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
DynamoDB is a NoSQL serverless database provided by AWS. It follows a key-value store structure and adopts a distributed architecture for high availability and scalability.
As in any serverless system, there’s no infrastructure provisioning needed. Dynamo offers two capacity modes that can serve both highly variable and predictable workloads.
Data is organized in tables, which contains items. Each item contains a set of key-value pairs of attributes. There are two special types of attributes: the primary-key, which works similarly to an item ID, and the sort-key, which allows for ordering the items.
Dynamo supports secondary indexes. They can be used to reference and order items by different primary-key and sort-keys. It is a schema-less database, in which items can have different sets of attributes. This allows for sparse indexes: composed only of items that contain a particular attribute.
Table: as a collection that can hold a virtually infinite number of items, it may also have secondary indexes associated
Secondary Index: duplicates table items using a different primary-key and sort-key
Item: the most basic unit in Dynamo, it holds the data attributes structured in a JSON
Attribute: a key value pair that contains informational data-points about an item in the database table
Primary Key: a special form of attribute that is used to reference items, similarly to an item ID
Sort Key: another special form of attribute that is used to organize items in a different sorting order
Streams: a constant stream of state-changing operations executed against a table
Query: operation to retrieve a particular item (or set of items)
Scan: oepration to scan the entire table or a section of it
Filter: rules to apply after a query or scan has executed, but before results are returned to the requester
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End-to-end observability and real-time error tracking for AWS applications.