# Moving Beyond Alteryx With a Data Type Mapping Guide for Migration

Migrating from Alteryx to Databricks or Snowflake? Get the complete data type mapping guide, edge cases and validation approach to avoid silent data corruption.

**TL;DR**

- **No clean type equivalents:** Alteryx has 17 data types, and several — including FixedDecimal, DateTime, and Time — have no direct equivalent in cloud data platforms like Databricks, Snowflake, or BigQuery.
- **Silent data corruption risk:** Wrong type mappings lead to silent precision loss, corrupted timestamps, and phantom NULLs in production data.
- **Hidden edge cases:** Differences in how NULLs behave, how text is encoded, and how decimals are handled won't surface until after go-live without explicit testing.
- **Three-phase validation required:** A validation approach covering before, during, and after migration is the only reliable way to catch behavioral differences between Alteryx and cloud data platforms.
- **Automated migration with Prophecy:** Prophecy's agentic, AI-accelerated data prep automates migration from Alteryx .yxmd files to cloud-native code in Databricks, Snowflake, or BigQuery, while visually surfacing type-mapping decisions for human review.

## **Why data type mapping is where migrations succeed or fail**

Data type mismatches between Alteryx and cloud data platforms cause the most damaging migration failures because they silently corrupt data. Alteryx handles types differently from cloud platforms in three important ways:

1. **Dates and times are stored as text.** Alteryx stores temporal types internally as formatted strings rather than true date or time objects.
2. **Decimal precision goes beyond what cloud platforms support.** Alteryx's FixedDecimal type supports up to 50 digits of precision — well beyond the 38-digit cap on cloud platforms.
3. **NULLs behave differently.** Alteryx treats NULL as an empty string in many operations rather than propagating it through calculations.

## **Alteryx to Databricks: the complete type mapping**

**Most types map directly (low risk):**

| Alteryx Type | Databricks (Spark) Type | Notes |
| --- | --- | --- |
| String, WString, V_String, V_WString | StringType | Spark handles Unicode natively; variable-length by default |
| Int16 | ShortType | Direct match |
| Int32 | IntegerType | Direct match |
| Int64 | LongType | Direct match |
| Float | FloatType | Direct match |
| Double | DoubleType | Direct match |
| Boolean | BooleanType | Direct match |

**These mappings are where data quality issues are most likely to appear after migration:**

- **FixedDecimal(p,s) → DecimalType(p,s):** Spark caps at 38 digits. Fields configured above 38 will fail or silently round off, potentially affecting financial calculations.
- **DateTime → TimestampNTZType:** Alteryx DateTime stores no timezone info, so TimestampNTZType is the safer default.
- **SpatialObj → GeographyType or GeometryType:** Requires validation that spatial formats align between source and target.

## **Alteryx to Snowflake: the complete type mapping**

**Many types map directly to Snowflake:**

| Alteryx Type | Snowflake Type |
| --- | --- |
| All string types | VARCHAR |
| Byte | NUMBER(3,0) |
| Int16 | SMALLINT |
| Int32 | INTEGER |
| Int64 | BIGINT |
| Float | FLOAT |
| Double | DOUBLE |
| Bool | BOOLEAN |

**These mappings require explicit configuration and are the most common sources of post-migration data issues:**

- **FixedDecimal(p,s) → NUMBER(p,s):** Snowflake defaults to NUMBER(38,0) — which means zero decimal places. Without explicitly setting the decimal scale, every decimal value gets rounded to a whole number.
- **DateTime → TIMESTAMP_NTZ (recommended as default):** Choosing incorrectly affects timezone-sensitive queries downstream.

### **The timestamp decision deserves its own moment**

Choosing the right timestamp variant is crucial:
- **TIMESTAMP_NTZ:** Best default for Alteryx DateTime fields since they carry no timezone info.
- **TIMESTAMP_LTZ:** Stores UTC internally and converts to the session timezone on read.
- **TIMESTAMP_TZ:** Stores the explicit timezone offset with the value.

## **Edge cases that silently corrupt data**

These issues won't show up in a type mapping table. They show up three weeks after go-live when a financial report doesn't reconcile:

- **The NULL behavior divide:** Alteryx treats NULL as an empty string in many operations, while cloud data platforms follow SQL behavior.
- **Text encoding expansion:** Some Alteryx encodings use one byte per character, but UTF-8 can use multiple bytes for extended characters.

## **A validation approach that actually catches these problems**

The only reliable way to catch type mapping issues is a structured validation plan covering every stage of migration. A practical plan works in three phases.

**Before migration:**
- **Field profiling:** Document each field's Alteryx type, target type, transformation rule, and known edge cases.

**During migration:**
- **Record count matching:** Match record counts after extraction to catch dropped rows early.

**After migration:**
- **Parallel testing:** Execute both the Alteryx data workflow and cloud-native data workflow against identical input data.

## **How Prophecy accelerates Alteryx migration without skipping validation**

Automated migration tooling compresses timelines but also requires validation to ensure accuracy. Migration can show momentum: workflows migrated, ETL pipelines modernized, and Prophecy's agents enable analysts to prepare data for analysis and build data workflows independently.

### **Your platform team stays in control**

Unlike legacy tools where you're locked into their governance model, Prophecy runs on your cloud data platform. Data engineering teams retain full ownership, and governance assets live in your stack.

### **Make your analysts the heroes**

With Prophecy's agentic, AI-accelerated data prep, analytics teams can build on the governed data. Analysts can build and run governed data workflows independently.

### **The engineering cost problem migration solves**

Analytics-related data workflow requests consume substantial engineering time. Migration to a platform where analytics teams can function independently with AI agents changes the equation entirely.

### **The real-world numbers**

A Fortune 50 healthcare company migrated 80+ Alteryx workflows in 10 weeks using Migration Copilot.

## **FAQ**

### **Can Prophecy migrate Alteryx workflows automatically?**

Yes. Prophecy's Migration Copilot imports Alteryx .yxmd files directly and transpiles them into cloud-native code.

### **What happens to FixedDecimal fields with more than 38 digits of precision?**

Cloud data platforms cap decimal precision at 38 digits, and fields above 38 will fail or silently truncate.

### **How do analysts use Prophecy after migration is complete?**

Analysts utilize Prophecy's AI agents to build their own data workflows independently.