Organizations moving and managing workloads in the cloud face similar challenges of selecting the cloud data platform and managing a Hybrid Multi-Cloud environment. This presentation will review the case study of applying our modeling and optimization technology in addressing these challenges.

Organizations moving and managing workloads in the cloud face similar challenges of selecting the cloud data platform and managing a Hybrid Multi-Cloud environment, including:
 How to select an appropriate cloud data platform to meet performance and financial goals?
 Benchmark tests do not represent actual workload well and can cost performance and financial surprises
 Tokenization and Detokenization overhead is different on cloud data platforms?
 How to reduce risk of performance and financial surprises in Hybrid-Multi-Cloud environment
 How to meet SLGs for ETL workloads in a Multi-Cloud environment
 How to optimize DevOps decisions prior to new applications deployment
 How to optimize Capacity Management decisions
 Increasing demand for resources affects performance and cost
 Not effective management workloads in Hybrid Multi-Cloud environment could be very costly and affect business completeness
 How to optimize FinOps decisions
 How to verify results
 Wrong decisions can trigger performance and financial surprises and can cause the job for C level executives.

This presentation will review the case study of applying our modeling and optimization technology in addressing these challenges:
• We will review how to continuously collect measurement data on premises and on all cloud data platforms
• How to aggregate measurement data into business workloads and build performance, resource utilization, data usage and financial profiles by each workload on each platform
• How to determine seasonality and workload and volume of data growth
• How to predicting the Minimum Configuration and Budget required to meet SLGs for each workload on each platform
• How to optimize the resource allocation and workload management across in Hybrid Multi-Cloud environment
• How to automate problems determination
• Performance and financial anomalies and root cause determination for all workloads on all platforms
• Apply modeling and optimization to recommend how to fix problems
• Tokenization and detokenization impact on performance and cost
• ETL processes optimization
• DevOps process optimization
• FinOps optimization
• How to compare predicted performance and financial results with actual measurement data and organize continuous close loop control to reduce risk of performance and financial surprises
• Wrong decisions can affect business and can cause the job loss by C level executives.

Takeaways:

  • How to apply modeling and optimization to select appropriate clod data platform for Data Warehouse workloads
  • How to optimize FinOps and dynamic capacity management for Hybrid Multi-Cloud environment
  • How to determine performance and financial anomalies and organize close loop control of the Hybrid Multi-Cloud environment

Speaker:

Boris Zibitsker is a CEO at BEZNext. His research implemented in modeling and optimization products which were used by hundreds of Fortune 500 companies to optimize strategic and tactical capacity management decisions.