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Target Type |
Input |
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Databricks Notebook |
- In Output Type, select Python 3 as the output type format for the generated artifacts.
- In Default Database, select the database against which all the pipelines are configured.
- In Source Database Connection, select the required source database to load the data such as Oracle, SQL Server, Teradata, Netezza, etc. If the database is selected, the converted code will have connection parameters related to the database. In case the database is not selected you need to manually add the database connection details to the parameter file to execute the dataset.
- In Attainable Automation, select the way you want the system to calculate achievable automation for the transformation of the source scripts.
- Assessment-Based: Calculates the level of automation based on assessment logic. The conversion-config.json file contains a pre-defined automation percentage for each component and you can also modify it as required.
- Transformation-Based: Calculates the level of automation based on actual conversion. In this method, automation percentage is calculated for each component based on the used, supported, and unsupported properties.
- 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.
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Databricks Lakehouse |
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AWS Glue Job |
- In Output Type, select Python 3 as the output type format for the generated artifacts.
- In Default Database, select the database against which all the pipelines are configured.
- In Source Database Connection, select the required source database to load the data such as Oracle, SQL Server, Teradata, Netezza, etc. If the database is selected, the converted code will have connection parameters related to the database. In case the database is not selected you need to manually add the database connection details to the parameter file to execute the dataset.
- In Attainable Automation, select the way you want the system to calculate achievable automation for the transformation of the source scripts.
- Assessment-Based: Calculates the level of automation based on assessment logic. The conversion-config.json file contains a pre-defined automation percentage for each component and you can also modify it as required.
- Transformation-Based: Calculates the level of automation based on actual conversion. In this method, automation percentage is calculated for each component based on the used, supported, and unsupported properties.
- 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.
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Spark |
- In Output Type, select Python 2 or Python 3 as the output type format for the generated artifacts.
- In Default Database, select the database against which all the pipelines are configured.
- In Source Database Connection, select the required source database to load the data such as Oracle, SQL Server, Teradata, Netezza, etc. If the database is selected, the converted code will have connection parameters related to the database. In case the database is not selected you need to manually add the database connection details to the parameter file to execute the dataset.
- In Attainable Automation, select the way you want the system to calculate achievable automation for the transformation of the source scripts.
- Assessment-Based: Calculates the level of automation based on assessment logic. The conversion-config.json file contains a pre-defined automation percentage for each component and you can also modify it as required.
- Transformation-Based: Calculates the level of automation based on actual conversion. In this method, automation percentage is calculated for each component based on the used, supported, and unsupported properties.
- 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.
- Choose the Validation Type - None or Cluster. If the Validation type is Cluster, upload the Data source.
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Matillion ETL |
- In Data Interaction Technique, select your data interaction method. Following are the options:
- Snowflake - Native: Select Snowflake - Native to fetch, process, and store data in Snowflake.
- Snowflake: External: Select this data interaction technique to fetch input data from an external source such as Oracle, Netezza, Teradata, etc., and process that data in Snowflake, and then move the processed data or output to an external target. For instance, if the source input file contains data from any external source like Oracle, you need to select Oracle as the Source Database Connection to establish the database connection and load the input data. Then data is processed in Snowflake, and finally the processed or output data gets stored at an external target (Oracle). However, if you select Oracle as the Source Database Connection but the source input file contains data from an external source other than Oracle, such as Teradata, then by default, it will run on Snowflake.
- If the selected data interaction technique is Snowflake: External, you need to specify the source database of your data. In the Source Database Connection, select the database you want to connect to. This establishes the database connection to load data from external sources like Oracle, Teradata, etc. If the database is selected, the converted code will have connection parameters (in the output artifacts) related to the database. If the database is not selected, you need to add the database connection details manually to the parameter file to execute the dataset; otherwise, by default, it executes on Snowflake.
- In Output Type, select JSON as output type format for the generated artifacts.
- In Default Database, select the source database to convert input graphs to the target equivalent if the source database is not defined in the input file.
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Leaplogic