AI Agent for Databricks Cost Optimization

Reduce cost by up to 30-70%
Estimate your Savings
Case Study

Fortune 200 Databricks savings.

The challenge
Fortune 200 enterprise

Profile: a major Databricks shop spending multi-millions annually.

Status: strong engineering team, years tuning jobs, clusters and SQL; already lean.

The expectation

“At our scale, even a few percent reduction is huge.”

The result
~$1M
savings unlocked for
25% of their production environment during
free trial
Projected savings: >$3M on entire production.

Job Optimization

DBUs saved

Cluster Optimization

DBUs saved

SQL Warehouse Optimization

DBUs saved

More Savings on Azure,
AWS, GCP

Infra spend on top

Automated Spend Optimization

Observability
  • Automated Warehouse Optimization continuously optimizes warehouses for optimal efficiency.
  • By analyzing historical and real-time data, it right-sizes resources and improves query execution.
  • Proactively adjusts warehouse and cluster sizes based on workload demands.

Performance and Optimization Insights

Observability
  • Warehouse Performance Report: Automatically know annualized cost savings achieved through dynamic warehouse optimization strategies with query performance and detailed performance metrics
  • Usage and Efficiency Metrics: Use detailed insights into Snowflake resource usage, query distribution, and user-level credit consumption with warehouse efficiency, credit trends, and idle resources, enabling precise cost optimization and planning.
  • Automated  Warehouse Management: Automatically optimizes your warehouse maximizing annual savings, and policies, helping with strategic resource allocation.

Automatic Data Observability and Quality

Observability
  • Reduced Downtime: Quick identification and remediation of anomalies mitigate potential disruptions in analytics or operations and  preserves the accuracy and trustworthiness of data assets.
  • Enhanced Operational Productivity: Automating monitoring frees data teams to focus on strategic projects rather than manual quality checks.
  • End-to-End Visibility: Insight into both upstream causes and downstream impacts provides holistic data quality management.
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Key Benefits:

Automated Cost Optimization
Full visibility into spend and resource utilization helps identify and quickly address spending inefficiencies automatically or assisted.
10x Improvement in Jobs Efficiency
Actionable insights on jobs performance and query activity facilitate immediate efficiency improvements.
Automated Data Observability and Quality
Automatic real-time monitoring and identification of data quality incidents strengthens overall trust in organizational data, reducing risk.

Automated Cost Optimization

  • Use Automated  Warehouse Management to automatically optimize or manually optimize your warehouses using details about each warehouse.
  • Understand usage patterns, query time, performance details to make right decisions.
  • Job configuration and optimization helps manage spending.
Observability
Observability

Jobs Configuration and Optimization

  • Proactive jobs management, displaying key details about each job.
  • Track current and past runs to proactively identify issues.
  • Experiment and Optimize your jobs by executing the job multiple times with different settings.

Automatic Data Observability and Quality

  • Reduce Downtime by automatically identification and remediation of anomalies to mitigate potential disruptions in analytics or operations and  preserves the accuracy and trustworthiness of your data assets.
  • Enhance operational productivity with automated monitoring which frees data teams to focus on strategic projects rather than manual quality checks.
  • Use dashboard, schema and lineage insights into both upstream causes and downstream impacts for complete data quality management.
Observability

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