All-in-one serverless DevOps platform.
Full-stack visibility across the entire stack.
Detect and resolve incidents in record time.
Conform to industry best practices.
Dashbird continuously monitors and analyses your serverless applications to ensure reliability, cost and performance optimisation and alignment with the Well Architected Framework.
What defines a serverless system, main characteristics and how it operates
What are the types of serverless systems for computing, storage, queue processing, etc.
What are the challenges of serverless infrastructures and how to overcome them?
How systems can be reliable and the importance to cloud applications
What is a scalable system and how to handle increasing loads
Making systems easy to operate, manage and evolve
Learn the three basic concepts to build scalable and maintainable applications on serverless backends
The pros and cons of each architecture and insights to choose the best option for your projects
Battle-tested serverless patterns to make sure your cloud architecture is ready to production use
Strategies to compose functions into flexible, scalable and maintainable systems
Achieving loosely-coupled architectures with the asynchronous messaging pattern
Using message queues to manage task processing asynchronously
Asynchronous message and task processing with Pub/Sub
A software pattern to control workflows and state transitions on complex processes
The strategy and practical considerations about AWS physical infrastructure
How cloud resources are identified across the AWS stack
What makes up a Lambda function?
What is AWS Lambda and how it works
Suitable use cases and advantages of using AWS Lambda
How much AWS Lambda costs, pricing model structure and how to save money on Lambda workloads
Learn the main pros/cons of AWS Lambda, and how to solve the FaaS development challenges
Main aspects of the Lambda architecture that impact application development
Quick guide for Lambda applications in Nodejs, Python, Ruby, Java, Go, C# / .NET
Different ways of invoking a Lambda function and integrating to other services
Building fault-tolerant serverless functions with AWS Lambda
Understand how Lambda scales and deals with concurrency
How to use Provisioned Concurrency to reduce function latency and improve overall performance
What are Lambda Layers and how to use them
What are cold starts, why they happen and what to do about them
Understand the Lambda retry mechanism and how functions should be designed
Managing AWS Lambda versions and aliases
How to best allocate resources and improve Lambda performance
What is DynamoDB, how it works and the main concepts of its data model
How much DynamoDB costs and its different pricing models
Query and Scan operations and how to access data on DynamoDB
Alternative indexing methods for flexible data access patterns
How to organize information and leverage DynamoDB features for advanced ways of accessing data
Different models for throughput capacity allocation and optimization in DynamoDB
Comparing NoSQL databases: DynamoDB and Mongo
Comparing managed database services: DynamoDB vs. Mongo Atlas
How does an API gateway work and what are some of the most common usecases
Learn what are the benefits or drawbacks of using APIGateway
Picking the correct one API Gateway service provider can be difficult
Types of possible errors in an AWS Lambda function and how to handle them
Best practices for what to log in an AWS Lambda function
How to log objects and classes from the Lambda application code
Program a proactive alerting system to stay on top of the serverless stack
Think of Finite-State Machine (also called Step Machine) as being a workflow or process with multiple steps that can be in a finite number of states at any given time.
One popular example is a vending-machine:
During each of these stages, the Step Machine may be programmed to react to different scenarios. For example: what happens if the customer orders a product the costs less than the amount of money entered? The machine should trigger a separate routine to return the change.
The FSM model is very robust and used in software applications throughout multiple industries and use cases.
Another practical example are traffic lights. Software controlling lights in a road intersection usually employ FSM techniques. It ensures that, when one way turns to green state, every other crossing way must have turned to the red state before.
It turns out FSM is well suited to address multiple challenges in modern cloud computing. It can help in orchestrating components of distributed, microservices architectures, for example, or for controlling data processing workflows involving ETL (Extract, Transform, Load) or Machine Learning tasks, for example.
Also many business use cases can benefit from FSM implementations. An e-commerce site, for example, can use FSM to control customer checkouts. This usually involves multiple steps, similarly to vending machines: process payment, validate order and inventory, decrement inventory availability for ordered items, send confirmation to the customer, notify fulfillment center, etc.
As in the vending-machine example, many things can go wrong in business process, such as an e-commerce checkout. FSM can help ensure these exceptional cases are going to be handled appropriately, contributing to a healthy and fault-tolerant system.
Although developing a custom implementation of a Finite-State Machine would not be a huge task, achieving scalable and rubust deployments in the cloud can be chalenging for large amounts of data and tasks.
Thankfully, there are managed FSM services available by the main cloud providers, such as AWS Step Functions.
These services usually charge per request, so there’s no fixed or startup cost involved. Even for small implementations, using a ready-to-start service such as Step Functions can be cheaper than implementing an in-house solution when considering the cost of hours of development.
There are multiple open source projects in many different programming languages available. For teams that choose for a custom implementation, this can be helpful to avoid reinventing the wheel.
Below are some projects we identified. This is not a recommendation or endorsement. Open source projects are subject to drastic changes and disruption and each developer/team should conduct its own analysis before deciding to use any:
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End-to-end observability and real-time error tracking for AWS applications.