduplicate customer names before joining
WITH cleaned AS (
  SELECT id,
         NORMALIZE(name) AS name,
         email,
         created_at
  FROM Customers
)
SELECT c.id, c.name, COUNT(o.id) AS orders
FROM cleaned c
LEFT JOIN Orders o ON o.customer_id = c.id
GROUP BY c.id, c.name;

How to NORMALIZE in Snowflake Syntax


NORMALIZE(
    <string_expr> [ , 'NFC' | 'NFD' | 'NFKC' | 'NFKD' ]
)

-- Example in ecommerce context
SELECT NORMALIZE(name, 'NFKC') AS normalized_name
FROM Products;

-- Bulk update before joining Orders and Customers
UPDATE Customers
SET    email = NORMALIZE(email);

Common Mistakes

Using NFD when NFC is needed: NFD keeps decomposed characters, so “é” and “é” still compare as different. Fix by switching to NFC or applying NFC on both sides of the comparison.
Normalizing only one table in a JOIN: When Orders.email is NFC but Customers.email is raw, the JOIN misses matches. Normalize both columns or create computed normalized columns in every table.