This article explores key concepts to convert Alteryx to SQL with Snowflake, whereby staging, transformation, and output delivering is facilitated by Snowflake and the host platform as a service (Azure, AWS, Google Cloud Platform). At DataTools Pro, we specialize in Azure as our preferred cloud platform.
Why Convert Alteryx to Snowflake?
Organizations are increasingly moving from Alteryx to modern data platforms to centralize data transformation, improve scalability, and reduce costs associated with self-service data preparation tools. By leveraging Snowflake’s cloud-native architecture, teams can streamline ETL workflows, enhance governance, and enable real-time analytics without the limitations of local processing or expensive enterprise servers to manage.
Alteryx is an amazing product, as it has helped us out of tough data wrangling situations many times. Modern data platforms like Snowflake and Databricks have commoditized data transformation for analytics and some AI use cases. This shift is part of a modern data strategy where all processing, transformation, and labeling of data can be centralize or remain federated to business and analytics teams.
Alteryx cloud (while late to market), offers a possibility to extend the viability of Alteryx. The reality is a vast majority of what Alteryx does and offered can execute natively in Snowflake now. In this article we not only explore Alteryx vs native SQL Snowflake functions but also a viable no code / low code flow solution with Datameer for more complex models.
Convert Alteryx Data Inputs (Source Data)
Static Data Files and Shares
There are numerous methods to load semi structured data files into Snowflake the same way you would Alteryx. The difference is instead of file shares and network drives, Snowflake uses modern cloud data storage.
Snowflake has native support for external stages where you can load numerous data formats including:
- AWS S3
- Azure Blob Storage
- Google Cloud Storage (GCS)
- CSV
- JSON
- Avro
- ORC
- Parquet
- XML
XML and JSON Parsing in Snowflake
Snowflake has native parsing for JSON and XML, making it straight forward to extract and transform data from these semi-structured formats.
Unsupported file formats
There are numerous file formats supported by Alteryx which are not supported natively by Snowflake. Excel is probably the most notable file format commonly used in Alteryx.
On Premise and Cloud File Shares (office / google)
Connection to cloud file shares like Office 365 and Google Drive require third party data tools. We are happy to recommend numerous solutions.
Live Data Connections
Snowflake has limited native data connectivity to databases, some of which require configuration and install of a proxy service. The following is a list as of 3-8-2025
| Connector | Description | Requires additional install / config |
|---|---|---|
| Snowflake Connector for Google Analytics Aggregate Data and Raw Data | Enables automatic ingestion of Google Analytics 4 (GA4) data into your Snowflake account. About the Snowflake Connector for Google Analytics Aggregate Data. | No |
| Snowflake Connector for ServiceNow® | Enables you to ingest ServiceNow data into your Snowflake account. For more information see About the Snowflake Connector for ServiceNow®. | No |
| Snowflake Connector for MySQL | Enables you to load data into Snowflake from a MySQL database as well as configure replication so that changes in your MySQL database are replicated to Snowflake. For more information see About the Snowflake Connector for MySQL. | Yes |
| Snowflake Connector for PostgreSQL | Enables you to load data into Snowflake from a PostgreSQL database as well as configure replication so that changes in your PostgreSQL database are replicated to Snowflake. For more information see About the Snowflake Connector for PostgreSQL. | |
| Salesforce Data Cloud | Allows you to take advantage of Zero-copy capabiltiy whereby Salesforce data objects are available to query from Snowflake as views | Salesforce DataCloud Snowflake ) |
Connectivity to enterprise databases and applications
Enterprise database like MS SQL, Oracle, SAP and others are not natively supported out of the box with Salesforce and require data pipelines to stage the data similar to Alteryx.
As an example, Snowflake on Azure will seamlessly integrate with DataFactory making it possible to integrate most popular enterprise data sources. An expansive list of enterprise data connections is available
Data Out from Alteryx
Alteryx can be used as a “data Army Swiss Knife” within an enterprise handling everything from traditional ETL, to Business Intelligence data preparation, and data delivery directly to business users. It is one of the attributes that makes Alteryx great governance and thus migration challenging. The following
Snowflake to SQL Data Transformation and Expressions
Data transformation and processing is where Snowflake shines given it is designed for analytical workloads and large volumes of data. In short any data volumes you process in Alteryx will work without any planning or considerations in Snowflake.
Convert Alteryx to SQL Conversion Worksheet
Schedule a call to meet with our team to get access to our Alteryx to SQL Conversion worksheet.
AI / Analytics
Snowflake has native AI functions with their Cortex AI including text classification, unstructured data processing and chunking of data within Snowflake native Vector data store.
Convert Alteryx Geospatial Functions
Geospatial is a huge part of Alteryx’s origin story and something it does very well. Geospatial and GIS remains a specialized job function and feature set. There are numerous geospatial functions and data sets that may not exist 1-1 and we highly recommend your GIS professional validate the following mapping:
| Alteryx Tool | Snowflake Function | Level of Support |
|---|
| Buffer | ST_BUFFER(geometry, distance) | ✅ Complete |
| Create Points | ST_MAKEPOINT(longitude, latitude) | ✅ Complete |
| Distance | ST_DISTANCE(geo1, geo2), HAVERSINE() | ✅ Complete |
| Find Nearest | ST_DISTANCE(), ROW_NUMBER() ranking | 🟡 Partial |
| Generalize | ST_SIMPLIFY(geometry, tolerance) | ✅ Complete |
| Heat Map | H3 Grid Aggregation, External BI tools | 🟡 Partial |
| Make Grid | H3 H3_POLYGON_TO_CELLS() | 🟡 Partial |
| Non Overlapping Drivetime | Mapbox Isochrone API, ST_DIFFERENCE() | ❌ None |
| Poly-Build | ST_MAKELINE(), ST_MAKEPOLYGON() | ✅ Complete |
| Poly-Split | Manual decomposition via SQL | 🟡 Partial |
| Smooth | GIS libraries (Shapely, ArcGIS) | ❌ None |
| Spatial Info | ST_AREA(), ST_LENGTH(), ST_CENTROID() | ✅ Complete |
| Spatial Match | ST_CONTAINS(), ST_INTERSECTS() | ✅ Complete |
| Spatial Process | ST_UNION(), ST_INTERSECTION(), ST_DIFFERENCE() | ✅ Complete |
| Trade Area (Radius) | ST_BUFFER(point, distance) | ✅ Complete |
| Trade Area (Drive-Time) | Mapbox Isochrone API | ❌ None |
Recommended Process for Migration and Validation
To successfully migrate and validate Alteryx to Snowflake logic and transformation side by side we recommend the following:
Exporting a Complete Alteryx Flow
To have a comprehensive migration path for Alteryx inputs and outputs, we recommend for every workbook preparing yxzp file which includes the workflow and any static data files.
Step 1: Open your workflow: Open the Alteryx workflow you want to package in Alteryx Designer.
Step 2: Navigate to Export Workflow: Go to the Alteryx Designer toolbar and select Options > Export Workflow.
Step 3: Choose Package Location: In the Export Workflow window, specify the location where you want to save the packaged workflow file (a .yxzp file).
Staging Alteryx Input Data to Snowflake
- To validate Alteryx to Snowflake powered processes you need a static snapshot of your source data. We recommend taking a snapshot of your data from Alteryx immediately to Snowflake or cloud data stage (S3,Blob,GCS). This ensures your Alteryx process and Snowflake process are sourced from the exact same data.
- For Static files included in your yxzp, we recommend eitehr manually loading via the Snowflake interface or data stage (S3,Blob,GCS) as a starting point to map and convert your Alteryx workflow.
Convert Alteryx to Low Code Snowflake Flows
Manually converting Alteryx tools to SQL code manually is possible for simple models. However, for large and complex Alteryx models, you an excessive number of common table expressions (CTEs) can make transformation very difficult to follow. To provide continuity of visual flows, we highly recommend Datameer as the solution of choice for customers that are serious about centralizing and standardizing data to a modern data platform. As long time Alteryx users, we feel it adequately covers most of the Alteryx use cases while running 100% on Snowflake. Think of Datameer as the visual builder with Snowflake as the engine.
