/ Databricks Cost Evaluation
Measure the datayour lakehouse needs
Run Expanso close to selected data sources, then compare downstream volume and Databricks workload cost against your existing baseline.
/ The problem
Where lakehouse costs accumulate
Ingestion, storage, and compute depend on workload shape and Databricks configuration.
Raw volume
Measure Input
Identify duplicates, unused fields, and records that downstream jobs do not consume.
Compute demand
Measure Work
Record job duration and resources for the current input.
Data utility
Verify Output
Preserve the records and context required by analytics and ML consumers.
/ The Expanso difference
Evaluate processing close to the source
Define a representative workflow, target connected nodes with labels, and measure outputs before changing production ingestion.
/ How it works
What to test
Use customer-owned baselines and acceptance criteria.
Filtering
Selected Records
Test customer-defined filters and inspect retained and rejected classes.
Normalization
Selected Schema
Evaluate transformations against the lakehouse schema and consuming jobs.
Routing
Selected Tables
Verify destination and failure behavior before broader deployment.
/ Outcomes
Customer-specific evaluation
Results depend on data shape, policy, jobs, and Databricks pricing.
Record source volume and current workload cost.
Measure retained volume, job behavior, and data utility.
Use observed results to decide whether and where to expand.
/ Why Expanso
Why evaluate Expanso for Databricks
Near-source execution
Run data-processing pipelines on connected nodes close to where data is generated.
Declarative jobs
Define jobs with YAML and target evaluation nodes with labels.
Measured rollout
Validate a representative flow before changing a wider production path.
/ FAQ
Frequently asked questions
How much will we save?
There is no universal result. Establish a workload baseline and calculate the observed effect using your own Databricks configuration and pricing.
What should we validate?
Validate schema, retained records, downstream job behavior, failure handling, and cost under representative load.
Does Expanso replace Databricks?
No. This evaluation tests upstream processing before selected data reaches Databricks.
/ Ready to start?
Bring a Databricks workloadand its baseline
We’ll help define a bounded evaluation with customer-specific measurements.
