Azure Synapse Analytics notebooks provide a flexible, collaborative analytics workspace for processing big data.
While notebooks are powerful out of the box, there are many tips and tricks that can help you get even more value from them.
As an experienced Synapse user, I’ve compiled my top techniques for supercharging your Synapse notebooks.
When I first started using Microsoft Azure Synapse Analytics, I primarily relied on the default settings. But over time, through trial and error, along with researching best practices, I uncovered many ways to optimize my development process.
Whether you’re new to Synapse notebooks or have used them extensively, I think you’ll find these tips helpful.
One key way to enhance your Synapse notebooks is by improving performance. Here are some methods I’ve found effective:
- Use Apache Spark pools instead of serverless SQL pools for intensive data transformation and machine learning workloads. Spark pools scale more efficiently for complex tasks.
- Tune Spark configurations like executor memory, core count, and parallelism to fit your specific workload needs. Don’t just accept the defaults.
- Cache datasets you use repeatedly with Spark caching. This avoids re-reading from storage each time.
- Use vectorized code instead of row-by-row processing where possible. Operations like UDFs see major speed boosts with vectorization.
- Materialize views so downstream queries hit pre-computed results rather than executing on demand.
Notebooks really shine for collaborative analytics when used to their full potential:
- Store notebooks in Git for version control and dev/prod environments. This enables code reviews, change tracking, and deployment automation.
- Parameterize key values so users can customize without modifying code. Parameters make notebooks reusable for different datasets.
- Create documentation notebooks with usage guidance and examples for other users.
- Modularize code into reusable snippets, functions, and visualizations using .py scripts. Reduce duplicate logic across notebooks.
- Develop reusable templates for onboarding new users and standardizing workflows.
You can really maximize your personal productivity in Synapse notebooks with these tips:
- Master keyboard shortcuts for running cells, adding cells, and more. This avoids constant clicking and scrolling.
- Use cell tagging to logically group and run sections of long notebooks. Add #tags for more readable workflows.
- Load Spark profiles to apply predefined configuration instead of manually tuning each time.
- Integrate external tools like D BT, MLflow, and Power BI to leverage work in other platforms.
- Schedule notebooks to run on triggers or calendars for automated execution.
By implementing techniques like these, I’ve been able to achieve significant gains in performance, collaboration, and ease of use with Azure Synapse notebooks.