There's nothing more cloud-native than serverless. Its event-driven design allows an on-demand microconsumption of compute, network and storage resources. It lets developers easily run function without take care of infrastructure and at any scale. But this come with several drawbacks: 1. Compared to a IaaS design, the microsecond billing might be way more expensive than long-term commitment. 2. Due to it's on-demand nature, It's more complex to predict the pricing. Usage will depend of the demand and can hugely differ from one day to another. Especially if a large audience consume the service. Additionally, as a stateless design, serverless can easily be offloaded to another cloud and thus constitute a multi-cloud architecture.

In my experience, serverless really fits well for multiple enterprise and startup use cases. It also works well for certain industries and types of workloads that are more seasonal and/or fluctuating. The key with serverless is to ensure that good hygiene is implemented regarding selection of runtimes (as you mostly pay based on the cpu and memory usage), finops, SRE, manageability and overall teams skills. Often this is the space that has the most innovation from cloud service provider and hence needs the team to be forward looking and agile.

While I understand the merits of Serverless. there are few use cases where this can be expensive. A good amount of applications out there have a steady and predictable workload. These can be less cost-effective because serverless cloud computing providers charge for compute time. If an application has a steady workload, then it will be using compute time all the time, leading to higher costs. Another use case that wont work with serverless is for applications that require high performance or low latency like real-time trading applications. Serverless cloud computing functions are typically invoked on demand leading to slight delay before a function is executed. This delay can be a problem for applications that require high performance.

Serverless architecture is a cloud computing model where developers focus on writing code without managing servers. Services like AWS Lambda or Azure Functions automatically scale and run code in response to events. It's cost-effective, as you only pay for actual usage, and highly scalable, making it ideal for microservices, APIs, and event-driven applications. Developers can concentrate on code logic, while the cloud provider handles infrastructure, offering simplicity and efficiency in application development and deployment.

The true value of a Microservices architecture is through an organisational construct that promotes ‘autonomy’ and ‘independence’. Microservices then become the technical capabilities that combine together to manifest enterprise business capabilities owned and operated independently. True autonomy also means a platform that allows agile teams to independently manage all layers of the stacks within the enterprise provided guardrails.

Microservices Architecture is a modern approach to building software systems. In this model, applications are composed of small, independent services that communicate through APIs. Each service focuses on a specific function, allowing for flexibility, scalability, and ease of maintenance. This approach promotes agility, as teams can develop, deploy, and update services independently. However, it also demands robust communication and management tools to ensure seamless coordination and reliability across services.

Event-Driven Architecture (EDA) is a design paradigm that promotes loosely coupled systems through the exchange of events. In EDA, components communicate asynchronously, reacting to events they subscribe to. This approach enhances flexibility, scalability, and responsiveness in complex systems. It enables real-time processing, decoupling of services, and fault tolerance. EDA is ideal for scenarios like microservices, IoT, and real-time data processing, fostering adaptability and agility in modern software ecosystems.

Event driven architecture has two primary topologies: “broker topology” wherein components broadcast events to the entire system without any orchestrator. It provides the highest performance and scalability. Whereas in “mediator topology” there is a central orchestrator which controls workflow of events. It provides better control and error handling capabilities. You can also use a hybrid model and combine these two topologies.

Working as a data engineer, understand various cloud architectures is important and one of the most important one is Lambda architecture. It is used to process historical data in batches, while stream processing is used to process real-time data in streams. Lambda architecture combines batch and stream processing to merge a serving layer that provides a unified view of the data. Understanding and implementing Lambda architecture can help to effectively handle large volumes of data in cloud computing environments. It focuses on low-latency access to real-time information while ensuring accurate and reliable results. With its scalability and adaptability, data engineers can manage and process large volumes of data in cloud environments.

Lambda Architecture is a data processing framework designed to handle large-scale, real-time data processing. It combines batch processing and stream processing to provide a comprehensive solution for data analytics. Lambda Architecture is resilient and fault-tolerant. It can handle high data volumes and provides flexibility for various analytics use cases. However, maintaining and synchronizing both layers can be complex, and it may require handling data consistency and dealing with issues related to system complexity. Despite its complexities, Lambda Architecture is valuable for organizations requiring real-time data insights and historical analysis. It addresses the trade-off between low latency and accuracy in big data processing.

While I understand the merits of Serverless, there are few use cases where this can be expensive. A good amount of applications out there have a steady and predictable workload. These can be less cost-effective because serverless cloud computing providers charge for compute time. If an application has a steady workload, then it will be using compute time all the time, leading to higher costs. Another use case that wont work with serverless is for applications that require high performance or low latency like real-time trading applications. Serverless cloud computing functions are typically invoked on demand leading to slight delay before a function is executed. This delay can be a problem for applications that require high performance

Hexagonal Architecture, also known as Ports and Adapters, is a software design pattern that prioritizes the separation of concerns in application development. At its core, it advocates organizing the codebase into concentric hexagonal layers. The innermost layer represents the application's core logic, free from external dependencies. Surrounding it are the adapters, which bridge the core with external systems like databases, UI, or third-party services. This design encourages modularity, testability, and maintainability. It isolates business rules from technical details, making it easier to swap out or upgrade components without affecting the core functionality.

Additional innovating patterns worth exploring based on the nature of application are: 1. Peer-to-Peer (P2P) Architecture: This architecture eliminates the need of a central server while enabling decentralized, and direct communication between peers or nodes in a network. An example is Torrent, where users share files directly with one another without relying on central repository. 2. CQRS (Command Query Responsibility Segregation) Architecture: This type separates the read/query and write/command operations in a system, allowing for independent scaling and optimization. For example, in a stock broker application like Robinhood, command operations like placing orders and query operations like stock search les are handled separately.