Elevate Cloud Cost Management with Datametica

1. Q1 Q2 Q3 Take your Cloud Cost Optimization to the Next Level with Datametica’s Workload Optimization By Datametica June 12, 2023 Blogs Home » Blogs » Take your Cloud Cost Optimization to the Next Level with Datametica’s Workload Optimization This blog is the second part of the series “FinOps Simpli?ed.” Learn more about the entire blog series here. As an increasing number of businesses migrate to the cloud, they are becoming aware of a signi?cant shift in t market: the initial rush to migrate to the cloud has subsided, leaving them with the imperative task of effective managing the ever-increasing cloud costs. Consequently, Cloud FinOps is becoming an increasing priority. Cloud FinOps is the practice of combining the principles of ?nancial management with cloud operations with the go of optimizing costs and maximizing the value of cloud investments. In our previous blog, we discussed ho Datametica’s ?ve FinOps pillars can be optimally applied to the cloud. But is cost optimization simpler in theory than in practice? How can businesses optimize their costs during and pos migration? Let’s take a closer look at the challenges with cloud cost optimization and how Datametica’s three uniq models help you tackle them head-on. Challenges with Cloud Cost Optimization The ability to understand the cloud costing model is the most signi?cant challenge to cost optimization. As the numb of cloud-based offerings grows, it becomes challenging for organizations to monitor and comprehend every aspect, resulting in cloud wastage and high cloud spending. In addition, the design and coding practices that worked on legacy platforms may not function as well on the cloud, resulting in increased CPU/Memory/Disk consumption and thereby costs. Without functional knowledge, modifying this code would be extremely challenging. Lastly, the need for constant analysis makes the whole process complex. Addressing these challenges through Workload Optimization As we discussed previously, optimizing the infrastructure is essential for minimizing cloud waste and enabling organizations to ef?ciently utilize cloud services. However, if the code is not suitable for the cloud environment, it cou result in poor performance and an increase in cloud capacity consumption, resulting in higher cloud costs. Datametica takes a step further than infrastructure optimization and helps businesses optimize their workloads as an essential next step in the cloud cost optimization journey. Workload optimization identi?es and addresses all of the application’s weak areas. This approach delves deeper into the application’s code, identi?es redundant cycles and joint types, and makes the code ef?cient (lean and mean). The ef?cient code limits I/O (read/write), thereby decreasin the number of CPU cycles and VM memory footprints. As a result, the use of resources and cloud costs are lowered.  Workload optimization can be achieved through three key strategies: optimizing Data Models, optimizing Applicatio Code, and optimizing Design and Architecture. 1 Optimizing data models This strategy involves analyzing data structure and legacy code to reduce storage requirements and improve performance. The primary goal of optimizing data models is to ensure that the database can handle increasing volumes of data, support complex queries and transactions, increase parallelism, and deliver fast and consistent results with minimal resource usage. It also reduces I/O and data shuf?ing (movement of data across different machines which are participating in a parallel processing system) by having the right indexing, clustering, data type optimization, and partition mechanism. 2 Optimizing Code Code optimization entails examining the code utilized by a workload and pinpointing opportunities to enhance performance and decrease resource usage. The goal of optimizing code is to improve the ef?ciency, scalability, and cost-effectiveness of cloud applications and services by getting the most optimized execution plan (fewer cycles, reduced CPU and memory usage, and minimal disc I/O), reducing resource usage and improving their performance. This can be achieved by techniques such as parallelization and algorithm optimization. 3 Optimizing design and architecture Optimizing design and architecture involves recognizing areas where enhancements can be made to minimize bottlenecks, enhance fault tolerance, and decrease the need for manual intervention by implementing automation an orchestration. It focuses on ensuring that cloud-based applications and services can handle increasing volumes of data and users, support complex business logic and work?ows, and deliver fast and consistent results with minimal resource usage and downtime. It chooses the services and features which are the right ?t not from a functional point of view, but from a Return on Investment (ROI) point of view. Optimizing design and architecture can be increasingly achieved using a cloud Native and fully managed services and components. It’s time to integrate FinOps into your migration journey Practicing workload optimization brings various advantages to FinOps, including cost savings, higher ROI, improved security and performance, enhanced sustainability, increased agility and visibility, and much more. However, to simpl the process, it is advisable to follow a systematic approach that promotes effective right-sizing practices and regular utilizes analytics to gather insights and take appropriate actions. Datametica is a global leader in modernizing and migrating data to the cloud. Our knowledge and years of experienc in helping businesses migrate multiple workloads with different patterns and properties to the cloud can help you optimize your costs while migrating to the cloud. Reach out to us today to learn how! FAQs Q: Why do we need cloud cost optimization? Q: What are the bene?ts of cloud cost optimization? Q: What is the concept of cost optimization? 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