Using these recommendations - provide a ballpark monthly estimate for running this system

Unfortunately, providing a precise monthly estimate for running your Databricks system is challenging due to several dynamic factors. However, using the recommendations and assuming certain average costs, I can offer a ballpark figure to help you plan:

Assumptions:

  • Cluster configuration: 2 worker nodes (4 cores, 16 GB RAM each) + 1 driver node (2 cores, 8 GB RAM).
  • Spark runtime: 10.4 LTS.
  • Storage: Azure Data Lake Storage (ADLS) Standard or Gen2 (cost depends on access frequency).
  • Data transfer: Regular data transfer between Synapse Analytics and ADLS for daily updates.
  • Monthly usage: Assuming daily batch updates and occasional additional processing tasks.

Cost components:

  • Databricks cluster resources: Based on current average pricing, the worker nodes (estimated at $0.25/vCPU/hour) and driver node might cost around $1200-$1500 per month.
  • Data transfer: The amount of data transfer between Synapse Analytics and ADLS will impact the cost. Assuming frequent updates but moderate volume (a few GB per day), the cost could be around $50-$100 per month.
  • ADLS storage: Standard ADLS charges per TB stored, while Gen2 has tiered storage options affecting the cost. Assuming 150 GB storage with moderate access, the cost might be around $20-$50 per month.

Ballpark estimate:

Combining these assumptions and current average pricing, the total monthly cost could range between $1300 and $1700 for this Databricks configuration and workload.

Remember, this is just a rough estimate, and the actual cost might vary significantly depending on several factors:

  • Exact cluster configuration (node type, number of nodes): Higher resources will increase the cost.
  • Spark runtime version: Different versions might have different pricing.
  • Data transfer volume: More frequent or larger data transfers will increase the cost.
  • ADLS storage usage and access patterns: Higher storage volume and frequent access will lead to higher charges.
  • Auto-scaling utilization: If auto-scaling effectively reduces idle time, it can significantly lower the cost.

Recommendations for accurate cost estimation:

  • Use the Databricks Cost Estimator: This tool provides a more specific cost estimate based on your chosen configuration and expected usage patterns.
  • Monitor actual resource utilization: Track your cluster usage and data transfer volume to understand your actual costs and refine your configuration for optimal efficiency.
  • Consider Databricks Reserved Instances: This option offers significant discounts on committed cluster resources if your workload is predictable.

By taking these factors into account and monitoring your actual usage, you can refine your configuration and optimize your Databricks setup for both performance and cost-effectiveness.