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
AWS Step Functions is well suited for handling any kind of process or workflow in which multiple services need to be coordinated to accomplish a higher level task. Especially in cases where there is a need to control the flow of information and process execution based on rules and/or results from previous tasks.
Below we list some examples of workloads that would usually benefit from using Step Functions. It is not an extensive list, intended only for illustration purposes.
Extract, Transform and Load (ETL) jobs are becoming more and more common in many applications and companies. The rise of tools for data analytics and artificial intelligence has made it inevitable to deal with large amounts of data. Often, the data points require manipulation in multiple steps before being ready for analysis.
The same is valid for Machine Learning or Deep Learning tasks. They often involve processing pipelines in which a series of steps must be followed before reaching a prediction or classification result.
To solve these needs, Step Functions can be used in connection with other services, such as AWS Lambda or AWS Batch to run custom logic upon the data, as well as S3 and DynamoDB to store and retrieve information along the workflow processing.
Applications that implement a microservices architecture can benefit from Step Functions by simplifying the orchestration work. Higher level tasks can be composed from multiple services that are coordinated in a centralized, but decoupled manner.
Complex logic and rules can be applied to the workflow in order to account for exceptional cases. The retry mechanism embedded in Step Functions (and integration services such as AWS Lambda) makes it even easier to implement fault-tolerant microservices composition strategies.
Step Functions can be used to process multiple tasks in parallel, or to control the fan-out process of tasks that require a breakdown in order to be tackled in small steps.
Step Functions support logic such as for loops. When there is a list of datapoints to process in a similar, but independent fashion, this feature can be implemented. The same workflow can be designed for the entire list, but each item is processed separately and sequentially by Step Functions.
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End-to-end observability and real-time error tracking for AWS applications.