Data Utilities

Data Type Cross-Platform Reference

Enter a data type from one platform to see its equivalent on others. Covers numeric precision, timestamp variants, JSON/semi-structured types, and array types.

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Data Type Cross-Platform Reference

The Data Type Cross-Platform Reference maps data types across PostgreSQL, BigQuery, Snowflake, Redshift, MySQL, and Spark SQL, covering numeric, timestamp, JSON, and array types.

โ€ข Find the BigQuery equivalent of PostgreSQL JSONB before writing a migration script

โ€ข Check Snowflake TIMESTAMP_NTZ vs TIMESTAMP_TZ behavior when migrating from Redshift

โ€ข Look up the Spark SQL equivalent of a PostgreSQL NUMERIC(p,s) type for a lake migration

โ€ข Understand array type differences before writing a cross-platform dbt model

JSONPath Tester โ€” Test JSONPath expressions against a JSON document with real-time results.
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What does this tool tell you?
The Data Type Cross-Platform Reference maps data types across PostgreSQL, BigQuery, Snowflake, Redshift, MySQL, and Spark SQL, covering numeric, timestamp, JSON, and array types.
What affects the result most?
Cross-platform type mapping: PostgreSQL VARCHAR(n) โ†” BigQuery STRING โ†” Snowflake VARCHAR โ†” Redshift VARCHAR. Numeric type precision: NUMERIC(p,s) precision and scale across all four platforms. Timestamp types: TIMESTAMP vs TIMESTAMPTZ vs TIMESTAMP_NTZ differences explained per platform.
How should I use the result?
Use this tool to orient quickly to the concepts, field names, or values you are about to look up in a full specification or vendor documentation. It summarizes the common cases; the authoritative source remains whichever standard or vendor doc defines the values themselves.
Data pipeline health and freshness visibility. Better Stack Logtail for data engineering teams monitoring pipeline SLOs and warehouse query health.
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