Your DBUs Are Burning on Data You Don't Need
Every record ingested into Delta Lake consumes DBUs for processing, storage for persistence, and compute for queries. Expanso filters upstream - Databricks only processes what delivers value.
Why Databricks Costs Escalate
DBU consumption scales with data volume and cluster size. Unfiltered ingestion drives compute costs far beyond what your actual analytics require.
DBU consumption scales linearly
More Data, More DBUs
Databricks charges per DBU for every notebook execution, every ETL job, and every ML training run. As data volume grows, cluster sizes grow, and DBU consumption grows with them.
Delta Lake stores everything
Write Once, Pay Forever
Raw, unfiltered data lands in Delta Lake and stays. Z-ordering, compaction, and vacuum operations all consume additional DBUs to maintain tables bloated with low-value records.
ML training on noisy data
Garbage In, GPU Hours Wasted
Training ML models on uncleaned, deduplicated data wastes expensive GPU cluster time. Feature engineering pipelines run longer because they're processing noise alongside signal.
Clean Data Before Delta Lake
Expanso processes data before it reaches Databricks. Cleaner ingestion means smaller clusters, fewer DBUs, and faster pipelines - without changing your Databricks workflows.
How Expanso Cuts Databricks Costs
Reduce DBU consumption across ingestion, processing, and ML workloads
Pre-ingestion filtering
Less Data Into Delta Lake
Filter noise, duplicates, and low-value records before they land in Delta Lake. Fewer records means smaller tables, less storage, and faster queries.
DBU reduction through volume
Smaller Clusters, Same Results
When ingestion volume drops 40-60%, auto-scaling clusters stay smaller. ETL jobs finish faster. Interactive queries scan less data. DBU consumption drops proportionally.
ML feature optimization
Train on Signal, Not Noise
Pre-filter and sample training data before it reaches ML pipelines. Reduce GPU cluster hours by training on clean, relevant data instead of processing raw streams.
Delta Lake maintenance savings
Smaller Tables, Less Overhead
Z-ordering, compaction, and OPTIMIZE operations cost fewer DBUs when tables are smaller. Vacuum runs faster. Time travel storage costs less.
Streaming ingestion control
Structured Streaming Costs Less
Filter and aggregate streaming data before it hits Databricks Structured Streaming. Fewer micro-batches, smaller checkpoints, less cluster time.
Data quality at source
No Post-Load Cleanup
Validate schemas, enforce types, and check quality before ingestion. Eliminate expensive post-load data quality jobs that consume DBUs on data that should have been rejected upstream.
Proven Databricks Cost Reductions
Real results from organizations optimizing data before Databricks
Cost reduction for manufacturing ML inference across 2,000 production lines
Monthly savings from $165K to $13K for computer vision pipeline
Production line ML pipelines optimized with upstream filtering
Local inference latency vs 800-2,000ms round-trip to cloud GPUs
Real-World Impact
See how organizations cut Databricks costs with upstream data control
ML Pipeline: $165K to $13K/month
A manufacturer ran computer vision models across 2,000 production lines, sending all image data to cloud GPUs for inference. Expanso enabled local inference at the edge, sending only metadata and anomaly flags to Databricks for analytics.
Enterprise DW: 58% Cost Reduction
A Fortune 500 retail chain reduced data warehouse costs by filtering and processing data at the source before cloud ingestion. The same upstream approach reduces Databricks costs by eliminating noise before it consumes DBUs.
Why Expanso for Databricks
Upstream of Delta Lake
Integrates before data reaches Delta Lake or Unity Catalog. No changes to your Databricks workspace or notebooks.
Works with any cloud
Deploy on AWS, Azure, or GCP alongside your Databricks deployment. Cloud-agnostic by design.
Prove savings on real data
Free tier processes 1TB/day. Test on your highest-volume ingestion pipelines and measure actual DBU reduction.
No Databricks lock-in
If you move workloads between Databricks, Snowflake, or BigQuery, Expanso's upstream filtering moves with you.
Frequently Asked Questions
How does Expanso integrate with Databricks?
Expanso sits upstream of your Databricks ingestion pipeline. It filters and transforms data before it reaches Delta Lake, Auto Loader, or Structured Streaming. Your notebooks, jobs, and dashboards continue working - they just process cleaner, smaller datasets.
Will this affect our ML model accuracy?
No - it improves it. By filtering noise and duplicates before training, models train on cleaner data. Feature engineering pipelines run faster because they process signal instead of noise. Model accuracy typically improves because training data quality improves.
How much can we save on Databricks?
Savings depend on data type and current noise levels. The manufacturing case study saw 92% savings by moving inference to the edge. For typical ETL and analytics workloads, 40-60% DBU reduction is common when filtering noise and duplicates upstream.
Does this work with Unity Catalog?
Yes. Expanso filters data before it enters Databricks. Once data lands in Delta Lake under Unity Catalog governance, all catalog features work normally. Filtered data is fully compatible with Unity Catalog's lineage, access controls, and auditing.
Can we start with specific Databricks jobs?
Yes. Most customers start with their highest-volume or most expensive Databricks jobs. Identify the jobs consuming the most DBUs, then deploy Expanso filtering on the data feeding those specific pipelines.
DBU costs eating your budget?
Every unfiltered record burns DBUs for ingestion, storage, and queries. Filter upstream and cut your Databricks bill.