Monitoring platform for keeping systems up and running at all times.
Full stack visibility across the entire stack.
Detect and resolve any incident 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
In this video, we are covering Serverless architectural patterns within three categories:
This is the “Serverless Architectural Patterns” video. My name is Renato and I welcome you to our Serverless Well-Architected series. Our purpose is to give a short overview of each pattern and cover details in the next videos.
Let’s start with the Circuit Breaker, in the Availability category. When there is an internal service and requests start to fail or slow down. Separate storage keeps track of how many issues occurred in the last 60 seconds, for example.
When there are too many failures, communication with the service is shut down to avoid overloading it. The Serverless function short-circuits to respond to API calls with an error or it falls back to a secondary backup service.
After some time, when the service performance has improved the API resumes communication and normalizes the workflow.
Other Availability patterns we will cover in the next videos are:
Now moving to the Orchestration category, we have the Queue-based Load Leveling pattern. This pattern can help avoid an over-loading of components that don’t scale rapidly, such as Databases.
Suppose this process receives information from various clients and stores it in a database. The serverless function backend can scale very fast but the Database cannot.
Using a queue allows keeping the database load within a certain level, regardless of how many requests the Serverless function receives.
This solution is only viable when the scale mismatch is temporary, otherwise the queue can grow indefinitely and become a bottleneck.
Other Orchestration patterns we will cover in the next videos are:
Moving into the Authorization category, this is the Gatekeeper pattern. Every request coming through an API, for example, goes through an authorizer before reaching any underlying service. This authorizer Serverless function is called “the Gatekeeper”.
This function performs identify authentication and validates whether the client is authorized to access the underlying resources. Another Authorization pattern we will cover in the next videos is the Valet Key, which solves some issues with the Gatekeeper
This video was based on a literature review published recently by a team of researchers from Finland and Italy. Go check their work to learn more about the subject.
In the next videos we will cover all Serverless patterns and dive into more details about their implementation as well as pros and cons.
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