Notebook Process - Overview

Introduction

The Notebook Process is a specialized execution node within Syntasa App workflow that enables organizations to operationalize Jupyter Notebooks as first‑class components of automated data pipelines. It bridges the gap between interactive data science development and reliable, production‑grade execution.

By converting a development notebook into a managed process node, teams can embed advanced Python or PySpark logic—such as machine learning models, statistical analysis, and complex data transformations—directly into scheduled workflows. This ensures that analytical logic runs consistently, securely, and at scale alongside other pipeline components such as ingestion, transformation, and publishing steps.

This article provides a comprehensive overview of what the Notebook Process is, how it is configured, how it integrates into workflows, and how to monitor and operate it effectively in production.

What is a Notebook Process?

In the Syntasa ecosystem, an App represents a complete data pipeline, and each step within that pipeline is implemented as a Process node. While standard processes typically handle structured tasks such as:

  • Data ingestion
  • SQL-based transformations
  • Data validation and publishing

Notebook Process is designed to execute an entire Jupyter Notebook file (.ipynb) as a single operational step.

Key Capabilities

The Notebook Process provides several important advantages:

Seamless transition from development to production
Data scientists and engineers can develop and test logic interactively in a notebook workspace and then deploy the same notebook into a production pipeline without rewriting code or translating logic into SQL.

Support for advanced and custom logic
Notebooks can leverage the full Python and PySpark ecosystem, including libraries such as:

  • Scikit‑learn
  • TensorFlow / PyTorch
  • Pandas, NumPy
  • Custom internal packages

This enables use cases that go far beyond traditional SQL‑based processing.

Workflow awareness and dependency management
The notebook executes only when upstream processes have completed successfully, ensuring that required input data is available and consistent.

Standardized execution environment
The process runs within a controlled runtime and infrastructure configuration, providing reproducibility across environments (development, staging, and production).

Configuring a Notebook Process

To add a Notebook Process to an App, drag the Notebook icon onto the App workflow canvas. The configuration interface is organized into three major sections:

  • Notebook Selection
  • Runtime & Infrastructure
  • Parameters (Input / Output)

Each of these sections plays a critical role in ensuring correct and repeatable execution.

Notebook Selection

This section defines which notebook will be executed by the process.

Workspace selection
Choose the workspace that contains the notebook. Workspaces are used to logically organize notebooks by project, team, or business domain.

Notebook Selection
Based on the workspace selection the Notebook drop down get populated and allow user to select the specific Notebook from that workspace.

Version control behavior
By default, the process executes the latest saved version of the notebook in the selected workspace. This ensures that updates made by developers are automatically reflected in subsequent pipeline runs (subject to governance and deployment practices).

Recommendation: For production pipelines, adopt a workspace or branching strategy that clearly separates development and production notebooks.

Runtime & Infrastructure

Notebooks require an execution environment with defined libraries, memory, and compute capacity. This section controls how and where the notebook runs.

Runtime environment
Select a preconfigured runtime template, such as:

  • Python 3.x
  • Spark 3.x with Python support

The runtime determines the available language version, core libraries, and base system configuration.

Compute resources
For PySpark‑based notebooks, configure:

  • Driver memory and CPU cores
  • Executor memory and CPU cores
  • Number of executors (if applicable)

This allows the notebook to scale for large datasets and computationally intensive workloads.

Environment variables
Define key‑value pairs that are injected into the runtime environment at execution time. Common examples include:

  • API tokens
  • Environment identifiers (DEV / QA / PROD)
  • Feature flags

These variables can be accessed directly from within the notebook code.

Parameters (Input / Output)

To support reusability and dynamic behavior, the Notebook Process supports parameterization.

Passing variables from the App
Values such as:

  • process_date
  • batch_id
  • region
  • dataset paths

can be defined at the App level and passed into the notebook at runtime.

Notebook integration
Within the Jupyter Notebook, a dedicated cell is tagged as parameters. During execution, Syntasa automatically overrides the default values in this cell with the values configured in the App Studio.

This approach allows a single notebook to be reused across multiple pipelines, schedules, or environments without modifying the notebook source code.

Scheduling and Workflow Integration

A Notebook Process is fully integrated into the App’s orchestration and scheduling framework and does not run as a standalone task.

Triggering the Process

Upstream dependencies
The process starts only after all connected upstream nodes complete successfully. This guarantees that required datasets and intermediate outputs are available before execution begins.

App‑level scheduling
The Notebook Process inherits the execution schedule of the App. For example:

  • If the App runs daily at 02:00 AM, the notebook will run automatically as part of that daily batch.
  • If the App runs hourly, the notebook will follow the same cadence.

Manual execution
For testing, debugging, or historical reprocessing, users can trigger a Run Now action directly on the Notebook Process node.

Data Flow

Inputs
The notebook can read data from:

  • Syntasa managed datasets
  • Other process outputs
  • External storage systems (such as S3 or GCS), using credentials managed securely by the platform

Outputs
Any datasets, files, or artifacts produced by the notebook should be written to locations accessible by downstream nodes in the App. This allows subsequent processes to consume the results for further transformation, validation, or publishing.

Monitoring and Logs

Once deployed, the Notebook Process can be monitored in real time and historically through the Syntasa user interface.

Process status
Each execution is clearly labeled as:

  • Pending
  • Running
  • Completed
  • Failed

This status is visible in the App Monitor view.

Execution logs
Detailed logs from the Python interpreter or Spark driver—including standard output and error streams—are available directly from the UI. These logs are essential for troubleshooting runtime errors, performance issues, or dependency failures.

Executed notebook artifact
For many executions, Syntasa generates an “executed” copy of the notebook that includes:

  • Cell outputs
  • Charts and visualizations
  • Printed metrics

This provides a point‑in‑time audit trail of exactly what the notebook produced during a specific run.

Best Practices

To ensure reliability, performance, and maintainability, consider the following best practices when designing Notebook Processes:

Design for idempotency
Structure notebooks so that repeated executions with the same inputs do not create duplicate or inconsistent data.

Implement robust error handling
Use structured try/except blocks and explicit error messages to make failures easy to diagnose from logs.

Optimize for scale
For large datasets, prefer PySpark operations over loading data into local Python memory.

Externalize configuration
Use parameters and environment variables instead of hard‑coded values for paths, credentials, and environment‑specific settings.

Keep notebooks focused
Avoid turning a single notebook into a monolithic pipeline. Each Notebook Process should perform a well‑defined, logical task within the broader App.

Summary

The Notebook Process transforms Jupyter Notebooks from interactive development tools into reliable, production‑ready pipeline components. By combining flexible execution environments, parameterization, scheduling, and enterprise‑grade monitoring, Syntasa enables teams to deploy advanced analytics and machine learning workflows with the same rigor as traditional data engineering processes.

This capability empowers organizations to operationalize data science at scale while maintaining governance, reproducibility, and performance across the entire data platform.