Databricks Best Practices


        Databricks Best Practices

  • Here’s a breakdown of Databricks best practices, covering essential areas to optimize your usage:

    Cluster Configuration

    • Right-size your clusters: Choose instance types and scaling configurations that match your workload demands. Avoid overprovisioning, which can lead to unnecessary costs.
    • Autoscaling:  Enable autoscaling to adjust cluster size based on load, optimizing resource usage.
    • Auto-termination: Set clusters to terminate automatically after a period of inactivity to prevent wasted resources.
    • Instance pools: Pre-warm pools of instances to speed up cluster startup times and reduce costs.
    • Spot instances: Consider using spot instances for non-critical workloads to take advantage of potentially lower costs.

    Data Management

    • Delta Lake: Delta Lake format is used for ACID transactions, schema enforcement, time travel, and performance optimizations.
    • Optimize file sizes: Aim for around 1 GB for efficient processing.
    • Partitioning: Partition large datasets by relevant columns to improve query performance.
    • Caching: Utilize Databricks’ caching mechanisms to speed up repeated data access.

    Workflows and Jobs

    • Databricks Jobs: Schedule and automate workflows using Databricks Jobs for reliable execution.
    • Orchestration tools: Consider using external orchestration tools like Apache Airflow for complex dependencies.

    Notebooks and Development

    • Modularization: Break notebooks into smaller, reusable functions and modules for improved maintainability.
    • Version control: Integrate with Git for versioning and collaboration.
    • Testing and CI/CD: To ensure code quality, set up unit tests and consider continuous integration/continuous delivery (CI/CD) pipelines.

    Security and Governance

    • IAM roles (AWS): Leverage IAM roles to fine-tune access to AWS resources.
    • Secrets management: Use Databricks secrets to store sensitive information like passwords.
    • Unity Catalog: Implement Unity Catalog for centralized governance and fine-grained access control across workspaces, data, and models.

    Cost Optimization

    • Monitoring: Track cluster usage and costs to identify areas for optimization.
    • Spot instances: Use spot instances where possible.
    • Instance Pools: Leverage instance pools for reduced costs and faster startup times.
    • Right-sizing:  Avoid overprovisioning clusters.

    Additional Tips

    • Take advantage of Databricks documentation: Refer to the extensive Databricks documentation for further in-depth best practices.
    • Stay updated: Databricks releases new features and optimizations regularly, so follow their updates.

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