Learn when to use shared vs dedicated Databricks clusters with Nick Lee. Compare cost, performance, isolation, and security, and build a simple decision framework for analytics, jobs, and production workloads.
Not all Databricks clusters are created equal—and choosing the wrong type can hurt both performance and cost. In Shared vs Dedicated Clusters, Nick Lee breaks down the practical differences between shared clusters used by many users and dedicated clusters reserved for a single workload or team. You’ll learn how each model affects isolation, resource contention, security, and user experience, using clear, real-world examples instead of theory.
From there, the session focuses on making the right choice for your scenarios. Nick walks through common patterns—ad hoc analysis, scheduled jobs, production pipelines, and high-security workloads—and maps them to shared or dedicated designs. You’ll also explore governance and cost-management tips, including policies, tagging, and monitoring, so your cluster strategy stays predictable as usage grows. By the end, you’ll have a decision framework you can apply to new and existing Databricks environments.
Course Outline ( Free Preview)
Module 01 - Introduction
In this module, Nick outlines the essential prerequisites for working with Azure Databricks clusters, including workspace access, appropriate permissions, and necessary role-based access controls. He emphasizes the importance of having the right setup to create, attach, and manage clusters, as well as access to Azure Data Lake storage for seamless data operations. With these foundations in place, students will be prepared to explore the differences between shared and dedicated clusters and understand their impact on cost, performance, and governance.
Module 02 - Why Cluster Types Matter
In this module, Nick Lee introduces the critical architectural decision of choosing between shared and dedicated clusters in Azure Databricks, emphasizing their impact on performance, cost, and security. Students will learn how each cluster type supports different workloads, the trade-offs involved, and how to apply governance through cluster policies to manage resources efficiently. By the end, learners will be equipped to select and configure the right cluster model to balance collaboration, control, and cost within their Databricks environment.
Module 03 - Shared Clusters6 min.
In this module, Nick introduces shared clusters in Azure Databricks, explaining how they enable multiple users to collaborate efficiently by sharing the same compute resources in a high concurrency environment. The focus is on the benefits of cost efficiency, centralized management, and suitability for interactive analytics workloads, while also addressing the challenges of resource contention and limited user control. Students will learn how shared cluster policies govern configurations and resource allocation to balance performance, security, and cost in multi-user settings.
Module 04 - Dedicated Clusters6 min.
In this module, Nick explains the concept and benefits of dedicated clusters in Azure Databricks, highlighting their role as isolated compute environments designed for single users or specific workloads. Dedicated clusters offer predictable performance, enhanced security, and full control over configuration, making them ideal for production pipelines, machine learning, and sensitive data processing. The module also covers the trade-offs, such as higher costs and management overhead, and demonstrates how to create and customize dedicated clusters using different policy settings.
Module 05 - Shared vs Dedicated Comparison3 min.
In this module, Nick compares shared and dedicated clusters, highlighting their distinct advantages and trade-offs. Shared clusters promote collaboration and cost efficiency by allowing multiple users to share resources, while dedicated clusters provide isolation, consistent performance, and control for single users or jobs. Students will learn how organizations often adopt a hybrid approach, using shared clusters for exploratory tasks and dedicated clusters for production or sensitive workloads.
Module 06 - Security and Governance7 min.
In this module, Nick explores the critical intersection of cluster configuration with enterprise security and governance policies in Databricks. Students will learn how cluster policies, permissions, and credential pass-through work together to enforce data access controls, manage user roles, and maintain compliance across shared and dedicated clusters. The module also provides practical guidance on creating and managing cluster policies, setting user permissions, and configuring security settings to align with organizational standards.
Module 07 - Security Cost and Performance4 min.
In this module, Nick explores the critical differences between shared and dedicated clusters in Azure Databricks, emphasizing their impact on security, cost, and performance. He explains how shared clusters enable multiple users to work simultaneously with efficient resource use but require robust access controls, while dedicated clusters offer isolated environments ideal for sensitive workloads with predictable performance at a higher cost. Additionally, Nick highlights key features like auto scaling and auto termination that help optimize cluster efficiency, ensuring the right balance between governance, speed, and budget.
Module 08 - Governance and Policy Controls2 min.
In this module, Nick explains the importance of governance in managing Databricks environments as organizations scale. They introduce cluster policies and role-based permissions as key tools that help administrators enforce cost controls, security, and compliance while maintaining flexibility for users. By defining preset configurations for different user needs, these policies prevent cluster sprawl and ensure consistent, efficient, and secure workspace management.
Module 09 - Best Practices and Wrap Up4 min.
In this module, Nick Lee summarizes the key distinctions between shared and dedicated clusters, emphasizing the importance of balancing cost, governance, and workload requirements when choosing the right cluster type. He highlights best practices such as using shared clusters for collaborative, lightweight tasks and dedicated clusters for intensive, production workloads, alongside strategies like auto scaling, auto termination, and policy enforcement to optimize performance and cost. By applying these principles, students will learn how to build a scalable, secure, and efficient Databricks environment tailored to their organization’s needs.
Nick has been a dedicated trainer and consultant since 2018, leveraging his extensive experience working with major companies, including Fortune 200 corporations, professional sports organizations, government entities, and leading firms in the finance and healthcare sectors. With a specialized focus on Power BI and data engineering, Nick has consistently demonstrated his ability to drive data-driven decision-making and optimize business processes. His commitment to excellence and his in-depth technical expertise have made him a trusted advisor and sought-after expert in the industry.
