Streamlining Your Data Warehouse: A 75% Cost Reduction Strategy

Data warehouses are invaluable tools for business intelligence and analytics. However, the sheer volume of data they store can lead to significant compute costs. In this blog post, I'll share a strategy I implemented that successfully reduced data warehouse compute costs by 75%, all while ensuring critical workflows remained uninterrupted.

The Challenge: Identifying Unused Resources

The primary challenge in optimizing data warehouse costs lies in pinpointing unused resources. Traditional metrics like query execution frequency may not always tell the whole story. "Zombie dashboards," for example, may still exist but haven't been actively used in months. Deleting such resources can free up significant processing power without impacting ongoing operations.

The Detective Work: Analyzing Access Patterns and Data Sources

To identify these hidden cost drivers, I adopted a two-pronged approach:

1. Dashboard Access Analysis: We meticulously examined access logs for dashboards associated with various data sources. By comparing the last access times of these dashboards with their expected usage patterns, we could identify potential candidates for deprecation.

2. Data Source Investigation: For resources without associated dashboards (often intermediate tables), we investigated their purpose.

   • End State vs. Intermediate Tables: Identifying whether the data source was an end-state table (directly used for analysis) or an intermediate table (used for processing) was crucial.

   • Dependency Tracing: For intermediate tables, we traced their dependencies on downstream resources (end-state tables). If the end-state tables were actively used, we knew the intermediate tables still served a purpose.

The "Scream Test": Safely Removing Unused Resources

Once we identified potentially unused resources, we employed a safe and controlled approach for removal:

1. Pipeline Pausing: We temporarily paused the data pipelines feeding the identified resource.

2. Monitoring Period: We closely monitored the environment for a predetermined period (e.g., a few weeks) to ensure no disruptions occurred due to the paused pipeline.

3. Resource Deprecation: If no issues arose during the monitoring period, we proceeded with deprecating the resource, either by:

   • Code Removal: Permanently removing the pipeline code from the data warehouse environment, adhering to company policies regarding data deletion or archiving.

   • Data Archiving: Especially for logging data, archiving the data was a safe option while reducing the overall data footprint managed by the warehouse.

The Results: Significant Cost Savings and a Leaner Data Warehouse

By applying this systematic approach, we achieved a remarkable 75% reduction in data warehouse compute costs. This not only translated to substantial financial savings but also streamlined our data infrastructure, making it more efficient and manageable.

Conclusion: A Continuous Optimization Process

Data warehouse optimization is an ongoing process. Regularly reviewing access patterns and data sources remains crucial for identifying and removing unused resources. The "scream test" provides a safe and controlled method for verifying the impact of removing potential candidates. By implementing these strategies, you can ensure your data warehouse operates at peak efficiency while minimizing costs.