For organizations leveraging Databricks, balancing cost and performance is crucial. How do you manage to keep costs down while optimizing performance? Are there any strategies or tools you use to monitor and improve cost efficiency on Databricks?
For organizations leveraging Databricks, balancing cost and performance is crucial. How do you manage to keep costs down while optimizing performance? Are there any strategies or tools you use to monitor and improve cost efficiency on Databricks?
Nihal - Good question, it is bigger wide topic, but these strategies you can look at overall.
Right-size compute
Choose appropriate VM/instance types.
Avoid over-provisioning workers.
Use autoscaling for variable workloads.
Use serverless compute where appropriate.
Cluster/job management
Prefer Job Clusters for scheduled ETL instead of always-on clusters.
Enable auto-termination for interactive clusters.
Use instance pools to reduce cluster startup time.
Use spot instances for fault-tolerant batch workloads.
Use Photon
Enable Photon for SQL, ETL and supported workloads.
Particularly useful for CPU-intensive SQL/DataFrame workloads.
Optimize Spark workloads
Reduce unnecessary shuffles.
Handle data skew.
Avoid unnecessary repartition.
Use broadcast joins for small dimension tables.
Avoid repeated full-table scans.
Don’t cache data unless it is reused.
Optimize Delta tables
Use OPTIMIZE for file compaction.
Control small files.
Use partition pruning.
Use appropriate Z-Ordering/data clustering where beneficial.
Use VACUUM according to retention requirements.
Incremental processing
Prefer CDC/incremental loads over full refreshes.
Use Structured Streaming / Auto Loader for incremental ingestion.
Process only changed data.
SQL Warehouse optimization
Use serverless SQL warehouses where appropriate.
Configure warehouse auto-stop.
Right-size warehouse size.
Use query history and query profiles to identify expensive queries.
Databricks Tools for Monitoring Cost
System Tables → DBU, billing, cluster and workload-level usage.
Databricks SQL Query History → identify expensive SQL queries.
Query Profile → find bottlenecks, scans, joins and shuffles.
Spark UI → stage/task/executor performance analysis.
Jobs & Workflows → job duration, failures and compute consumption.
Cluster Metrics → CPU, memory, disk and executor utilization.
Lakeflow Jobs / Workflows → monitor pipeline execution.
Databricks Asset Bundles (DABs) → standardized deployment/configuration.
Unity Catalog → governance, lineage and controlled data access.
Databricks billing/system-table dashboards → build cost-per-job / cost-per-domain / cost-per-TB monitoring.