Configuring Alteryx

To configure the Alteryx analytics conversion stage, follow the below steps:

In Source Type, select the source data source as Alteryx.
In Original Data Source, upload the files that you need to transform to the target source.

In Target Type, select the preferred target type, such as AWS Glue Notebook, Databricks Lakehouse, or Spark.
Click Data Configuration.

The Input column in the table below provides the input requirements based on the Target Type selection.

Source Type	Target Type	Input
Alteryx	AWS Glue Notebook	In Output Type, select Python as the output type format for the generated artifacts. Enable the Code Optimizer toggle to optimize the converted artifacts while preserving business logic and flow. In Source Database Connection, select the required source database to load the data such as Oracle, SQL Server, Teradata, Netezza, DB2, Vertica. In S3 Bucket Base Path, provide the S3 storage repository path where you need to store the source and target files. In Artifacts Location, specify the location from where you need to call external files such as parameter files, orchestration scripts.
	AWS Glue Job
	Databricks Lakehouse	In Output Type, select Python or Databricks / Apache Spark (PySpark + Spark SQL), based on the format in which the output should be generated. Enable the Code Optimizer toggle to optimize the converted artifacts while preserving business logic and flow. In Source Database Connection, select the required source database to load the data such as Oracle, SQL Server. In DBFS File Base Path, specify the DBFS (Databricks File System) location where you need to fetch the input files and store the transformed data. In other words, it is a base path for input files and output data.
	Databricks Notebook
	Spark	In Output Type, select Python as the output type format for the generated artifacts. Enable the Code Optimizer toggle to optimize the converted artifacts while preserving business logic and flow. In Source Database Connection, select the required source database to load the data such as Oracle, SQL Server, Teradata, Netezza, DB2, Vertica. In File Base Path, specify the base path for input files and output data. In Artifacts Location, specify the location from where you need to call external files such as parameter files, orchestration scripts.

In AI Augmentation, select the preferred Transformation model to convert data transformations and inline queries that require augmented transformation, then validates them for accuracy and optimal performance.

In Transformation Model, select the preferred Transformation model to convert data transformations and inline queries that are not handled by the default LeapLogic Core transformation engine to the target equivalent. The Transformation Model supports both Open-Source AI models (LeapLogic) and Enterprise AI models (Amazon Bedrock such as Claude, Nova, etc.) for transformation.
The Open-Source AI models include:

OpenOrca 7B, GPTQ 4-bit quantized Model (Hugging Face), Llama 8B, 16-bit quantized Model (Ollama), and Llama 8B, 4-bit quantized Model (Ollama): To convert small- to medium-sized ETL Scripts.
Code Llama 34B, GPTQ 4-bit quantized Model (Hugging Face): To convert complex ETL scripts.

In Additional Prompt, provide supplementary instructions in addition to system-generated prompts. For example: Ensure the given DDL metadata is applied during conversion and validation for casting, precision, and scale based on the target system.
In Response Fine Tuning, provide examples based on the additional prompt to fine-tune the response to align the generated output with your preferred level of detail. For example, input could be INSERT INTO CUSTOMER (cust_id, cust_name, credit_limit, discount_rate) VALUES (1, ‘JohnDoe’, 12345.6789, 0.123456); and the expected output could be INSERT INTO CUSTOMER (cust_id, cust_name, credit_limit, discount_rate) VALUES (1, ‘JohnDoe’, CAST (12345.6789,numeric(10,2)), 0.123456);

In Review Model, select one or more Review Models to perform syntax validation on the data transformations and inline queries transformed by the LeapLogic Core engine and the Transformation Model. The Review Model validates the transformed inline queries and data transformations syntactically and suggests corrections where required. You can configure multiple Review Models. When multiple Review Models are configured:

The first Review Model validates the data transformations and inline queries transformed by the LeapLogic Core engine and the Transformation Model. If any transformations or queries are identified as incorrectly transformed, it suggests updated queries.
The updated queries are then passed to the next Review Model, which validates them and suggests corrections, if required.
This process continues through all configured Review Models until the queries are successfully validated and the optimized queries are generated.

Click Save to update the changes.
Click to provide a preferred pipeline name.
Click to execute the pipeline. Clicking (Execute) navigates you to the listing page which shows your pipeline status as Running state. It changes its state to Success when it is completed successfully.
Click pipeline card to see report.

To view the Alteryx Transformation Stage report, visit Alteryx Transformation Report