Alteryx function mapping

While importing Alteryx workflows to Prophecy pipelines, Prophecy converts Alteryx functions into equivalent SQL functions. The SQL dialect used in imported pipelines is determined by the target platform you select during import (Databricks, BigQuery, or Snowflake). The following tables list all the Alteryx functions that Prophecy supports and their corresponding SQL functions. If a table does not specify Databricks, BigQuery, or Snowflake, functionality is equivalent for all three dialects.

Conditional

When you import conditional Alteryx functions into Prophecy, the logic of your formulas stays the same, but the syntax changes, because SQL engines use case expressions instead of IF/IIF/Switch. Prophecy’s import makes these conversions for you. When reading or debugging generated SQL, you should recognize the case functions below as the SQL equivalent of Alteryx’s conditional functions.

Alteryx Function	Equivalent SQL Function	Notes
`IF c THEN t ELSE f ENDIF`	`case when then else end`	`case_when` is equivalent to `if`; `endif` is equivalent to `END`.
`IF c THEN t ELSEIF c2 THEN t2 ELSE f ENDIF`	`case when c then t when c2 then t2 else f end`	`case_when` is equivalent to `if`; `endif` is equivalent to `end`. In Alteryx, you chain conditions with `elseif`. in SQL, each `elseif` becomes another `when … then …`.
`IIF`	`case when c then t else f end`	In Alteryx, `iff` serves as a shorthand for `if ... else`. In SQL, `iff` is equivalent to a `case` with one condition.
`SWITCH`	`case when c then t when c2 then t2 ... end`	In Alteryx, `switch` tests one case against several possibilities. In SQL, this is expressed as a `case` with multiple `when … then …` clauses.

Conversions

Alteryx provides many specialized conversion functions. SQL engines rely on general-purpose casting, encoding and decoding functions, and numeric conversion functions.

BigQuery provides safer casting via safe_cast, which returns null instead of failing.
Snowflake provides error-handling conversion functions such as try_cast and try_to_number, which return null when conversion fails.
Databricks SQL supports conv() for base conversions, but BigQuery and Snowflake do not have the same general-purpose base conversion function.

Databricks SQL
BigQuery
Snowflake

Alteryx Function	Equivalent SQL Function	Notes
`BinToInt`	`conv(expr, from_base, to_base)`	Databricks uses `conv` for base conversions.
`CharFromInt`	`char()`	`char` is equivalent to `CharFromInt` in Alteryx.
`CharToInt`	`ascii()`	`ascii` is equivalent to `CharToInt` in Alteryx.
`ConvertFromCodePage`	`convertFromCodePage()`	Functions are equivalent; note slightly different case in SQL function name.
`HexToNumber`	`conv()`	Databricks uses `conv` for base conversions.
`IntToBin`	`bin()`	Databricks uses `bin` for binary conversions.
`IntToHex`	`conv()`	Databricks uses `conv` for base conversions.
`ToDegrees`	`degrees()`	Exact mapping.
`ToNumber`	`regexp_extract()`	In Databricks, `ToNumber` is approximated with `regexp_extract`. This extracts numeric substrings.
`ToRadians`	`radians()`	Exact mapping.
`ToString`	`cast(col as string)`	Converts to a string.

Alteryx Function	Equivalent SQL Function	Notes
`BinToInt`	No direct equivalent	BigQuery does not have a general base conversion function like `conv`. Requires custom logic or UDF.
`CharFromInt`	`chr()`	Function name differs from Databricks SQL.
`CharToInt`	`ascii()`	Same mapping as Databricks SQL.
`ConvertFromCodePage`	No direct equivalent	BigQuery does not expose code page conversion functions.
`HexToNumber`	No direct equivalent	Requires manual parsing or UDF in BigQuery.
`IntToBin`	No direct equivalent	BigQuery lacks built-in binary string conversion.
`IntToHex`	`to_hex()`	BigQuery supports hex conversion via `to_hex()`.
`ToDegrees`	`degrees()`	Exact mapping.
`ToNumber`	`safe_cast()` or `cast()`	`safe_cast` avoids runtime errors by returning `null` when conversion fails.
`ToRadians`	`radians()`	Exact mapping.
`ToString`	`cast(col as string)`	Converts to a string.

Alteryx Function	Equivalent SQL Function	Notes
`BinToInt`	No direct equivalent	Snowflake does not appear to provide a general-purpose `conv(expr, from_base, to_base)` equivalent. Requires custom logic or UDF.
`CharFromInt`	`chr()` or `char()`	Snowflake supports `chr`/`char` for returning a character from a Unicode code point.
`CharToInt`	`ascii()`	Returns the numeric value of the first character in a string.
`ConvertFromCodePage`	No direct equivalent	Confirm whether Import supports this conversion for Snowflake or requires external processing.
`HexToNumber`	No direct equivalent	Snowflake has hex encoding/decoding functions, but numeric base conversion may require custom logic or UDF.
`IntToBin`	No direct equivalent	Confirm whether Import generates custom logic for binary string conversion.
`IntToHex`	`hex_encode()`	Exact mapping.
`ToDegrees`	`degrees()`	Exact mapping.
`ToNumber`	`try_cast(... as float)`	Import uses `try_cast` so invalid numeric conversions return `null` instead of failing.
`ToString`	`format_number(...)` or `regexp_replace(...)`	Generated SQL varies depending on the formatting or cleanup behavior required by the original Alteryx expression.
`ToRadians`	`radians()`	Exact mapping.
`ToString`	`format_number(...)` or `regexp_replace(...)`	Generated SQL varies depending on the formatting or cleanup behavior required by the original Alteryx expression.

DateTime

Alteryx provides many specialized datetime functions. The SQL mapping depends on the target engine. Databricks SQL, BigQuery, and Snowflake all support broad datetime functionality, but they differ in function names, argument order, return types, format patterns, and time zone behavior.

Databricks SQL
BigQuery
Snowflake

Alteryx Function	Equivalent SQL Function	Notes
`DateTimeAdd`	`date_add()` or `ts + interval ...` or `add_months()`	`date_add` only adds days. For other units, use interval expressions or `add_months()` depending on the unit.
`DateTimeDay`	`day()`	Exact mapping.
`DateTimeDiff`	`datediff()`	Returns date differences. For finer units, use timestamp or interval expressions.
`DateTimeFirstOfMonth`	`date_trunc()`	Exact mapping.
`DateTimeFormat`	`date_format(ts, 'pattern')`	Formats date or timestamp values as strings.
`DateTimeHour`	`hour()`	Exact mapping.
`DateTimeLastOfMonth`	`last_day()`	Exact mapping.
`DateTimeMinutes`	`minute()`	Exact mapping.
`DateTimeMonth`	`month()`	Exact mapping.
`DateTimeNow`	`current_timestamp()`	Exact mapping.
`DateTimeNowPrecise`	`current_timestamp()`	Exact mapping.
`DateTimeParse`	`to_date()` or `to_timestamp()`	Use `to_timestamp()` when the parsed value needs to retain time information.
`DateTimeQuarter`	`quarter()`	Exact mapping.
`DateTimeSeconds`	`second()`	Exact mapping.
`DateTimeToday`	`current_date()`	Exact mapping.
`DateTimeToUTC`	`to_utc_timestamp(ts, tz)`	Converts a timestamp from the specified time zone to UTC.
`DateTimeTrim`	`date_trunc()`	Exact mapping.
`DateTimeYear`	`year()`	Exact mapping.
`ToDate`	`to_date()`	Converts to a date.
`ToDateTime`	`to_timestamp()`	Outputs a SQL timestamp.

Alteryx Function	Equivalent SQL Function	Notes
`DateTimeAdd`	`datetime_add(datetime_expression, interval int64_expression part)`	BigQuery supports `DATETIME_ADD` across units including `MICROSECOND` through `YEAR`.
`DateTimeDay`	`extract(day from datetime_expression)`	BigQuery uses `EXTRACT` for datetime parts.
`DateTimeDiff`	`datetime_diff(end_datetime, start_datetime, part)`	BigQuery counts part boundaries. week behavior can vary depending on `week`, `week(monday)`, or `isoweek`.
`DateTimeFirstOfMonth`	`datetime_trunc(datetime_value, month)`	Truncates the datetime value to the beginning of the month.
`DateTimeFormat`	`format_datetime(format_string, datetime_expr)`	BigQuery uses its own format element syntax.
`DateTimeHour`	`extract(hour from datetime_expression)`	BigQuery uses `extract`.
`DateTimeLastOfMonth`	`last_day(datetime[, month])`	`last_day` returns a `date`, not a `datetime`.
`DateTimeMinutes`	`extract(minute from datetime_expression)`	BigQuery uses `extract`.
`DateTimeMonth`	`extract(month from datetime_expression)`	BigQuery uses `extract`.
`DateTimeNow`	`current_datetime([time_zone])`	Use `current_datetime` when the result should stay a `datetime`.
`DateTimeNowPrecise`	`current_datetime([time_zone])`	BigQuery `datetime` includes fractional seconds, but is still not a timestamp.
`DateTimeParse`	`parse_datetime(format_string, datetime_string)`	Format string positions must match the input string.
`DateTimeQuarter`	`extract(quarter from datetime_expression)`	BigQuery uses `extract`.
`DateTimeSeconds`	`extract(second from datetime_expression)`	BigQuery uses `extract`.
`DateTimeToday`	`current_date()`	Exact mapping.
`DateTimeToUTC`		Confirm separately against BigQuery timestamp/time zone behavior.
`DateTimeTrim`	`datetime_trunc(datetime_value, granularity)`	Supports truncation to date and time granularities.
`DateTimeYear`	`extract(year from datetime_expression)`	BigQuery uses `extract`.
`ToDate`	`extract(date from datetime_expression)` or `date(datetime_expression)`	Converts or extracts the date portion.
`ToDateTime`	`parse_timestamp(format, timestamp_string)`	Use when parsing a string into a timestamp value.

Alteryx Function	Equivalent SQL Function	Notes
`DateTimeAdd`	`dateadd(part, value, date_or_time_expr)`	Snowflake uses `dateadd` with the date/time part first, followed by the amount and expression.
`DateTimeDay`	`day()` or `date_part('day', expr)`	Use `date_part` when you want a consistent extraction pattern across parts.
`DateTimeDiff`	`datediff(part, start_expr, end_expr)`	Snowflake returns the difference for the specified part by subtracting the second argument from the third.
`DateTimeFirstOfMonth`	`date_trunc('month', expr)`	Truncates to the first day of the month.
`DateTimeFormat`	`to_char(expr, format)`	Use `to_char` to format date, time, or timestamp values as strings.
`DateTimeHour`	`hour()` or `date_part('hour', expr)`	Exact extraction mapping.
`DateTimeLastOfMonth`	`last_day(expr, 'month')`	Returns the last day of the month.
`DateTimeMinutes`	`minute()` or `date_part('minute', expr)`	Exact extraction mapping.
`DateTimeMonth`	`month()` or `date_part('month', expr)`	Exact extraction mapping.
`DateTimeNow`	`current_timestamp()`	Returns the current timestamp.
`DateTimeNowPrecise`	`current_timestamp()`	Confirm whether imported Snowflake SQL needs a specific precision or timestamp variant.
`DateTimeParse`	`to_timestamp()`	Converts string input into a Snowflake timestamp value.
`DateTimeQuarter`	`quarter()` or `date_part('quarter', expr)`	Exact extraction mapping.
`DateTimeSeconds`	`second()` or `date_part('second', expr)`	Exact extraction mapping.
`DateTimeToday`	`current_date()`	Exact mapping.
`DateTimeToUTC`	`CONVERT_TIMEZONE('UTC', ts)`	Converts the timestamp value to UTC.
`DateTimeTrim`	`date_trunc(part, expr)`	Snowflake truncates `DATE`, `TIME`, or `TIMESTAMP` values to the specified precision.
`DateTimeYear`	`year()` or `date_part('year', expr)`	Exact extraction mapping.
`ToDate`	`to_date()`	Converts to a date. Time information is discarded when converting timestamp values to date.
`ToDateTime`	`to_timestamp()`	Outputs a Snowflake timestamp value.

Math

Alteryx offers a large catalog of dedicated math functions, many of which map directly to Databricks SQL. Most Alteryx functions have 1:1 equivalents, with a few cases (random integers, logs) requiring light SQL expressions to reproduce exact behavior.

Alteryx Function	Equivalent SQL Function	Notes
`ABS`	`abs()`	Exact mapping.
`ACOS`	`acos()`	Exact mapping.
`ASIN`	`asin()`	Exact mapping.
`ATAN`	`atan()`	Exact mapping.
`ATAN2`	`atan2()`	Exact mapping.
`Average`	`avg()`	`avg` ignores nulls. Use `AVG(col)` with `COALESCE(col, 0)` to treat nulls as 0.
`AverageNonNull`	`avg()`	`avg` ignores nulls by default.
`CEIL`	`ceil()`	Exact mapping.
`COS`	`cos()`	Exact mapping.
`COSH`	`cosh()`	Exact mapping.
`DISTANCE`	`haversine()`	Exact mapping.
`EXP`	`exp()`	Exact mapping.
`FACTORIAL`	`factorial()`	Exact mapping.
`FLOOR`	`floor()`	Exact mapping.
`LOG`	`ln()`	Alteryx `LOG()` returns the natural log (`ln`). In SQL, `ln(x)` is the natural log and `log10(x)` is base-10. Databricks SQL also supports arbitrary bases via `log(base, x)`.
`LOG10`	`log10()`	Exact mapping..
`Median`	`median`	Exact mapping.
`Mod`	`mod()`	Exact mapping.
`PI`	`pi()`	Exact mapping.
`POW`	`pow()`	Exact mapping.
`RAND`	`rand()`	Exact mapping.
`RandInt`	`rand()`	Alteryx `RandInt` returns an integer in an inclusive range. Databricks `rand()` returns a `DOUBLE` drawn uniformly from the interval `[0, 1)`, meaning it can return 0 but will never return 1. To mimic `RandInt(low, high)` exactly, use `cast(floor(rand() * (high - low + 1)) + low as int)`.
`Round`	`round()`	Exact mapping.
`SIN`	`sin()`	Exact mapping.
`SINH`	`sinh()`	Exact mapping.
`SQRT`	`sqrt()`	Exact mapping.
`TAN`	`tan()`	Exact mapping.
`TANH`	`tanh()`	Exact mapping.

Bitwise

Alteryx uses named bitwise functions (such as BinaryAnd and BinaryOr), while SQL relies primarily on native operators (&, |, <<, >>) and a small set of helper functions. The mappings are direct, with no semantic differences.

Alteryx Function	Equivalent SQL Function	Notes
`BinaryAnd`	`\&`	No `Binary` keyword necessary in SQL.
`BinaryNot`	`bitwise_not`	No `Binary` keyword necessary in SQL.
`BinaryOr`	`\|`	No `Binary` keyword necessary in SQL.
`BinaryXOR`	`bitwiseXOR`	No `Binary` keyword necessary in SQL.
`ShiftLeft`	`<<`	Exact mapping.
`ShiftRight`	`>>`	Exact mapping.

Range and bound functions

Range and bound functions behave similarly across SQL engines, but differences arise in how arrays, ordering, and positional logic are handled.

between works directly; syntax just shifts to SQL form.
Bound requires an explicit case statement.
Max and Min are direct equivalents.
MaxIDX and MinIDX in Alteryx identify the position of a value, but SQL engines typically use sorting or window functions (row_number() over (...)) to achieve the same result.

Databricks SQL
BigQuery
Snowflake

Alteryx Function	Equivalent SQL Function	Notes
`Between`	`between()`	In Alteryx, `Between([x], 10, 20)`; in SQL, `x between 10 and 20`.
`Bound`	`case when x < min then min when x > max then max else x end`	Databricks SQL has no single `bound` function; use a `case` expression to clamp the value inside a range.
`Max`	`max()`	Exact mapping. Nulls are ignored by default.
`MaxIDX`	`array_max()`	Applies to arrays rather than datasets. To find the row containing the max value, use `order by ... desc limit 1` or a window function.
`Min`	`min()`	Exact mapping. Nulls are ignored by default.
`MinIDX`	`array_min()`	Like `array_max()`, this applies to arrays. To find the row with the minimum value, use `order by ... asc limit 1`.

Alteryx Function	Equivalent SQL Function	Notes
`Between`	`between`	In Alteryx, `Between([x], 10, 20)`; in SQL, `x between 10 and 20`.
`Bound`	`case when x < min then min when x > max then max else x end`	BigQuery has no single `bound` function; use a `case` expression to clamp the value inside a range.
`Max`	`max()`	Exact mapping. BigQuery ignores nulls by default.
`MaxIDX`	`array_max()`	Applies to arrays rather than datasets. To find the row containing the max value, use `order by ... desc limit 1` or a window function.
`Min`	`min()`	Exact mapping. BigQuery ignores nulls by default.
`MinIDX`	`array_min()`	Like `MaxIDX`, this applies to arrays rather than datasets. To find the row with the minimum value, use `order by ... asc limit 1` or a window function.

Alteryx Function	Equivalent SQL Function	Notes
`Between`	`between`	In Alteryx, `Between([x], 10, 20)`; in SQL, `x between 10 and 20`.
`Bound`	`case when x < min then min when x > max then max else x end`	Snowflake has no built-in `bound` function; use a `case` expression to clamp values to a range.
`Max`	`max()`	Exact mapping. Snowflake ignores nulls by default.
`MaxIDX`	`array_position(array(a), array_max(array(a))) - 1`	Snowflake preserves Alteryx zero-based positional behavior by subtracting 1 from the returned position.
`Min`	`min()`	Exact mapping. Snowflake ignores nulls by default.
`MinIDX`	`array_position(array(a), array_min(array(a))) - 1`	Snowflake preserves Alteryx zero-based positional behavior by subtracting 1 from the returned position.

Operators

Alteryx Function	Equivalent SQL Function	Notes
Block comment	`/` `/`	Exact mapping.
Single-line comment	`--`	`//` does not work in SQL.
Boolean `AND` or `\&\&`	`and`	`\&\&` is not supported in SQL. Use `AND`.
Boolean `NOT` or `!`	`!`	`!` is not supported in SQL. Use `NOT`.
Boolean `OR` or `\|\|`	`OR`	`\|\|` is not supported in SQL. Use `OR`.
Equality `==`, `=`	`=`	In SQL, equality is expressed as `=`, not `==`.
Non-equality `!=`	`!=`	Exact mapping.
Greater than, greater than or equal to, less than, less than or equal to	`>`, `>=`, `<`, `<=`	Exact mapping.
Addition, Subtraction, Multiplication, Division	`+`, `-`, `*`, `/`	Exact mapping.
Parentheses	`(` `)`	Exact mapping.
`value IN`	`in`	Exact mapping.
`value NOT IN`	`not in`	Exact mapping.

Specialized

Specialized functions have similar conceptual mappings across SQL engines, but differences arise in indexing, null handling, and how arrays are processed. These differences are especially important when translating positional logic from Alteryx.

Indexing: Alteryx uses zero-based indices. BigQuery and Snowflake arrays are 0-based, while Databricks SQL arrays are 1-based.
Null handling: NULL() is a callable function in Alteryx, but a literal in SQL.
TOPNIDX: In Alteryx it finds the position of the top N value; SQL engines typically use sorting and positional access to replicate this behavior.

Databricks SQL
BigQuery
Snowflake

Alteryx Function	Equivalent SQL Function	Notes
`Coalesce`	`coalesce()`	Alteryx evaluates left-to-right; Databricks SQL follows standard SQL semantics.
`GetVal`	`element_at()`	In Alteryx, `GetVal(list, index)` retrieves the element at a 0-based index. Databricks SQL arrays are 1-based, so `GetVal([x], 0)` corresponds to `element_at(x, 1)`.
`NULL`	`coalesce()` or `NULL` literal	`NULL` is a literal in SQL, not a function. Use `coalesce()` to replace nulls.
`TOPNIDX`	`array_sort()`, `element_at()`, and `array_position()`	No direct equivalent. Use array sorting plus positional lookup to approximate the behavior. For row-level ranking, use `order by` or window functions.
`UrlEncode`	`url_encode()`	Exact mapping.

Alteryx Function	Equivalent SQL Function	Notes
`Coalesce`	`coalesce()`	Exact mapping.
`GetVal`	`array[offset(index)]`	BigQuery arrays are 0-based using `offset`. `GetVal(x, 0)` becomes `x[offset(0)]`.
`NULL`	`coalesce()` or `null` literal	`null` is a literal in BigQuery SQL. Use `coalesce()` to replace nulls.
`TOPNIDX`	`array_sort()`, `array_reverse()`, `offset`, and `array_position()`	No direct equivalent. In BigQuery, you typically sort the array, optionally reverse it for descending order, then access the value with `offset(n-1)`.
`UrlEncode`	No direct equivalent	BigQuery does not have a built-in `url_encode` function; requires UDF or external processing.

Alteryx Function	Equivalent SQL Function	Notes
`Coalesce`	`coalesce()`	Exact mapping.
`GetVal`	`array(...)[i-1]`	Snowflake array access is adjusted to preserve Alteryx zero-based indexing behavior.
`NULL`	`coalesce()` or `null` literal	`null` is a literal in Snowflake SQL. Use `coalesce()` to replace nulls.
`TOPNIDX`	`array_position(array(a), reverse(array_sort(array(a)))[i+1]) - 1`	Snowflake preserves Alteryx positional semantics by sorting the array, selecting the indexed value, and subtracting 1 from the resulting position.
`UrlEncode`	`url_encode()`	Exact mapping.

String

Key differences across SQL engines:

Indexing: Databricks SQL arrays are 1-based, while BigQuery and Snowflake arrays are 0-based. Alteryx uses 0-based indexing.
Case sensitivity: All three engines are case-sensitive by default unless you normalize with functions such as lower() or upper().
Regex behavior:
- Databricks: Java regex
- BigQuery: RE2
- Snowflake: POSIX-style regular expressions
Function coverage: Some Alteryx string functions have no direct equivalent and require workarounds or UDFs.
Substring & trimming: Similar core functions exist across engines, but syntax and edge-case behavior may differ slightly.

Databricks SQL
BigQuery
Snowflake

Alteryx	Equivalent SQL Function	Notes
`Contains`	`contains()`	Databricks SQL is case-sensitive by default unless you apply `lower()` or `upper()`.
`CountWords`	`size(split(col, ' '))`	Split the string into tokens and count the resulting array elements.
`DecomposeUnicodeForMatch`	No native equivalent	Requires external processing or Python UDF.
`EndsWith`	`col like '%pattern'`	SQL uses `LIKE`; remember to escape special characters and use `%` wildcards.
`FindNth`	No native equivalent	Requires workaround logic.
`FindString`	`locate(substring, col)`	Returns the 1-based position of the substring.
`GetLeft` / `Left`	`substring(col, 1, n)`	SQL substring positions start at 1.
`GetPart`	`element_at(split(col, ' '), n)`	Databricks SQL arrays are 1-based. `GetPart(col, n)` → `element_at(split(col, ' '), n+1)` if the Alteryx index is 0-based.
`GetRight` / `Right`	`substring(col, length(col)-n+1, n)`	SQL lacks a built-in `right()` equivalent in some contexts; emulate with `substring()`.
`GetWord`	`element_at(split(col, ' '), n)`	Uses array indexing after `split()`.
`Length`	`length()`	Exact mapping.
`LowerCase`	`lower()`	Exact mapping.
`MD5_ASCII`, `MD5_UNICODE`, `MD5_UTF8`	`md5()`	SQL does not distinguish between encoding-specific MD5 variants.
`PadLeft`	`lpad(col, n, 'char')`	Exact mapping.
`PadRight`	`rpad(col, n, 'char')`	Exact mapping.
`REGEX_CountMatches`	`size(split(col, regex)) - 1`	No built-in counter; emulate with `split()` and `size()`.
`REGEX_Match`	`regexp_extract(col, regex, 0)`	Returns the substring matching the regex pattern.
`REGEX_Replace`, `Replace`, `ReplaceChar`, `ReplaceFirst`	`regexp_replace(col, pattern, replacement)`	Replaces all matches by default.
`ReverseString`	`reverse()`	Exact mapping.
`StartsWith`	`col LIKE 'pattern%'`	Case-sensitive by default.
`STRCSPN`	No native equivalent	Requires custom UDF or workaround logic.
`StripQuotes`	`regexp_replace(col, '["'']', '')`	Removes quotes using regex replacement.
`STRSPN`	No native equivalent	Requires custom logic.
`Substring`	`substring(col, start, length)`	Positions are 1-based.
`TitleCase`	`initcap()`	Exact mapping.
`Trim`	`trim(col)`	Exact mapping.
`TrimLeft`	`ltrim()`	Exact mapping.
`TrimRight`	`rtrim()`	Exact mapping.
`Uppercase`	`upper()`	Exact mapping.
`UuidCreate`	`uuid()`	Exact mapping.

Alteryx	Equivalent SQL Function	Notes
`Contains`	`strpos(col, substring) > 0`	BigQuery does not provide a `contains()` function.
`CountWords`	`array_length(split(col, ' '))`	Split the string into an array, then count elements.
`DecomposeUnicodeForMatch`	No native equivalent	Requires external processing or UDF.
`EndsWith`	`col like '%pattern'`	Uses standard SQL wildcard syntax.
`FindNth`	No native equivalent	Requires workaround logic.
`FindString`	`strpos(col, substring)`	Returns a 1-based position; returns 0 if not found.
`GetLeft` / `Left`	`substr(col, 1, n)`	Function name differs from Databricks SQL.
`GetPart`	`split(col, ' ')[offset(n-1)]`	BigQuery arrays are 0-based using `offset`.
`GetRight` / `Right`	`substring(col, length(col)-n+1, n)`	Uses substring arithmetic to emulate right-side extraction.
`GetWord`	`split(col, ' ')[offset(n-1)]`	Uses array indexing after `split()`.
`Length`	`length()`	Exact mapping.
`LowerCase`	`lower()`	Exact mapping.
`MD5_ASCII`, `MD5_UNICODE`, `MD5_UTF8`	`md5()`	Uses a single MD5 function for all variants.
`PadLeft`	`lpad()`	Exact mapping.
`PadRight`	`rpad()`	Exact mapping.
`REGEX_CountMatches`	`array_length(regexp_extract_all(col, regex))`	BigQuery supports `regexp_extract_all`.
`REGEX_Match`	`regexp_extract(col, regex)`	Uses RE2 regex syntax.
`REGEX_Replace`, `Replace`, `ReplaceChar`, `ReplaceFirst`	`REGEXP_REPLACE(col, pattern, replacement)`	Uses RE2 regex syntax.
`ReverseString`	`reverse()`	Exact mapping.
`StartsWith`	`col LIKE 'pattern%'`	Case-sensitive by default.
`STRCSPN`	No native equivalent	Requires custom logic or UDF.
`StripQuotes`	`regexp_replace(col, '["'']', '')`	Removes quotes using regex replacement.
`STRSPN`	No native equivalent	Requires custom logic.
`Substring`	`substr(col, start, length)`	Uses `substr` rather than `substring`.
`TitleCase`	`initcap()`	Exact mapping.
`Trim`	`trim()`	Exact mapping.
`TrimLeft`	`ltrim()`	Exact mapping.
`TrimRight`	`rtrim()`	Exact mapping.
`Uppercase`	`upper()`	Exact mapping.
`UuidCreate`	`generate_uuid()`	Generates a UUID string.

Alteryx	Equivalent SQL Function	Notes
`Contains`	`contains(col, substring)`	Exact mapping.
`CountWords`	`array_size(split(col, ' '))`	Split the string into an array, then count elements.
`DecomposeUnicodeForMatch`	No native equivalent	Requires external processing or UDF.
`EndsWith`	`col LIKE '%pattern'`	Uses standard SQL wildcard syntax.
`FindNth`	No native equivalent	Requires workaround logic.
`FindString`	`charindex(needle, haystack) - 1`	Snowflake indexing is adjusted to preserve Alteryx zero-based behavior.
`GetLeft` / `Left`	`substr(col, 1, n)`	Snowflake uses `substr`/`substring`.
`GetPart`	`split_part(s, delim, n)`	Snowflake provides a dedicated split-and-access function.
`GetRight` / `Right`	`substr(col, length(col)-n+1, n)`	Uses substring arithmetic to emulate right-side extraction.
`GetWord`	`split(col, ' ')[n]`	Snowflake arrays are 0-based. Confirm generated syntax for array access.
`Length`	`length()`	Exact mapping.
`LowerCase`	`lower()`	Exact mapping.
`MD5_ASCII`, `MD5_UNICODE`, `MD5_UTF8`	`md5()`	Snowflake uses a single MD5 function for all variants.
`PadLeft`	`lpad()`	Exact mapping.
`PadRight`	`rpad()`	Exact mapping.
`REGEX_CountMatches`	`regexp_count(col, regex)`	Snowflake provides a native regex count function.
`REGEX_Match`	`regexp_like()` or `regexp_substr()`	`regexp_like` is used for boolean matching; `regexp_substr` is used when extracting matched text.
`REGEX_Replace`, `Replace`, `ReplaceChar`, `ReplaceFirst`	`regexp_replace(col, pattern, replacement)`	Snowflake regex behavior differs from both Java regex and RE2.
`ReverseString`	`reverse()`	Exact mapping.
`StartsWith`	`col LIKE 'pattern%'`	Case-sensitive by default.
`STRCSPN`	No native equivalent	Requires custom logic or UDF.
`StripQuotes`	`regexp_replace(col, '["'']', '')`	Removes quotes using regex replacement.
`STRSPN`	No native equivalent	Requires custom logic.
`Substring`	`substr(col, start, length)`	Snowflake supports both `substr` and `substring`.
`TitleCase`	`initcap()`	Exact mapping.
`Trim`	`trim()`	Exact mapping.
`TrimLeft`	`ltrim()`	Exact mapping.
`TrimRight`	`rtrim()`	Exact mapping.
`Uppercase`	`upper()`	Exact mapping.
`UuidCreate`	`UUID_STRING()`	Generates a UUID string.

Overview

Alteryx

Informatica

Ab Initio

DataStage

Conditional

Conversions

DateTime

Math

Bitwise

Range and bound functions

Operators

Specialized

String

​Conditional

​Conversions

​DateTime

​Math

​Bitwise

​Range and bound functions

​Operators

​Specialized

​String

Conditional

Conversions

DateTime

Math

Bitwise

Range and bound functions

Operators

Specialized

String