What is the primary difference between Gen1 and Gen2 operators?

Gen1 operators are legacy components focused on compatibility, whereas Gen2 operators offer modern features like automatic error recovery, state management via snapshots, and native multiplexing.

How does this skill support ABAP system integration?

It provides specific patterns for Cloud Connector setup, ABAP connection management, and utilizing CDS Views or ODP for efficient data extraction.

Does this skill help with Machine Learning tasks?

Yes, it covers the ML Scenario Manager, tracking metrics with the SDK, and converting JupyterLab experiments into production training pipelines.

Which programming languages are supported for custom operators?

The skill provides guidance for developing operators in Python 3.9, Node.js, and C++, as well as ABAP for pipeline engine operators.

Can I mix Gen1 and Gen2 operators in a single graph?

No, a single pipeline must exclusively use either Gen1 or Gen2 operators; mixing them is not supported to ensure execution stability.

SAP Data Intelligence Cloud Development

Name: SAP Data Intelligence Cloud Development
Author: secondsky

bysecondsky

•

Data Science & ML

Builds and optimizes data processing pipelines, integrations, and machine learning scenarios within the SAP Data Intelligence Cloud environment.

About

This skill provides comprehensive guidance for developers working with SAP Data Intelligence Cloud to architect robust data orchestration workflows. It assists in creating complex graphs using Gen1 and Gen2 operators, developing custom operators across Python, Node.js, and C++ subengines, and managing seamless integrations with ABAP-based systems like S/4HANA. From configuring replication flows for high-volume data movement to managing the full ML lifecycle with the ML Scenario Manager, this skill ensures implementations follow production-grade patterns and SAP best practices.

Key Features

Graph orchestration with Gen1 and Gen2 operator frameworks
Custom operator development using Python, Node.js, and ABAP subengines
11 GitHub stars
Machine Learning lifecycle management and JupyterLab integration
Automated replication flow configuration with exactly-once delivery
Deep S/4HANA and SAP BW integration via Cloud Connector and ODP

Use Cases

Orchestrating end-to-end ML scenarios from experimentation to model deployment
Creating custom Python-based operators for specialized real-time data transformations
Building ELT pipelines to synchronize on-premise SAP data with cloud data warehouses

About

Key Features

Graph orchestration with Gen1 and Gen2 operator frameworks
Custom operator development using Python, Node.js, and ABAP subengines
11 GitHub stars
Machine Learning lifecycle management and JupyterLab integration
Automated replication flow configuration with exactly-once delivery
Deep S/4HANA and SAP BW integration via Cloud Connector and ODP

Use Cases

Orchestrating end-to-end ML scenarios from experimentation to model deployment
Creating custom Python-based operators for specialized real-time data transformations
Building ELT pipelines to synchronize on-premise SAP data with cloud data warehouses