Author: Lorenzo Nicora

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 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.

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.

Apache Flink is an open source distributed processing engine, offering powerful programming interfaces for both stream and batch processing, with first-class support for stateful processing and event time semantics. Apache Flink supports multiple programming languages, Java, Python, Scala, SQL, and multiple APIs with different level of abstraction, which can be used interchangeably in the same […]

February 2024: This post was reviewed and updated for accuracy. This post is a continuation of a two-part series. In the first part, we delved into Apache Flink‘s internal mechanisms for checkpointing, in-flight data buffering, and handling backpressure. We covered these concepts in order to understand how buffer debloating and unaligned checkpoints allow us to […]

This post is the first of a two-part series regarding checkpointing mechanisms and in-flight data buffering. In this first part, we explain some of the fundamental Apache Flink internals and cover the buffer debloating feature. In the second part, we focus on unaligned checkpoints. Apache Flink is an open-source distributed engine for stateful processing over […]