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Organizations experience uncertainty while comparing AWS, Azure and Google as Cloud platforms and Snowflake, Redshift, Vantage and Big Query Data Warehouse platforms in the Cloud.
They are looking the answers to questions like,
• Will the current production and new workload work well in the Cloud?
• What is the minimum configuration (Number of instances and Instance type) that will be required to satisfy business performance requirements (Service Level Goals – SLGs) for the expected increase in number of users and volume of data?
• How much will it cost to meet performance requirements during the next 12 months?
The “Trial and Error” approach of selecting a Cloud platform is too risky. It can affect performance and often costs much more than expected.
BEZNext addresses these challenges by providing software, services and training, and organizing automated Performance Assurance Process for Cloud and On Prem environments.
How are we different from other companies?
We are an independent company without any gravitation to specific technology or Cloud platform.
We are the leader in Performance Assurance and our unique software incorporates modeling and performance optimization generating recommendations and expectations enabling automatic verification of results.
We have a proven technology and record of helping many Fortune of 500 companies with Performance Assurance ofData Warehouses and Big Data applications in On Prem and Cloud environments.
About our software
BEZNext keeps you a Step ahead with Performance Assurance software, services and training that optimize Cloud Selection and Dynamic Capacity Management of Clouds and On Prem environments.
Our unique software incorporates ML, AI, Iterative Queueing Network Models and Gradient Optimization technology to assist customers with Cloud Selection and determination of Dynamic Capacity Management actions necessary to meet Service Level Goals with lowest cost. We apply our technology to the entire application life cycle, starting with Performance Engineering during DevOps for new applications, Performance and Workload Management in production environment and Strategic Capacity planning, helping clients compare options, predict cost and select the most appropriate Cloud and On Prem solutions.
What very specifically do we do?
1. Help Customers to Select Appropriate Cloud for Data Warehouse Workloads
We collect performance measurement data and do Workload Characterization and Performance Analysis of Production Workload in On Prem Environment.
Delivering World-Class Performance Assurance Solutions for Cloud Platform Selection and Dynamic Capacity Management for Data Warehouse and Big Data Workloads
A typical Cloud selection project takes 2 - 4 weeks. It includes Data Collection, Workload Characterization, Modeling and Optimization and delivery report with findings and recommendations. BEZNext Modeling and Optimization solutions reduce uncertainty and risk of performance and financial surprises.
Value of our Cloud Selection Solutions
• Reduce Risk of Performance and Financial Surprises
• Reduce Uncertainty and Provide Realistic Expectations
• Significantly reduce time for POC process
• Enable verification by comparing actual results with expected/predicted
2. Help Customers Implement Performance Assurance of Production Environment
After selection of the Cloud platform we offer Dynamic Capacity Management solutions during entire Application Life Cycle:
Performance Engineering During DevOps Process
Our Performance Engineering Solutions Incorporating Modeling and Optimization complement results of Load and Stress testing during the DevOps process. It provides value to both Application Developers and Operations.
Value of our Performance Engineering Solutions for Application developers:
• Predict new applications implementation impact
• Predict how new applications will perform in production environment
• Identify Anomalies and their Root Causes during performance testing of new applications
• Develop performance tuning recommendations for Application Developers
• Predict how new applications will affect performance of existing production applications
• Develop proactive performance tuning, workload management, and capacity planning recommendations
• Set up realistic expectations
Value of our Performance Engineering Solutions for Operations
• Selection of appropriate Cloud platform for new application
• Develop Proactive Performance Management, Workload Management and Capacity Planning Recommendations
• Compare performance measurement results with expected
• Develop proactive performance tuning recommendations
Dynamic Capacity Management
• We automate Data Collection, Workload Characterization and discovery of the most frequent and severe anomalies for Data Warehouse and Big Data applications, determination of their root causes, changes in pattern of resource utilization to narrow down performance tuning and workload management optimization efforts.
• We apply iterative modeling and gradient performance optimization to compare options and develop proactive performance tuning, resource allocation and workload management recommendations.
• We recommend the most appropriate platforms and configurations required to meet SLGs for growing Data Warehouse and Big Data workloads in production On Prem and Cloud environments. It addresses the following challenges:
Value of Our Dynamic Capacity Management Optimization
• Automaton of problem prediction, most frequent and server Anomalies and Root Cause determination, Pattern change recognition, Analysis of Availability, and balance of resource utilization narrows down the scope of performance management and workload management optimization
• Alerting and active dashboards
• Modeling and optimization compare alternatives and develop proactive Performance Management and Workload Management Optimization recommendations.
• Automatic verification by comparing actual results with expected
Customers say our solutions reduce uncertainty, provide realistic expectations and lower the risk of performance and financial surprises. The performance and cost predictions enable verification of results by comparing the actual results with expected.