Can I use this for Machine Learning projects?

Yes, the skill includes specialized workflows for the ML Scenario Manager, JupyterLab integration, and building robust training and deployment pipelines.

Can this skill help with both Gen1 and Gen2 operators?

Yes, it provides guidance for both generations, including migration patterns and the specific architectural rule that operators cannot be mixed within a single graph.

How does it handle SAP system integration?

It offers detailed patterns for connecting to S/4HANA, BW, and other ABAP-based systems via ODP, CDS Views, and Cloud Connector configuration.

Does it support custom scripting in pipelines?

Absolutely. It covers development for Python, Node.js, and C++ subengines, as well as Data Transformation Language (DTL) functions for SQL-like processing.

SAP Data Intelligence Cloud Developer

Name: SAP Data Intelligence Cloud Developer
Author: secondsky

bysecondsky

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Ciencia de Datos y ML

Streamlines the development of data processing pipelines, ABAP integrations, and machine learning scenarios within SAP Data Intelligence Cloud.

Acerca de

This skill empowers developers to build and manage complex data architectures using SAP Data Intelligence Cloud. It provides expert guidance for creating data processing graphs, developing custom Gen1 and Gen2 operators, and orchestrating seamless integrations with SAP S/4HANA and BW systems. By offering patterns for Replication Flows, Machine Learning Scenario Manager, and Data Transformation Language (DTL), the skill ensures production-ready implementation of data-driven workflows while optimizing performance across Python, Node.js, and C++ subengines.

Características Principales

End-to-end pipeline development using Gen1 and Gen2 operator patterns
Advanced integration for ABAP-based systems including S/4HANA and BW
Comprehensive ML lifecycle management from JupyterLab to production pipelines
2 GitHub stars
Automated replication flow configuration with exactly-once delivery logic
Support for Data Transformation Language (DTL) and custom subengine runtimes

Casos de Uso

Architecting scalable data ingestion pipelines from diverse SAP and non-SAP sources
Developing and deploying custom machine learning models within the SAP BTP ecosystem
Configuring high-volume data replication to HANA Cloud or cloud object storage

Acerca de

Características Principales

End-to-end pipeline development using Gen1 and Gen2 operator patterns
Advanced integration for ABAP-based systems including S/4HANA and BW
Comprehensive ML lifecycle management from JupyterLab to production pipelines
2 GitHub stars
Automated replication flow configuration with exactly-once delivery logic
Support for Data Transformation Language (DTL) and custom subengine runtimes

Casos de Uso

Architecting scalable data ingestion pipelines from diverse SAP and non-SAP sources
Developing and deploying custom machine learning models within the SAP BTP ecosystem
Configuring high-volume data replication to HANA Cloud or cloud object storage