Databricks Consulting → Performance Optimization
Databricks Performance Optimization
Reduce Databricks workload runtime and improve data platform performance through architecture, SQL and workload optimization.
Sound familiar?
Are your Databricks workloads suffering from…
Long-running Spark jobs?
Expensive SQL queries?
Small-file problems?
Poor cluster utilization?
Slow joins?
Excessive shuffles?
Inefficient data layouts?
Poor partitioning?
Slow MERGE operations?
High-latency dashboards?
What we analyze
Where we look for the bottleneck
Spark
- Query execution plans
- Shuffle behaviour
- Partitioning & skew
- Parallelism
- Executor utilization
Delta Lake
- File sizes
- Data layout
- OPTIMIZE & compaction
- Liquid clustering
- Data skipping
- Deletion vectors
SQL
- Joins
- Filters
- Aggregations
- Subqueries
- Query plans
Compute
- Cluster sizing
- Worker configuration
- Photon
- Autoscaling
- Spot instances
- Workload isolation
Architecture
- Bronze/Silver/Gold design
- Incremental processing
- Streaming architecture
- Job dependencies
Methodology
How we run an optimization engagement
Case study
How Karsient reduced a Databricks workload from 14 hours to 5 hours
A retail client's nightly batch job was missing its SLA window. We profiled the pipeline, re-clustered the largest Delta tables, fixed a skewed join, and right-sized the cluster — cutting runtime by nearly two-thirds without changing business logic.