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Answer: Leveraging Databricks‘ autoscaling feature to automatically adjust the number of nodes in a cluster based on the workload, with cost constraints defined in the job configuration.
1. **Databricks‘ autoscaling feature**: This feature dynamically adjusts the number of nodes in a cluster to meet workload demands, ensuring optimal resource use and cost efficiency. It scales up during high demand and down during low demand, preventing unnecessary costs. 2. **Cost constraints in job configuration**: Setting cost limits ensures autoscaling remains within budget, balancing performance and cost. 3. **Dynamic resource allocation**: This approach guarantees the right amount of resources for each job, avoiding over-provisioning. 4. **Automation**: Autoscaling reduces manual oversight, streamlining operations and enhancing efficiency. In summary, using Databricks‘ autoscaling with predefined cost constraints is the optimal method for managing ETL workloads efficiently and cost-effectively.
Author: LeetQuiz Editorial Team
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To ensure your Spark jobs on Databricks are both cost-effective and scalable while managing varying ETL workloads throughout the day, which strategy would you adopt for dynamically scaling resources based on workload demands?
A
Utilizing spot instances for Spark clusters whenever possible, with a fallback strategy to on-demand instances for critical jobs.
B
Implementing a custom monitoring solution that adjusts clusters based on CPU and memory utilization, integrating with Azure Cost Management for budget tracking.
C
Leveraging Databricks‘ autoscaling feature to automatically adjust the number of nodes in a cluster based on the workload, with cost constraints defined in the job configuration.
D
Scheduling Spark jobs during off-peak hours using Databricks jobs scheduler to capitalize on lower compute costs and reduced contention.
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