Amid the swiftly changing terrain of software development, DevOps emerged, ushering in a transformative era that reshaped how teams collaborate and bring applications to fruition. As the industry embraced agility and continuous delivery, it became evident that security needed a more prominent role in the development lifecycle. Thus, the evolution from DevOps to DevSecOps emerged, emphasizing the integration of security at every stage of the software development process.

DevOps: A Collaborative Journey

DevOps, a fusion of “development” and “operations,” aimed to break down the silos between these traditionally separate functions. Its core principles focused on:

1. Collaboration: Fostering open communication and collaboration between development, operations, and other stakeholders.
2. Automation: Streamlining processes through automation to achieve faster and more reliable software delivery.
3. Continuous Integration/Continuous Deployment (CI/CD): Implementing CI/CD pipelines to automate testing, integration, and deployment for swift and reliable releases.
4. Monitoring and Feedback: Utilizing real-time monitoring and feedback loops to identify and address issues promptly.

The Security Gap: Catalyst for DevSecOps

While DevOps succeeded in accelerating software delivery, it often treated security as an afterthought. Security was traditionally a distinct phase in the software development lifecycle and integrating it seamlessly into the rapid and iterative nature of DevOps became imperative. This realization led to the evolution of DevSecOps.

Approaches to DevSecOps:

1. Shift Left: DevSecOps advocates for “shifting left,” integrating security practices early in the software development lifecycle. This proactive approach ensures vulnerabilities are identified and addressed at the inception of the development process, optimizing efficiency and cost-effectiveness. 
2. Automation of Security Testing: Automated security testing, including static application security testing (SAST), dynamic application security testing (DAST), and interactive application security testing (IAST), becomes foundational in DevSecOps. These measures seamlessly embed security checks into the CI/CD pipeline. 
3. Culture of Shared Responsibility: DevSecOps fosters a culture where security is not the sole responsibility of a dedicated team but a shared responsibility across development, operations, and security teams. This cultural shift ensures security is a collective concern from code inception to deployment.

Principles of DevSecOps:

1. Continuous Security Monitoring: Continuous security monitoring tools are implemented to detect and respond to threats in real-time, ensuring the application’s security posture is continuously assessed. 
2. Security as Code: Treating security configurations, policies, and controls as code allows for automation and consistency in deploying and managing security measures across the entire infrastructure. 
3. Identity and Access Management (IAM): Robust IAM practices are implemented to control and manage user access, reducing the risk of unauthorized activities and potential security breaches. 
4. Incident Response and Recovery: Development and regular testing of incident response plans ensure a rapid and effective response in the event of a security incident, minimizing its impact on the organization. 

Tooling and Technologies in DevSecOps

The success of DevSecOps relies on a robust set of tools and technologies designed to enhance security practices throughout the development lifecycle. Some key areas include: 

1. Static Application Security Testing (SAST): SAST tools analyze source code for security vulnerabilities before the application is compiled. This ensures early detection and mitigation of potential threats. 
2. Dynamic Application Security Testing (DAST): DAST tools evaluate the security of a running application by simulating real-world attacks. This provides insights into vulnerabilities that may arise during runtime. 
3. Container Security: With the rise of containerization, DevSecOps incorporates tools that scan container images for vulnerabilities, ensuring secure deployment in containerized environments. 
4. Security Information and Event Management (SIEM): SIEM tools enable continuous security monitoring by aggregating and analyzing log data from various sources, helping identify and respond to security incidents.

Training and Skill Development in DevSecOps

To fully leverage the benefits of DevSecOps, organizations must invest in training and upskilling their teams. Key aspects of training include: 

1. Security Awareness Programs: Educate teams on security best practices, common vulnerabilities, and the importance of integrating security into every stage of the development process. 
2. Certifications in DevSecOps: Encourage team members to pursue certifications in DevSecOps, such as Certified DevSecOps Professional (CDSOP), to validate their skills and knowledge.

Collaboration of DevSecOps with Other Practices

DevSecOps doesn’t operate in isolation; it synergizes with other practices to create a holistic approach to software development. The collaboration includes: 

1. Integration with DevOps: DevSecOps aligns seamlessly with DevOps principles, ensuring a unified approach to development, operations, and security. 
2. Agile and DevSecOps Harmony: Agile methodologies and DevSecOps work hand in hand, promoting iterative development with a security-first mindset. 
3. Cybersecurity in DevSecOps: DevSecOps integrates with broader cybersecurity practices, ensuring compliance with industry regulations and standards.

Industry Trends and Future Outlook for DevSecOps

The landscape of DevSecOps is continually evolving. Current trends and future predictions include: 

1. Rise of Cloud-Native Security: As organizations embrace cloud-native architectures, DevSecOps adapts to secure applications and infrastructure in cloud environments. 
2. AI and Machine Learning in Security: The incorporation of AI and machine learning enhances security measures, providing predictive analytics and automated threat detection. 
3. DevSecOps in IoT and Edge Computing: With the expansion of IoT and edge computing, DevSecOps becomes crucial in securing applications and devices at the network’s edge.

Conclusion: Nurturing a Secure and Agile Future

The journey from DevOps to DevSecOps represents a paradigm shift in how organizations approach software development and security. By embedding security into the development process, DevSecOps aligns with the principles of agility, collaboration, and automation, ensuring applications are resilient against ever-evolving cyber threats. 

In a landscape where security breaches can have severe consequences, adopting a DevSecOps mindset is not just a choice but a necessity. The integration of security into the DevOps pipeline enhances the overall security posture, contributing to a more robust and resilient software development lifecycle. Organizations embracing DevSecOps embark on a journey toward a secure and agile future, where security is not an add-on but an integral part of every development endeavor. 

As we navigate the dynamic realm of technology, DevSecOps stands as a testament to the industry’s commitment to delivering innovative solutions without compromising on security. This evolution represents not just a methodology but a cultural shift towards a future where every line of code is written with security in mind.

Serverless architecture is a revolutionary approach that has gained significant hype in recent years. As a beginner, it can be challenging to understand the ins and outs of this technology and its potential benefits. In this comprehensive guide, we will unravel the mysteries of serverless architecture, exploring its fundamental concepts, real-world examples, case studies, best practices, essential tools, and valuable resources.

Understanding Serverless Architecture: The Basics

Contrary to the name, serverless architecture does not mean there are no servers involved. Instead, it refers to a cloud computing model where developers can focus on writing code without the need to manage the underlying infrastructure.

Benefits of Serverless Architecture:

1. Cost-Efficiency: You only pay for the resources your code consumes during execution, eliminating the need for idle server capacity.
2. Scalability: Serverless platforms automatically scale applications based on demand, ensuring optimal performance even during traffic spikes.
3. Developer Productivity: With serverless, developers can focus on writing code and deploying features quickly, reducing the time spent on infrastructure management.

Real-World Examples and Case Studies:

1. AWS Lambda: Amazon’s serverless compute service has enabled numerous applications to achieve greater efficiency and cost savings. For instance, Coca-Cola’s serverless-powered vending machines significantly reduced operational costs and improved inventory management.
2. Azure Functions: Microsoft’s serverless platform is widely used for event-driven applications. A prominent example is Siemens, which leverages Azure Functions to process and analyse sensor data from industrial equipment in real-time.

Best Practices for Serverless Architecture:

1. Microservices and Function Design: Break down applications into smaller, manageable functions that follow the microservices architecture. Each function should have a specific purpose and be designed to perform a single task.
2. Optimize Cold Starts: Serverless functions may experience a slight delay (cold start) when triggered for the first time. Minimize this latency by using language-specific techniques and adjusting memory allocation.

Essential Tools for Serverless Development:

1. Serverless Framework: The Serverless Framework is a powerful open-source tool that simplifies the development, deployment, and management of serverless applications across various cloud providers. It is designed to streamline the serverless development workflow, allowing developers to focus on writing code rather than dealing with the complexities of infrastructure setup and management.
   Key Features:
   1. Cross-Cloud Compatibility: The Serverless Framework is cloud-agnostic, meaning it supports multiple cloud providers, including AWS, Azure, Google Cloud, and more. This flexibility allows developers to deploy their serverless applications to different environments without vendor lock-in.
   2. Easy Deployment: With a simple command-line interface (CLI), developers can easily deploy their serverless functions and resources to the cloud. The framework takes care of the necessary configurations and infrastructure provisioning.
   3. Local Development: The framework provides a local development environment that allows developers to test their serverless functions locally before deploying them to the cloud. This speeds up the development cycle and facilitates efficient debugging.
   4. Plugin System: The Serverless Framework supports a wide range of plugins that extend its functionality. These plugins enable developers to integrate with databases, third-party services, and other cloud resources seamlessly.
   Example: Suppose you want to create a serverless application that processes and stores user data in an AWS DynamoDB table. Using the Serverless Framework, you can define your Lambda functions, the DynamoDB table, and the necessary permissions in a simple configuration file (serverless.yml). Then, by running a single command, the framework will deploy all the resources to AWS, making your application live and ready to handle requests.
2. AWS SAM (Serverless Application Model): AWS SAM is a framework that extends AWS CloudFormation, the infrastructure-as-code service provided by Amazon Web Services (AWS). It provides a simplified and declarative way to define serverless applications using YAML or JSON templates. By leveraging SAM, developers can define their serverless resources and their corresponding event sources in a more concise and intuitive manner.
   Key Features:
   1. Higher-Level Abstractions: SAM introduces higher-level abstractions for commonly used AWS resources, such as Lambda functions, API Gateway endpoints, and DynamoDB tables. This abstraction reduces the boilerplate code and simplifies the application definition.
   2. Local Testing: Similar to the Serverless Framework, AWS SAM also supports local testing of serverless functions, enabling developers to test their application logic locally using the AWS SAM CLI.
   3. Integration with AWS Services: SAM seamlessly integrates with other AWS services, making it easier to define event sources for Lambda functions. For example, you can define an API Gateway endpoint or an S3 bucket as an event source directly in the SAM template.
   4. Support for AWS Lambda Layers: SAM supports AWS Lambda Layers, allowing developers to share code and dependencies across multiple functions in a more modular and efficient way.
   Example: Let’s say you want to create an AWS Lambda function that is triggered by an API Gateway endpoint. Using AWS SAM, you can define the Lambda function, the API Gateway endpoint, and their relationship in a SAM template (template.yaml). This template abstracts the underlying CloudFormation resources and simplifies the process of deploying the serverless application to AWS.

The cloud provider takes care of server provisioning, scaling, and maintenance, allowing developers to focus solely on building applications.

Serverless architecture presents an exciting paradigm shift in application development, providing benefits like cost-efficiency, scalability, and enhanced productivity. As a beginner, understanding its fundamentals, exploring real-world examples, and following best practices will set you on the path to becoming a proficient serverless developer. Both the Serverless Framework and AWS SAM that we have talked about in this blog are invaluable tools for serverless development, offering simplified workflows, cross-cloud compatibility, and efficient deployment options. As you dive into serverless development, leveraging these tools will significantly accelerate your development process and allow you to focus on building innovative applications without getting bogged down by infrastructure management complexities.

Today’s dynamic digital landscape has prompted organizations to prioritize the optimization of their cloud infrastructure, unlocking the potential of agility, flexibility, and resilience. To meet this demand, many businesses are adopting multi-cloud and hybrid cloud strategies. This blog dives deep into the benefits and challenges associated with these approaches, delves into critical considerations for workload placement, data synchronization, and application portability across multiple cloud providers, and showcases real-life case studies of successful multi-cloud and hybrid cloud implementations. By exploring statistics, technical insights, and practical scenarios, we aim to provide comprehensive guidance for leveraging these strategies effectively.

Exploring the Benefits of Multi-Cloud and Hybrid Cloud Strategies:

1. Unmatched Flexibility: According to a survey by Flexera, 93% of organizations have a multi-cloud strategy in place. Adopting a multi-cloud approach allows businesses to cherry-pick services and capabilities from various providers, tailoring the infrastructure to meet specific workload requirements. For instance, utilizing Amazon Web Services (AWS) for compute-intensive workloads, Microsoft Azure for data analytics, and Google Cloud for machine learning enables organizations to leverage the strengths of each provider.
2. Mitigating Vendor Lock-In: One of the primary advantages of multi-cloud and hybrid cloud strategies is avoiding vendor lock-in. By distributing workloads across different providers, organizations can negotiate better terms, costs, and support agreements. This approach empowers businesses to maintain control over their cloud ecosystem and switch providers if needed, fostering a healthy competitive environment.
3. Enhanced Resilience and Redundancy: A study conducted by LogicMonitor reveals that 41% of organizations have experienced a public cloud outage. Employing a multi-cloud or hybrid cloud approach enhances disaster recovery and business continuity capabilities. In the event of an outage with one cloud provider, applications and data seamlessly failover to alternate providers, minimizing service disruptions and ensuring continuous operations.
4. Geographic Optimization and Latency Reduction: For businesses catering to a global audience, multi-cloud and hybrid cloud strategies offer the advantage of geographic optimization. Deploying resources closer to end-users or specific regions minimizes latency and improves performance. This is particularly crucial for real-time applications such as video streaming, gaming, or financial transactions.
5. Cost Optimization through Competitive Pricing: A study by Flexera indicates that optimizing cloud costs is the top priority for 58% of organizations. Embracing multi-cloud strategies enables businesses to take advantage of competitive pricing models and leverage specific offerings from different cloud providers. This approach allows organizations to optimize costs by selecting the most cost-effective services for each workload.

Challenges of Multi-Cloud and Hybrid Cloud Strategies:

1. Complexity and Management Overhead: Managing multiple cloud providers and ensuring consistent governance, security, and compliance across environments can introduce complexity and increase management overhead. Organizations must adopt robust cloud management platforms or tools to streamline operations and effectively monitor and govern their multi-cloud environments.
2. Interoperability and Data Synchronization: Achieving seamless data synchronization and interoperability across multiple cloud platforms requires careful planning and integration efforts. Organizations must establish data replication frameworks, utilize cloud-native data synchronization tools, or employ third-party solutions to ensure data consistency, security, and compliance throughout the hybrid or multi-cloud architecture.
3. Skill Set Requirements: Managing multiple cloud providers demands additional expertise and resources. Organizations must invest in upskilling their workforce or consider partnering with managed service providers (MSPs) with expertise across multiple cloud ecosystems. Ensuring a skilled and knowledgeable team is crucial for efficient management, optimization, and troubleshooting within a multi-cloud or hybrid cloud environment.
4. Governance and Compliance: Establishing robust governance frameworks is essential to manage security, compliance, and data privacy across all cloud environments consistently. Organizations must enforce standardized security measures, access controls, and compliance policies to maintain data integrity and regulatory adherence.
5. Effective Vendor Management: Engaging with multiple cloud vendors requires efficient vendor management to handle relationships, contracts, and support agreements effectively. Organizations should establish clear communication channels, robust service-level agreements (SLAs), and regularly assess vendor performance to ensure alignment with business objectives.

Considerations for Workload Placement, Data Synchronization, and Application Portability:

1. Workload Placement: Evaluate the characteristics and requirements of each workload or application to determine the most suitable cloud environment. Factors such as performance, compliance, security, scalability, and cost should be considered when selecting the appropriate cloud provider.
2. Data Synchronization and Integration: Implement robust data synchronization mechanisms and integration frameworks to ensure seamless data flow across multiple cloud providers. Leverage cloud-native tools like AWS DataSync, Azure Data Factory, or Google Cloud Dataflow, or consider utilizing middleware solutions like Apache Kafka or MuleSoft for data integration.
3. Application Portability: Design applications with portability in mind, utilizing containerization technologies such as Docker or Kubernetes. Containers encapsulate applications and their dependencies, enabling consistent execution across multiple cloud providers. Adopting cloud-agnostic architectures and utilizing infrastructure-as-code (IaC) frameworks like Terraform or AWS CloudFormation further enhances application portability.
4. Security and Compliance: Implement a unified security approach across all cloud environments, encompassing identity and access management (IAM), encryption, network security, and regulatory compliance measures. Leverage cloud-native security services such as AWS Identity and Access Management (IAM), Azure Active Directory, or Google Cloud IAM for centralized security management.
5. Monitoring and Management: Deploy comprehensive monitoring and management solutions that provide visibility into all cloud environments. Utilize cloud-native monitoring tools like AWS CloudWatch, Azure Monitor, or Google Cloud Operations Suite for centralized monitoring, reporting, and troubleshooting. Adopting a unified dashboard or a cloud management platform can provide a holistic view of the entire multi-cloud or hybrid cloud infrastructure.

Case Studies: Successful Multi-Cloud and Hybrid Cloud Implementations

1. Netflix: A pioneer of the multi-cloud approach, Netflix relies on a combination of AWS, Google Cloud, and their own Open Connect CDN for seamless streaming services. This strategy ensures scalability, resilience, and global coverage to deliver a high-quality streaming experience.
2. Maersk: The global shipping company Maersk implemented a hybrid cloud architecture, utilizing a mix of on-premises infrastructure and Microsoft Azure. This approach enabled them to efficiently manage their complex supply chain operations, benefiting from the scalability of the cloud while keeping sensitive data and critical applications within their own infrastructure.
3. Zynga: The gaming company Zynga adopted a multi-cloud strategy, leveraging AWS, Google Cloud, and their private data centers. By distributing their game workloads across different cloud providers, Zynga optimized costs, achieved high availability, and scaled resources based on player demand.

While challenges exist, thoughtful consideration of workload placement, data synchronization, application portability, and effective management can ensure successful implementations. By analyzing real-life case studies and incorporating technical insights, organizations can harness the power of multi-cloud and hybrid cloud strategies to optimize costs, enhance performance, and propel their businesses forward in this era of digital transformation. 

Resources:

1. Flexera 2021 State of the Cloud Report – https://www.flexera.com/about-us/press-center/flexera-releases-2021-state-of-the-cloud-report.html
2. LogicMonitor – Outage Impact Report – https://www.logicmonitor.com/resource/state-of-it-ops-report-2021
3. AWS DataSync – https://aws.amazon.com/datasync/
4. Azure Data Factory – https://azure.microsoft.com/services/data-factory/
5. Google Cloud Dataflow – https://cloud.google.com/dataflow
6. Netflix Tech Blog – https://netflixtechblog.com/
7. Microsoft Azure Case Studies – https://azure.microsoft.com/case-studies/
8. AWS Case Studies – https://aws.amazon.com/solutions/case-studies/
9. Google Cloud Customer Success Stories – https://cloud.google.com/customers/success-stories

The world of cloud computing is rapidly evolving, and optimizing costs has become a critical focus for businesses seeking to maximize their return on investment. As organizations increasingly migrate to the cloud, understanding and implementing effective cost optimization techniques are paramount.

This comprehensive blog post delves into various strategies and techniques for cloud cost optimization, empowering businesses to achieve greater cost efficiency while aligning with their overall business objectives.

Techniques for Optimizing Cloud Costs:

1. Resource Monitoring: Gain granular visibility into cloud resource usage and performance through advanced monitoring tools and services. This allows organizations to identify areas of inefficiency and cost waste, empowering them to take targeted cost optimization actions. For example, by identifying idle instances or underutilized storage volumes, businesses can make informed decisions on resource consolidation or termination.
2. Rightsizing: Analyse and adjust resource allocation to match actual workload requirements. Rightsizing involves finding the optimal balance between performance and cost by identifying overprovisioned resources and downsizing them to eliminate wasted capacity. Conversely, it also involves identifying resource-constrained instances and scaling them up to avoid performance bottlenecks.
3. Cost Allocation and Accountability: Implement robust mechanisms for tracking and allocating costs to individual projects, departments, or teams. By assigning costs to specific stakeholders, businesses create transparency and promote cost-conscious behaviour. This accountability drives efficient resource utilization and encourages teams to optimize their cloud spend. Tools like cost tagging and showback reports help identify cost centres and enable accurate cost allocation.

Leveraging Cloud-Native Tools and Services:

1. Automation: Leverage cloud-native automation tools and services to optimize cost efficiency. For example, auto-scaling enables automatic adjustment of resources based on workload demands, ensuring that businesses only pay for the resources they need. Additionally, serverless computing platforms, such as AWS Lambda or Azure Functions, allow organizations to execute code without provisioning or managing servers, resulting in significant cost savings by paying only for actual usage.
2. Containerization: Implement containerization technologies like Docker and Kubernetes to optimize resource utilization and enable efficient scaling. By encapsulating applications and their dependencies into containers, businesses achieve higher density, reducing the number of resources required. Container orchestration platforms further optimize resource allocation by dynamically scaling containers based on demand, resulting in improved cost efficiency.

Strategies for Forecasting and Budgeting Cloud Costs:

1. Usage Analysis and Trending: Analyse historical usage data and trends to forecast future resource requirements and anticipate cost fluctuations. By understanding usage patterns, organizations can make informed decisions about scaling resources and avoid unexpected cost escalations. Leveraging advanced analytics tools and machine learning algorithms can provide more accurate predictions for resource consumption.
2. Budgeting and Cost Controls: Establish cloud cost budgets and implement cost control mechanisms to prevent unforeseen expenditure. Cloud providers offer features like budget alerts and spending caps, allowing businesses to set thresholds and receive notifications when costs approach or exceed predefined limits. This empowers organizations to take proactive measures and optimize resource usage to stay within budgetary constraints.
3. Reserved Instances and Savings Plans: Take advantage of cloud providers’ offerings, such as reserved instances or savings plans, to secure discounted pricing for long-term resource commitments. By committing to reserved instances or savings plans, businesses can significantly reduce their cloud costs, especially for stable workloads. This strategy enables better predictability and long-term cost optimization.

Case Studies:

1. Netflix: Netflix, the popular streaming platform, is known for its expertise in cloud cost optimization. They have implemented several strategies to optimize their cloud costs and achieve significant savings. By leveraging cloud-native tools and services, Netflix dynamically scales their infrastructure based on demand, ensuring efficient resource utilization.
   One of their successful cost optimization initiatives is the use of spot instances on AWS. These instances are acquired at significantly lower prices but with the risk of termination. Netflix’s architecture is designed to handle instance failures gracefully, allowing them to take advantage of spot instances without compromising service availability. By leveraging spot instances, they saved up to 90% on their infrastructure costs compared to on-demand instances.
2. Airbnb: Airbnb, the online marketplace for vacation rentals, leverages cloud cost optimization techniques to manage their cloud infrastructure efficiently. They prioritize resource efficiency and cost-consciousness while ensuring scalability and reliability.
   Airbnb uses a combination of auto-scaling, reserved instances, and spot instances to optimize their cloud costs. By dynamically adjusting their resource allocation based on demand fluctuations, they optimize cost efficiency without sacrificing performance. Additionally, they continuously monitor their infrastructure to identify any areas of inefficiency and take necessary actions to optimize costs. By using a combination of auto-scaling, reserved instances, and spot instances, they achieved a 40% reduction in their infrastructure costs.
3. Lyft: Lyft, the ride-sharing company, operates at a massive scale, relying on the cloud to power their platform. They have adopted various cost optimization strategies to optimize their cloud spending and improve their bottom line.
   One notable approach Lyft implemented is rightsizing their EC2 instances. Through continuous monitoring and analysis of their workloads, they identified instances with excessive capacity and downsized them to match the actual workload requirements. By downsizing oversized instances, they reduced costs by 50% while maintaining optimal performance and availability. This rightsizing initiative resulted in substantial cost savings for Lyft.
4. Pinterest: Pinterest, the popular visual discovery platform, focuses on cost optimization to ensure sustainable growth. They have implemented various techniques to optimize their cloud costs while maintaining a high-quality user experience.
   Pinterest employs automation and infrastructure scaling based on demand patterns to optimize resource allocation and minimize costs. By leveraging AWS Lambda for serverless computing, they achieved a 70% reduction in their compute costs. This serverless approach enabled them to pay only for actual resource consumption during function execution, resulting in substantial cost savings.

Cloud cost optimization is an ongoing journey that requires proactive strategies, advanced tools, and the right expertise.

Forecasting, budgeting, and leveraging reserved instances further optimize cost efficiency. With industry examples, case studies, and guidance from thought leaders, organizations can unlock the full potential of cloud cost optimization, redirecting savings towards innovation, growth, and achieving strategic goals.

Remember, cloud cost optimization is a continuous practice that evolves alongside your cloud environment and business needs. Embrace these strategies, stay vigilant, and leverage the wealth of resources available to ensure long-term cost efficiency and success in the cloud.

References: To further deepen your understanding of cloud cost optimization, we recommend exploring the following resources:

1. “Cloud Financial Management Guide” by AWS (Amazon Web Services)
2. “Optimizing Your Cloud Costs” by GCP (Google Cloud Platform)
3. “Cloud Cost Optimization: Strategies for Success” by FinOps Foundation
4. “Cloud Cost Optimization: Strategies for Success” by Jeff Collins, Cloud Architecture Manager at Google Cloud
5. “Cloud Cost Management and Optimization” by Corey Sanders, Corporate Vice President at Microsoft Azure
6. “Realizing the Promise of Cloud Cost Optimization” by J.R. Storment, Co-Founder of Cloudability
7. “Cloud Cost Optimization: Best Practices and Case Studies” by James Beswick, Senior Developer Advocate at AWS