BEZNext Cloud Performance and Financial Governance Optimization
Cloud Data Platform Selection
Build performance, resource consumption and data usage profile for each business workload
Predict the minimum configuration and budget needed to meet Service Level Goals for each business workload on each of cloud data platform
Cloud migration decisions optimization
Loading data on time and within the budget
Optimize security decisions
Set realistic performance and financial expectations
Dynamic Capacity Management
Predict the impact of expected changes and set performance and financial expectations
Verify results, determine performance and financial anomalies and their root causes
Evaluate options and predict measures necessary to continuously meet SLGs for all workloads on all platforms with the lowest cost
Organize continuous close loop control
Our History
We’ve offered capacity management software and services for large data warehouses on Teradata, DB2, Oracle, and SQL Server for several decades
We added Big Data (Hadoop) support in 2014 and Cloud Data platforms support in 2017
The Need
How to reduce the uncertainty and risk of performance and financial surprises while making cloud migration and dynamic capacity management decisions in the Hybrid Multi-Cloud environments?
Current Focus
We enhanced our modeling and optimization software to optimize performance and financial governance of Hybrid Multi-Cloud environments
DevOps decisions optimization prior to deployment of new applications
Illustration of BEZNext Iterative Modeling and Gradient Optimization determining the minimum configuration required to meet SLGs for all business workloads
Workload characterization and performance characteristics of the cloud data platforms targets, including pricing data are input to our modeling technology.
The iterative queueing network modeling and gradient optimization performs multi step automatic evaluation to predict the minimum configuration and budget required to meet SLGs of each business workload on each of the Cloud Data Platform being evaluated.
The BEZNext modeling and optimization technology has been proven on numerous projects across multiple industries.
Applying BEZNext Technology for Cloud Data Platform Selection
Predict the minimum configuration and budget required to meet SLGs for each workload on each cloud data platform
Differences in cloud data platforms architecture
The security implication and tokenization overhead
Consider the change in resource demand pattern during different hours of the day and expected workload and volume of data growth during next 12 months
Review our white paper: Which Cloud Data Platform is Best for Your Data Warehouse?
Recommendations to Operations during DevOps prior to deployment of new Application
Select cloud data platform for new application prior to deployment
Predict the minimum configuration and budget required for deployment of the new application on different cloud data platforms, taking into consideration expected increase in number of concurrent users and volume of data after deployment comparing with the test environment and SLG
Set up realistic performance and financial expectations Organize process of verification of results and development proactive recommendations
Verify results
Organizing Dynamic Capacity Management for Hybrid Multi-Cloud
BEZNext software optimizes the resource allocation and workload management rules to meet SLGs for each workload on each platform with the lowest cost
We automate Data Collection, Workload Characterization and discovery performance and financial anomalies and root causes for each workload
We apply iterative modeling and gradient performance optimization to compare options and develop proactive performance tuning, resource allocation and workload management recommendations
Automatic results verification achieved by comparing the actual measurement data with expected / predicted results
Feedback control enables continuous Dynamic Capacity Management
We offer a Hands-On Workshop on Cloud Performance and Financial Governance
Description
Cloud platform selection, migrating workloads to the cloud, and dynamic capacity management of hybrid multi-cloud environments
Nine hours of instruction and hands-on learning delivered over three weeks (three hours per week)
Workshop includes instructor-led lectures, videos, quizzes and lab exercises
The lab exercises will give students first-hand knowledge of the skills needed to effectively select and manage cloud platforms
Participants will have access to BEZNext modeling and optimization software
Participants will run and modify Python program to compare several ML algorithms of anomaly detection.
Prerequisites
This workshop is designed for participants who understand Cloud capabilities and have had exposure to Data Warehouse performance management.
Workshop participants will learn how to:
Collect measurement data on-prem and cloud platforms.
Perform workload characterization
Detect anomalies and their root causes
Determine seasonality for each workload
Predict the minimum configuration and cost required to meet SLAs for each workload on each platform
Analyze Workload Patterns and Resource Management
Organize continuous Dynamic Capacity Management to meet SLAs for each workload on each platform.
Audience, Who should attend:
This workshop is for managers, architect, analysts, DBAs, etc. who participate in Cloud Data platform selection and management of Hybrid Multi-Cloud environments. For the complete program description, please download the workshop brochure here.
Let us assist you in getting more from your Enterprise IT environment