Managing Lakehouse Costs: Storage Optimization, Compute Scaling, and Data Lifecycle Strategies

As data volumes continue to surge, managing costs in a data lakehouse architecture is a top priority for data leaders. The flexibility, scalability, and performance of lakehouses are undeniable—but without proactive cost management, expenses can quickly spiral. Here’s how organizations can optimize storage, scale compute efficiently, and manage the data lifecycle for maximum ROI, with benchmarks and real-world examples.

1. Storage Optimization: Tiered Storage, Partitioning, and File Management

Tiered Storage: Lakehouses support multiple storage tiers—hot, warm, and cold—allowing organizations to balance cost and data accessibility.

• Hot storage is ideal for frequently accessed data but comes at a premium.
• Warm storage is less expensive, with slightly higher latency.
• Cold storage offers the lowest cost but is best for archival data, with longer retrieval times.

Best Practice:

• Store only active, high-demand datasets in hot storage.
• Move historical or infrequently accessed data to warm or cold tiers using automated policies.
• For example, Databricks users can cut storage costs by up to 70% by archiving data to cold storage while keeping it ready for queries for compliance or analytics needs.

Partitioning and Compaction:

• Partitioning data (e.g., by date or region) enables faster queries and reduces unnecessary scans, directly lowering compute costs.
• Regular compaction merges small files into larger, optimal ones, addressing the “small file problem” that can degrade both performance and cost efficiency.

Real-World Example: An energy company using a cloud lakehouse reduced storage costs significantly by implementing tiered storage and regular compaction, while also improving operational efficiency and decision-making.

2. Compute Scaling: Dynamic Allocation and Auto-Scaling

Dynamic Compute Allocation: Lakehouses decouple storage from compute, allowing organizations to scale compute resources up or down based on workload demand.

• Dynamic allocation (e.g., in Spark or Databricks) automatically provisions more resources during peak loads and releases them when idle, preventing over-provisioning and unnecessary costs.
• Auto-scaling ensures you only pay for the compute you actually use, which is especially valuable for unpredictable or spiky workloads.

Benchmarks:

• Organizations can reduce their lakehouse compute costs by up to 75% through implementing C2S’ patented compute optimization accelerators.
• Organizations using dynamic compute allocation have reported 15–30% reductions in compute costs by eliminating idle resources and right-sizing clusters.
• Monitoring and tuning compute usage is also another way to optimize costs, with Databricks and similar platforms providing granular usage metrics for ongoing adjustment.

3. Data Lifecycle Management: Retention, Archival, and Deletion

Retention Policies: Define clear data retention policies based on regulatory, business, and analytical needs4.

• Retain only what’s necessary for compliance or analytics.
• Archive or delete obsolete or redundant data to free up storage and reduce costs.

Archival Strategies: Move infrequently accessed data to lower-cost storage tiers while maintaining accessibility for compliance or occasional analysis.

• For example, archiving historical transaction logs can reduce storage costs by up to 80% compared to keeping all data in hot storage.

Automated Deletion: Implement automated deletion of expired data to prevent unnecessary accumulation and associated costs.

4. Regular Audits and Monitoring

• Conduct regular audits of storage and compute usage to identify underutilized resources and opportunities for optimization.
• Use built-in monitoring tools to track consumption by workload, department, or project, enabling transparent cost allocation and accountability.

Cost management in a data lakehouse is an ongoing process that blends technology, policy, and operational discipline. By leveraging tiered storage, dynamic compute scaling, and robust data lifecycle strategies—along with regular audits—organizations can achieve significant, measurable cost savings while maintaining the agility and performance that modern analytics demand.