Category: Amazon Managed Service for Apache Flink

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.

On November 11, 2024, the Apache Flink community released a new version of AWS services connectors, an AWS open source contribution. This new release, version 5.0.0, introduces a new source connector to read data from Amazon Kinesis Data Streams. In this post, we explain how the new features of this connector can improve performance and reliability of your Apache Flink application.

Recently, AWS introduced over 50 new capabilities across its streaming services, significantly enhancing performance, scale, and cost-efficiency. Some of these innovations have tripled performance, provided 20 times faster scaling, and reduced failure recovery times by up to 90%. We have made it nearly effortless for customers to bring real-time context to AI applications and lakehouses. In this post, we discuss the top six game changers that will redefine AWS streaming data.

This post demonstrates how to implement a dynamic rules engine using Amazon Managed Service for Apache Flink. Our implementation provides the ability to create dynamic rules that can be created and updated without the need to change or redeploy the underlying code or implementation of the rules engine itself. We discuss the architecture, the key services of the implementation, some implementation details that you can use to build your own rules engine, and an AWS Cloud Development Kit (AWS CDK) project to deploy this in your own account.