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Explore discussions on Databricks administration, deployment strategies, and architectural best practices. Connect with administrators and architects to optimize your Databricks environment for performance, scalability, and security.
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Databricks deployment and automation tools comparison.

Learnit
New Contributor II

Hello All, 

As a newcomer to databricks, I am seeking guidance on automation within databricks environments. What are the best best practices for deployment, and how do Terraform, the REST API, and the databricks SDK compare in terms of advantages and disadvantages? Any references document would be greatly appreciated.

Thanks in Advance

 

1 ACCEPTED SOLUTION

Accepted Solutions

Kaniz_Fatma
Community Manager
Community Manager

Hi @Learnit , Certainly! As a newcomer to Azure Databricks, understanding best practices for deployment and automation is crucial. 

 

Let’s explore some recommendations and compare the tools you’ve mentioned:

 

Best Practices for Deployment in Azure Databricks:

  • The Azure Databricks documentation provides a wealth of best practices to optimize performance and cost efficiency when using and administering Databricks. Some key areas covered include:
    • Delta Lake: Learn about best practices related to Delta Lake, which provides ACID transactions and data reliability.
    • Hyperparameter Tuning with Hyperopt: Optimize your machine learning models effectively.
    • Deep Learning in Databricks: Explore best practices for deep learning workloads.
    • CI/CD (Continuous Integration/Continuous Deployment): Understand how to set up CI/CD pipelines for seamless deployment.
    • Cluster Configuration: Configure clusters efficiently.
    • Cluster Policies: Define policies for resource management.
    • GDPR and CCPA Compliance Using Delta Lake: Ensure compliance with data privacy regulations.
    • Identity Pools: Manage identity and access control.

Azure Databricks: 14 Best Practices for Developers:

  • This article covers specific best practices for developers working with Azure Databricks. Some highlights include:
    • Choice of Programming Language: Select the appropriate language based on your cluster type.
    • ADF (Azure Data Factory) for Invoking Databricks Notebooks: Integrate Databricks with ADF.
    • Widget Variables: Use widget variables for dynamic parameterization.
    • Key Vault for Storing Access Keys: Securely manage secrets.
    • Organization of Notebooks: Keep your notebooks well-organized.
    • Include Appropriate Documentation: Document your code and processes.
    • Use AutoComplete to Avoid Typographical Errors: Leverage autocomplete features.
    • Code Review with ‘Comments’ Feature: Collaborate effectively.

Software Engineering Best Practices for Notebooks:

Operational Excellence with Databricks Repos and REST API:

Remember that each tool has its advantages and disadvantages:

 

Terraform:

  • Advantages: Infrastructure as Code (IaC), declarative syntax, supports multiple cloud providers.
  • Disadvantages: Learning curve, limited Databricks-specific features.

Databricks SDK (Python):

  • Advantages: Native integration with Databricks, programmatic control, rich functionality.
  • Disadvantages: Requires Python knowledge.

REST API:

  • Advantages: Universal, can be used with any programming language.
  • Disadvantages: Requires manual HTTP requests, less user-friendly.

For detailed information, explore the references provided in the links above.

 

Happy automating! 🚀🤖

View solution in original post

1 REPLY 1

Kaniz_Fatma
Community Manager
Community Manager

Hi @Learnit , Certainly! As a newcomer to Azure Databricks, understanding best practices for deployment and automation is crucial. 

 

Let’s explore some recommendations and compare the tools you’ve mentioned:

 

Best Practices for Deployment in Azure Databricks:

  • The Azure Databricks documentation provides a wealth of best practices to optimize performance and cost efficiency when using and administering Databricks. Some key areas covered include:
    • Delta Lake: Learn about best practices related to Delta Lake, which provides ACID transactions and data reliability.
    • Hyperparameter Tuning with Hyperopt: Optimize your machine learning models effectively.
    • Deep Learning in Databricks: Explore best practices for deep learning workloads.
    • CI/CD (Continuous Integration/Continuous Deployment): Understand how to set up CI/CD pipelines for seamless deployment.
    • Cluster Configuration: Configure clusters efficiently.
    • Cluster Policies: Define policies for resource management.
    • GDPR and CCPA Compliance Using Delta Lake: Ensure compliance with data privacy regulations.
    • Identity Pools: Manage identity and access control.

Azure Databricks: 14 Best Practices for Developers:

  • This article covers specific best practices for developers working with Azure Databricks. Some highlights include:
    • Choice of Programming Language: Select the appropriate language based on your cluster type.
    • ADF (Azure Data Factory) for Invoking Databricks Notebooks: Integrate Databricks with ADF.
    • Widget Variables: Use widget variables for dynamic parameterization.
    • Key Vault for Storing Access Keys: Securely manage secrets.
    • Organization of Notebooks: Keep your notebooks well-organized.
    • Include Appropriate Documentation: Document your code and processes.
    • Use AutoComplete to Avoid Typographical Errors: Leverage autocomplete features.
    • Code Review with ‘Comments’ Feature: Collaborate effectively.

Software Engineering Best Practices for Notebooks:

Operational Excellence with Databricks Repos and REST API:

Remember that each tool has its advantages and disadvantages:

 

Terraform:

  • Advantages: Infrastructure as Code (IaC), declarative syntax, supports multiple cloud providers.
  • Disadvantages: Learning curve, limited Databricks-specific features.

Databricks SDK (Python):

  • Advantages: Native integration with Databricks, programmatic control, rich functionality.
  • Disadvantages: Requires Python knowledge.

REST API:

  • Advantages: Universal, can be used with any programming language.
  • Disadvantages: Requires manual HTTP requests, less user-friendly.

For detailed information, explore the references provided in the links above.

 

Happy automating! 🚀🤖

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