How to Optimize Your Notebook for a Notebook Process

When transitioning a notebook from an interactive data exploration tool to a production-grade Notebook Process within a Syntasa workflow, it is critical to ensure that the code is dynamic, parameter-driven, and fully integrated with the platform’s managed data ecosystem.

Optimizing a notebook primarily involves:

• Eliminating hardcoded values

• Using system-injected runtime parameters

• Replacing local file I/O with managed tables and storage paths

• Following production best practices for stability and observability

Replace Hardcoded References with System Parameters

Syntasa automatically injects a set of system parameters into the notebook’s runtime environment when executed as a Notebook Process. Leveraging these parameters makes your notebook workflow-aware, allowing it to run seamlessly across different environments, dates, and schedules without manual code changes.

Key Injected Parameters

Refactoring Example (Python)

Before (Hardcoded)

# Hardcoded database and date range
df = spark.sql(
    "SELECT * FROM my_dev_db.raw_events WHERE event_date = '2023-10-01'"
)

# Hardcoded output path
df.write.mode("overwrite").parquet("gs://my-temp-bucket/output_data")

After (Optimized)

# Using injected system parameters
query = f"""
SELECT * 
FROM {database}.raw_events 
WHERE event_date = '{fromDate}'
"""
df = spark.sql(query)

# Writing to the managed output location
df.write.mode("overwrite").parquet(location)

This approach ensures the notebook behaves consistently across workflows, schedules, and environments.

Transition from Local Files to Managed Tables

Interactive notebooks often rely on local files, uploaded CSVs, or temporary /tmp storage. These patterns are not suitable for production Notebook Processes.

Instead, use the Spark session to interact with:

• The managed Event Store (Hive Metastore)

• Cloud storage paths provided by Syntasa

Reading Data

Avoid local file reads such as:

pandas.read_csv("/path/to/local/file.csv")

Use managed tables instead:

# Read from a managed table
input_df = spark.table(f"{database}.input_dataset_name")

Writing Data

Syntasa manages the lifecycle of datasets produced by a Notebook Process. Always write final results to the path provided by the injected location variable.

# Standard write pattern for Notebook Processes
output_df.write.format("parquet") \
    .mode("overwrite") \
    .save(location)

This ensures downstream workflow nodes can reliably consume the output.

3. Handling Multiple Outputs

If your Notebook Process is configured with multiple output datasets, Syntasa injects a unique location variable for each output using the pattern:

location_<templateTableName>

Example

If an output node is named ProcessedUsers, the injected variable will be:

location_processedusers

# Writing to multiple managed outputs
df_users.write.mode("overwrite").save(location_processedusers)
df_metrics.write.mode("overwrite").save(location_processedmetrics)

This pattern ensures each output dataset is tracked and managed independently.

4. Best Practices for Production Notebooks

Remove Visualization Code

Remove or comment out:

• df.show()

• plt.show()

• Large print() statements

These can clutter execution logs and consume unnecessary resources during scheduled runs.

Use Error Handling

Wrap critical logic in try-except blocks to produce meaningful error messages in execution logs:

try:
    transformed_df = transform_data(input_df)
except Exception as e:
    raise RuntimeError(f"Data transformation failed: {e}")

Leverage MLflow for Tracking

If your notebook performs model training or experimentation, use the injected mlflowUrl to log parameters, metrics, and artifacts to MLflow.

import mlflow

mlflow.set_tracking_uri(mlflowUrl)

with mlflow.start_run():
    mlflow.log_param("alpha", 0.5)
    # training logic

Maintain Partitioning Strategy

When writing large datasets, preserve Event Store partitioning by using date-based columns such as fromDate or partitionDate.

df.write \
  .partitionBy("event_date") \
  .mode("append") \
  .save(location)

Summary

Optimizing a notebook for a Notebook Process ensures it is:

• Parameter-driven

• Environment-agnostic

• Fully integrated with Syntasa-managed data assets

By following these best practices, your notebooks will execute reliably as scheduled processes and scale smoothly within the Syntasa workflow ecosystem.