Long-form posts on technology, various musings, and a back catalog of posts over twenty plus years in chronological order.
In this blog post, we will explore approaches for connecting Stripe events received by your backend services to your frontend applications. We will delve into asynchronous, event-driven integrations that foster agility, responsiveness, and scalability across client-server interactions.
Amazon EventBridge now supports publishing events to AWS AppSync GraphQL APIs as native targets. The new integration enables builders to publish events easily to a wider variety of consumers and simplifies updating clients with near real-time data. You can use EventBridge and AWS AppSync to build resilient, subscription-based event-driven architectures across consumers.
Amazon VPC Lattice is a new, generally available application networking service that simplifies connectivity between services. Builders can connect, secure, and monitor services on instances, containers, or serverless compute in a simplified and consistent manner. VPC Lattice supports AWS Lambda functions as both a target and a consumer of services. This blog post explores how to incorporate VPC Lattice into your serverless workloads to simplify private access to HTTP-based APIs built with Lambda.
Serverless applications can lower the total cost of ownership (TCO) when compared to a server-based cloud execution model because it effectively shifts operational responsibilities such as managing servers to a cloud provider.
Today, we are excited to announce an all-new way to demonstrate your AWS Serverless knowledge and skills: a verifiable, digital badge. The new digital badge is aligned with our Serverless Learning Plan now available in AWS Skill Builder.
AWS re:Invent 2022 is only a few weeks away, featuring an exciting slate of sessions on Serverless and Application Integration. This post highlights many of the sessions we are hosting on Serverless and Application Integration. It groups sessions by theme to help you quickly find the sessions most interesting to you.
AWS AppSync is a managed GraphQL service that can enable real-time data through the use of GraphQL subscriptions. Subscriptions are implemented and managed between the client and backend in AppSync via WebSocket connections. Any data source exposed by an AppSync API can support subscriptions, including pre-integrated data sources such as Amazon DynamoDB and Amazon OpenSearch Service (successor to Amazon Elasticsearch Service) in addition to alternative data sources such as Amazon Neptune or Amazon QLDB.
Update to my earlier post on building AWS Lambda Layers for Ruby
Learning in Public - exploring Amazon API Gateway Lambda Authorizers.
Learning in Public - my notes on the types of Lambda invocations and related error handling.
This post is the second installment of a two-post series discussing how to integrate Amazon Quantum Ledger Database (QLDB) and AWS AppSync. This combination provides a versatile, GraphQL-powered API on top of the Amazon QLDB-managed ledger database. For information about connecting Amazon QLDB and AWS AppSync by building an AWS Lambda function and running a query, see Building a GraphQL interface to Amazon QLDB with AWS AppSync: Part 1.
Amazon Quantum Ledger Database (QLDB) is a purpose-built database for use cases that require an authoritative data source. Amazon QLDB maintains a complete, immutable history of all changes committed to the database (referred to as a ledger). Amazon QLDB fits well in finance, eCommerce, inventory, government, and numerous other applications. Pairing Amazon QLDB with services such as AWS AppSync allows you to safely expose data and that data’s history for mobile applications, websites, or a data lake. This post explores a reusable approach for integrating Amazon QLDB with AWS AppSync to power an example government use case.
Real world reaction to Forrest Brazeal's recent article on organizational impact to CI/CD pipelines.
Tagging can be one of the most useful mechanisms to understand and track resources in AWS. The linked guide provides a great overview of how to get started.
Quick post on how to start execution of an AWS Step Functions state machine from Java.
This post discusses an approach to securely storing and using API Keys for HTTP Data Sources by leveraging AWS AppSync Pipeline Resolvers.
A collection of AWS AppSync resolvers that can be used to directly invoke AWS services, including Secrets Manager, SQS, S3, and Step Functions.
In this post, we explore how AWS AppSync can utilize AWS Lambda to integrate with alternative data sources—in other words, those not directly integrated out-of-the-box with AWS AppSync. While we look specifically at Amazon ElastiCache and Amazon Neptune here, you could support other data sources via a similar approach (including forthcoming services such as Amazon QLDB and Amazon Timestream).
In this post, I will describe how you can leverage ElastiCache for Redis with cluster mode enabled to enhance reliability and availability with little change to your existing workload. Cluster Mode comes with the primary benefit of horizontal scaling up and down of your Redis cluster, with almost zero impact on the performance of the cluster, as I will demonstrate later. If you have ever encountered a Redis cluster that is over or under-provisioned or just want to better understand its inner workings, please read on.
AWS AppSync is a managed GraphQL service that enables developers to easily build data-driven mobile and web applications. Using a serverless backend, you can build a GraphQL API by connecting AWS AppSync to various data sources—including Amazon DynamoDB, AWS Lambda, and Amazon OpenSearch Service (successor to Amazon Elasticsearch Service). AWS AppSync added support for HTTP data sources in May 2018, which makes it easy to add legacy APIs to your GraphQL endpoints.
Most often, when we discuss Amazon ElastiCache, it is in the context of enhancing the performance of read-heavy database workloads. We update our applications to employ a read-through or write-through pattern to keep data in the cache fresh and ease the burden on the database. When used in this context, ElastiCache accelerates your high volume workloads by caching your data in memory, delivering sub-millisecond data retrieval performance. Additionally, Amazon ElastiCache for Redis improves the availability and fault tolerance of your workload via automatic failover in Multi-AZ configurations.
Message brokers can be used to solve a number of needs in enterprise architectures, including managing workload queues and broadcasting messages to a number of subscribers. Amazon MQ is a managed message broker service for Apache ActiveMQ that makes it easy to set up and operate message brokers in the cloud. This posts describes invoking AWS Lambda from queues and topics.
Amazon ElastiCache makes it easy to deploy and manage a highly available and scalable in-memory data store in the cloud. Among the open source in-memory engines available to you for use with ElastiCache is Redis, which added powerful geospatial capabilities in version 3.2. This post explores using these capabilities to build an app that locates nearby bike share stations in Chicago. You can easily extend the app to other cities or to use other geo datasets.