Serverless architectures let you build and deploy applications and services with infrastructure resources that require zero administration. In this session, you will learn about serverless architectures, their benefits, and the basics of the AWS’s serverless stack (e.g., AWS Lambda, Cognito, Amazon API Gateway, and AWS Step Functions). We will discuss how to use serverless architectures for a variety of use cases including data processing, website backends, serverless applications, and “operational glue.” You will also get practical tips and tricks, best practices, and architecture patterns that you can take back and implement immediately.
Eduardo Patino, LATAM Solutions Architect, Amazon Web Services
Mauricio Romero Escobar, IT Consultant, SAT Guatemala
Web application pattern - this is one of the common use case where customers are leveraging Serverless services. These are some of the key aspects of typical web application. And we want to build a reference architecture using Serverless services with these key characteristics in mind.
There is static content, images, java scripts, css, etc being reneeded in your website, your application users might be seeking dynamic content, and you might have created a bunch of microservices! Of course, You want to provide a way for your users to authn and authz. And all of this while being able to scale and globally present!
Combining Lambda and Kinesis is a popular architecture model for transmission applications, there are a couple of different components for Kinesis. Today, you can use two Kinesis firehose transmissions, you can use analysis and lambda can interact with all those, but mainly what we see customers do is ingest data and then have to do some sort of calculation almost in real-time against that data, so in this case we have a Kinesis flow coming and that information is being processed by Lambda. One of my Lambda functions will take that information. and put it on S3, the others might process that data and extract some metadata about something that went into Dynamo and maybe enter some data into the observation logs of the cloud. Maybe I'm processing an event flow from one of my own products. Many different things can be done in this space and this is again one of the largest use cases for service applications today.
Bustle.com 52 million monthly users, and 100 million daily events.
___________________________
Combing Lambda and Kinesis is a popular architecture model for streaming applications, there's a couple different components to Kinesis today you can use two Kinesis firehose streams can use analytics and actually lambda can interface with all of those but primarily what we're seeing customers do ingesting data and then they need to do some sort of near real-time compute against that data and if in this case we have a kenisha stream coming in that data is then being processed by lambda one of my lambda functions is going to take that data and put it in s3 the other ones gonna maybe that data and pull out some metadata about something put into dynamo and maybe put some data into cloud watch logs maybe i'm processing an event stream from one of my own products and so there's a lot of different things that you can do in this space and this is again one of the biggest use cases for service applications today.
Bustle.com 52 million monthly users, and 100 million events daily.
Keypoints
SAT is Guatemala's entity in charge of internal taxes and customs, created 20 years ago from a technology-based strategy.
Income tax returns and import and export declarations at 100%.
But FEL has been the greatest technological challenge for the SAT.
Complete modernization of the strategy for this process.
A team with mixed experience, business, current systems and cloud technologies was appointed.
Through concept tests, a new architecture was developed, agnostic to the cloud or premises.
Strong use of opensource for this, going through the process of installing servers for technologies such as Kafka.
Comment that although it goes unnoticed, infrastructure services used reduce many costs of specialization and management.
All the logic for reception and validation of FEL is serverless.
Briefly comment each case, in particular commenting on how triggers operate between services, for example, how to trigger from on-premise we send a json to S3 that triggers a Lambda that loads information from taxpayers to redis, or how some of the public query APIs work via API Gateway - Lambda - DynamoDB - S3 (complete serverless)