Recommend a Databricks cluster configuration to manage a 150 GB Synapse Analytics database. After a one-time data load of around 100 GB, a daily batch will load one to five MB of transactions.

Here's a recommended Databricks cluster configuration for managing a 150 GB Azure Synapse Analytics database, considering the one-time data load and daily batch updates:

1. Cluster size:

  • Worker nodes: Start with 2-3 worker nodes, each containing 4-8 cores and 16-32 GB RAM. This provides sufficient compute power for the daily batch updates and occasional larger tasks like the initial data load.
  • Driver node: Choose a driver node with 2-4 cores and 8-16 GB RAM to handle cluster coordination and communication with Synapse Analytics.

2. Spark runtime version:

Consider using the latest stable Spark runtime version available on Databricks (currently 10.4 LTS) for optimal performance and security.

3. Storage and resources:

  • Use Azure Data Lake Storage (ADLS) for persistent storage: Store your data and notebook libraries in ADLS for reliable and cost-effective storage. Mount the ADLS container to your Databricks workspace for easy access.
  • Utilize auto-scaling features: Opt for auto-scaling on worker nodes to dynamically adjust resources based on your workload. This optimizes cost efficiency by scaling down during idle periods and scaling up during peak loads.

4. Additional optimizations:

  • Optimize your data loading code: Use efficient Spark data connectors like Delta Lake for Synapse Analytics to optimize data ingestion and transformations.
  • Partition your data: Partition your Synapse Analytics database on frequently accessed columns to improve query performance within Databricks.
  • Monitor cluster performance: Utilize Databricks metrics and logs to monitor your cluster utilization and adjust your configuration based on actual usage patterns.

Cost considerations:

  • Carefully balance performance with cost: Start with a smaller cluster and scale up only if needed based on actual workload demands.
  • Leverage auto-scaling: Maximize cost efficiency and avoid wasting resources when the cluster is idle.

Remember, this is a starting point, and you may need to adjust the configuration based on your specific data processing needs and budget constraints. Consider performing load testing and monitoring actual performance to refine your cluster configuration for optimal efficiency and cost-effectiveness.