New SnapLogic Sigma Framework: Continuous Data Processing with Headless Ultra Tasks SnapLogic Ultra Tasks offer the speed and scalability needed for critical integrations that demand high availability, high throughput, and continuous execution. This feature ensures that data reaches its destination in realtime, regardless of data volume, endpoint variety, or integration complexity. This document serves as a reference guide detailing the necessary configurations and steps for the successful deployment of Headless Ultra tasks in the SnapLogic platform. This guide is targeted towards SnapLogic developers and administrators who have familiarity with the SnapLogic platform, tasks, and pipelines. Author: Ram Bysani, SnapLogic Enterprise Architecture team
Hi James H. - Clarifying how Python libraries get installed when using the Remote Python Executor (RPE). How Python Libraries Are Installed in RPE The installation process depends on which approach you're using: 1. Using the Standard RPE Image (Pre-built) When you use the standard RPE Docker image with:
docker pull snaplogic/rpe:main-5
docker run --memory-swap="-1" --restart=always -dti -p 5301:5301 -e "REMOTE_PYTHON_EXECUTOR_TOKEN=" -v /opt/remote_python_executor_log/:/opt/remote_python_executor_log/ --name=rpe snaplogic/rpe:main-5The libraries are automatically installed at runtime using the SLTool.ensure() method in your Python script. You don't need to run pip commands manually. Here's how it works:
from snaplogic.tool import SLTool as slt
# Libraries are automatically installed when ensure() is called
slt.ensure("scikit-learn", "0.20.0")
slt.ensure("keras", "2.2.4")
slt.ensure("tensorflow", "1.5.0")2. Using a Custom RPE Image If you want to pre-install libraries or have more control, you can:
Download the custom RPE package (contains Dockerfile and requirements.txt)
Add your required libraries to requirements.txt
Build your custom Docker image:
docker build --no-cache -t snaplogic_custom_rpe .
Run the custom container:
docker run --memory-swap="-1" --restart=always -dti -p 5301:5301 -e "REMOTE_PYTHON_EXECUTOR_TOKEN=" -v /opt/remote_python_executor_log/:/opt/remote_python_executor_log/ --name=rpe snaplogic_custom_rpe
Key Points:
No manual pip commands needed - The RPE handles library installation automatically
Runtime installation happens when you use SLTool.ensure() in your Python scripts
Custom images allow you to pre-install libraries via requirements.txt during the Docker build process
The requirements.txt approach is used when building custom RPE images, not for runtime installation
The confusion likely comes from the fact that there are two different workflows - the standard image uses runtime installation via SLTool.ensure(), while custom images use the traditional requirements.txt + Docker build approach.
SnapLogic MCP Support (published September 16, 2025 by Tim Fan) explains how SnapLogic is integrating the Model Context Protocol (MCP) into its platform and outlines what that support looks like, how it works, and why it matters for customers building AI-driven workflows.
Automated Deployment (CICD) of SnapLogic assets with GitHub This guide is a reference document for the deployment of SnapLogic assets to a GitHub repository. It also includes sample YAML code for a GitHub Actions workflow which can be used to automate the deployment of assets across orgs (Dev -> Stg / Stg -> Prod, etc.) This guide is targeted towards SnapLogic Environment Administrators (Org Administrators) and users who are responsible for the deployment of SnapLogic assets / Release management operations. Section B covers automated deployment with GitHub Actions, and Section A illustrates a manual deployment flow using the Manager interface. Author: Ram Bysani SnapLogic Enterprise Architecture team
AWS SageMaker Model Integration I am using Snaplogic to create a data set that I then write as a CSV to S3. My next step is to make a call to the SageMaker model that reads the data and writes an output file to S3. I am currently not able to execute the SageMaker model. I am attempting to use
HTTP Client snap
AWS Signature V4 Account
Is there anything special that you did to the user account or SageMaker?
SnapLogic Test Automation with Robot Framework: A Complete Testing Solution By Sriswapna Pothana In today’s fast-paced integration landscape, ensuring the reliability and performance of your SnapLogic pipelines is crucial. We’re excited to introduce a comprehensive test automation framework that combines the power of Robot Framework with SnapLogic’s APIs to deliver a robust, scalable, and easy-to-use testing solution. This approach leverages the snaplogic-common-robot [PyPI-published library] to provide prebuilt Robot Framework keywords for interacting with SnapLogic Public APIs, integrated within a Docker-based environment.. This lets teams spin up full SnapLogic environments on demand—including Groundplex, databases, and messaging services—so tests run the same way everywhere This blog post explores two key components of our testing ecosystem:
snaplogic-common-robot: A PyPI-published library https://pypi.org/project/snaplogic-common-robot/
providing reusable Robot Framework keywords for SnapLogic automation
snaplogic-robotframework-examples: A public repository
providing a complete testing framework with baseline test suites and Docker-based infrastructure for comprehensive end-to-end pipeline validation
Read more details in this SnapLogic Technical Blog >>
We’re excited to share that Integration Nation is moving to a new home — a Slack-based community designed to make it easier, faster, and more natural for you to connect with peers and the SnapLogic team. This transition will give you:
Real-time conversations with other SnapLogic users, partners, and experts
More active peer-to-peer knowledge sharing
Faster help and collaboration across use cases, best practices, and AI-powered automation
Dedicated channels for product topics, SnapGPT instant answers, and more
A more modern and engaging space to learn, share, and build together
What’s changing
Integration Nation’s URL stays the same — you’ll continue to access the community from the same place you do today.
From the site, you’ll find a direct link to join the new Slack community
All future conversations, announcements, and peer discussions will take place in Slack.
Why we’re making this move Members have told us they want a faster, more conversational way to engage, and Slack is already where many of you collaborate day to day. Moving to Slack allows Integration Nation to become a truly real-time, high-value community—where insights spread faster, questions get answered quicker, and connections happen more naturally. What you need to do Just click the link to join the new Integration Nation Slack community. Once inside, you’ll be able to explore channels, meet other members, and jump into discussions immediately. We can’t wait for you to experience this new chapter of Integration Nation. If you have any questions, feel free to reach out - we’re here to help.