SQL converter
Rewrite quoting, escaping, row limits and function names between 16 dialects, with a list of what it refused to guess at.
Amazon Athena / Trino query
Google BigQuery query
What it changed
'', backslash is literal\', backslash escapes1 placecurrent timestampCURRENT_TIMESTAMP1 placeVARCHARSTRING1 place
What you still need to do
1 thing this tool will not guess at
Array subscript
Amazon Athena / Trino counts array positions from 1, Google BigQuery from 0: the same arr[1] is the first element in one and the second in the other, with no error. In BigQuery, write arr[ORDINAL(n)] to count from 1.
Anything left in this list was recognised and left as it was on purpose. A function renamed to one that takes its arguments in a different order produces SQL that runs and returns the wrong rows, which is worse than SQL that fails.
Converting Amazon Athena / Trino to Google BigQuery: Key Syntax Differences
Translating SQL queries between Amazon Athena / Trino and Google BigQuery involves subtle dialect differences that standard text find-and-replace often misses. Everything below is processed entirely in your browser using SQLParity.
Identifier Quoting
Amazon Athena / Trino: "name"
Google BigQuery: `name`
String Literal Escaping
Amazon Athena / Trino: Quotes are doubled (''), backslash is literal.
Google BigQuery: Quotes are escaped with backslash (\'), backslash is an escape character.
Null-Safe Equality
Google BigQuery: colA IS DISTINCT FROM colB
Why In-Browser SQL Conversion Matters
Database migrations frequently involve proprietary business logic, sensitive table schemas, and production column names. When converting SQL between Amazon Athena / Trino and Google BigQuery, SQLParity executes all parsing, tokenization, and code rewriting locally in your browser. Zero queries or schemas are transmitted to remote servers.