Category: Amazon Managed Service for Apache Flink

In this post, you’ll learn how to use Terraform to automate and streamline your Apache Flink application lifecycle management on Amazon Managed Service for Apache Flink. We’ll walk you through the complete lifecycle including deployment, updates, scaling, and troubleshooting common issues. This post builds upon our two-part blog series “Deep dive into the Amazon Managed Service for Apache Flink application lifecycle”.

This post was originally published in March 2024 and updated in February 2026. Stream data processing allows you to act on data in real time. Real-time data analytics can help you have on-time and optimized responses while improving overall customer experience. Apache Flink is a distributed computation framework that allows for stateful real-time data processing. It […]

Encryption of both data at rest and in transit is a non-negotiable feature for most organizations. Furthermore, organizations operating in highly regulated and security-sensitive environments—such as those in the financial sector—often require full control over the cryptographic keys used for their workloads. Amazon Managed Service for Apache Flink makes it straightforward to process real-time data […]

In Part 1 of this series, we discussed fundamental operations to control the lifecycle of your Amazon Managed Service for Apache Flink application. In this post, we explore failure scenarios that can happen during normal operations or when you deploy a change or scale the application, and how to monitor operations to detect and recover when something goes wrong.

In this two-part series, we explore what happens during an application’s lifecycle. This post covers core concepts and the application workflow during normal operations. In Part 2, we look at potential failures, how to detect them through monitoring, and ways to quickly resolve issues when they occur.

In this post, we describe Nexthink’s journey as they implemented a new real-time alerting system using Amazon Managed Service for Apache Flink. We explore the architecture, the rationale behind key technology choices, and the Amazon Web Services (AWS) services that enabled a scalable and efficient solution.

In this post, we present Riskified’s journey toward enabling self-service streaming SQL pipelines. We walk through the motivations behind the shift from Confluent ksqlDB to Apache Flink, the architecture Riskified built using Amazon Managed Service for Apache Flink, the technical challenges they faced, and the solutions that helped them make streaming accessible, scalable, and production-ready.

In this post, we explain how the new connector works. We also show how you can manage your Prometheus metrics data cardinality by preprocessing raw data with Flink to build real-time observability with Amazon Managed Service for Prometheus and Amazon Managed Grafana.

In this post, we explore how AWS customers can extend Amazon DataZone to support streaming data such as Amazon Managed Streaming for Apache Kafka (Amazon MSK) topics. Developers and DevOps managers can use Amazon MSK, a popular streaming data service, to run Kafka applications and Kafka Connect connectors on AWS without becoming experts in operating it.

This post discusses strategies for handling errors in Apache Flink applications. However, the general principles discussed here apply to stream processing applications at large.