Azure Databricks Optimization: 4-Week Implementation

Blueprint Technologies LLC

Blueprint will run a historical and current analysis on the code and infrastructure in your Azure Databricks workspace and optimize your price-to-performance balance across the environment.

Performance and Cost Optimization + Monitoring for Azure Databricks

Our Azure architects have developed a framework to monitor and report on granular costs of running Azure Databricks, factoring in 400+ distinct variables to drive continual optimization recommendations. With our proprietary tools for parsing out the complex costs of running cloud data platforms, we will provide a historical and current analysis of what you have spent by job and resource, allocated to specific business functions, reports, and applications.

Once the analysis is complete, you will receive a detailed set of recommendations for changes to both the infrastructure and the code related to your Azure Databricks jobs that will improve performance and reduce waste. Our engineers will then work with you to prioritize a roadmap for addressing the most urgent and highest value recommendations immediately and will help to implement the changes.

At the end, you will have access to a dashboard for ongoing monitoring and alerts set to custom thresholds based on your needs as well as quarterly check-ins from our experts to ensure that your cloud data platform is running at maximum performance and efficiency.

Save 35-50% on cloud costs

Our team analyzes your Databricks environment to quickly identify areas of improvement, share those insights, and create an optimization roadmap. Clients who implement these changes typically save 35-50% on cloud infrastructure costs and increase query speed 60-100X.

Monitor detailed performance metrics on any cloud platform

We provide custom dashboards that break down usage and cost on a per-job and per-resource basis. Our tools deliver unprecedented visibility into costs, performance and line-of-business attribution.

Prevent bottlenecks, timeouts, and crashes

Our models assess workloads against the resources on which they run to help our team quickly identify opportunities for improving the performance and reliability of your Databricks applications.

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