Cutting Cloud Waste on Databricks: Turning ROT into ROI with Pentaho Data Optimizer

Databricks has become one of the main enterprise platforms for lakehouse analytics and AI. While incredibly powerful, its agility masks an uncomfortable truth: data sprawl and unnecessary data retention are quietly driving up compute and storage bills. 

As AI pipelines expand and datasets multiply and grow, many organizations are fighting escalating storage costs. Often, they are paying for “ROT” (redundant, obsolete, trivial) data they don’t need, for jobs that run longer than they should, and for storing data in overly expensive locations that don’t align with their use or needs. 

This is where Pentaho Data Optimizer (PDO) helps you take back control. Data storage optimization with PDO identifies waste, automates remediation, and right-sizes where and how your data lives, so Databricks stays fast while keeping cloud costs in line with value. 

Find the Waste and Keep the Value 

PDO surfaces ROT and cold data across your lakehouse, classifying what should be archived, tiered, or eliminated while preserving the high-value, high-velocity datasets your teams rely on. It complements existing governance and cataloging efforts by pairing intelligence about data sensitivity and lineage with practical actions. Moving, retiring, compressing, or purging happens within your guidelines and strategy, going beyond a simple optimization report to traceable and refined action. 

PDO sets the table to become “Data Fit”: the right data, in the right place, in the right shape to drive analytics and AI effectively with high-velocity datasets your teams can rely on. 

PDO for Databricks-Aware Optimization 

On Databricks, cloud spend typically comes in threes: storage (object store + Delta tables), compute (clusters + jobs), and data movement. PDO effectively targets each. 

• Storage tiering & retention: Automatically route older snapshots and low-touch tables to lower-cost tiers while maintaining auditability and restore paths. 
• Reduce data duplication: Trim stale copies and intermediate artifacts that accumulate across development, staging, and production. 
• Streamline pipelines: Remove unnecessary joins and oversized inputs by pruning columns and rows at the source, cutting job runtimes and shuffle costs. 

This is smart simplicity in action: making complex data environments easier to use with strong controls that reduce costs and support compliance. 

Reinforcing and Extending Governance 

A frequent concern with optimization is risk. Data professionals rightly ask, “If we delete or move data, will we break audits or models?” PDO anchors actions in policy. Sensitive data stays protected; business-critical data stays highly available. 

Everything follows a rules-based path to lower-cost storage tiers based on use and value, without compromising lineage or recoverability. And by integrating optimization with cataloging and data quality efforts, you create a virtuous cycle of cleaner datasets, which feed faster pipelines and better models, reducing both cost and complexity over time.  

Building the Business Case 

Finance leaders and storage/DB admins can easily model initial savings based on existing source systems, elimination percentages, and tiering choices using the Pentaho Data Optimizer ROI Calculator. The calculator helps estimate savings for your own lakehouse in minutes, turning cloud cost conversations into concrete plans. 

Ready to turn ROT into ROI? 

Explore the ROI calculator and our Databricks one-pager to see how teams are stabilizing spend while accelerating outcomes. Then put PDO to work in your lakehouse and make your cloud cost practice as disciplined as your data strategy.