Category: Amazon Managed Service for Apache Flink

Data streaming enables generative AI to take advantage of real-time data and provide businesses with rapid insights. This post looks at how to integrate generative AI capabilities when implementing a streaming architecture on AWS using managed services such as Managed Service for Apache Flink and Amazon Kinesis Data Streams for processing streaming data and Amazon Bedrock to utilize generative AI capabilities. We include a reference architecture and a step-by-step guide on infrastructure setup and sample code for implementing the solution with the AWS Cloud Development Kit (AWS CDK). You can find the code to try it out yourself on the GitHub repo.

This post takes a step-by-step approach to showcase how you can use Retrieval Augmented Generation (RAG) to reference real-time tweets as a context for large language models (LLMs). RAG is the process of optimizing the output of an LLM so it references an authoritative knowledge base outside of its training data sources before generating a response. LLMs are trained on vast volumes of data and use billions of parameters to generate original output for tasks such as answering questions, translating languages, and completing sentences.

We’re thrilled to announce that AWS has been named a Leader in the IDC MarketScape: Worldwide Analytic Stream Processing Software 2024 Vendor Assessment (doc #US51053123, March 2024). We believe this recognition validates the power and performance of Apache Flink for real-time data processing, and how AWS is leading the way to help customers build and […]

Managed Service for Apache Flink is a fully managed, serverless experience in running Apache Flink applications, and now supports Apache Flink 1.18.1, the latest released version of Apache Flink at the time of writing. In this post, we explore in-place version upgrades, a new feature offered by Managed Service for Apache Flink. We provide guidance on getting started and offer detailed insights into the feature. Later, we deep dive into how the feature works and some sample use cases.

Krones provides breweries, beverage bottlers, and food producers all over the world with individual machines and complete production lines. This post shows how Krones built a streaming solution to monitor their lines, based on Amazon Kinesis and Amazon Managed Service for Apache Flink. These fully managed services reduce the complexity of building streaming applications with Apache Flink. Managed Service for Apache Flink manages the underlying Apache Flink components that provide durable application state, metrics, logs, and more, and Kinesis enables you to cost-effectively process streaming data at any scale.

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. FMs, as the name suggests, provide the foundation to build more specialized downstream applications, and are unique in their adaptability. They can perform a wide range of different tasks, such as natural language processing, classifying images, […]

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 […]

When running Apache Flink applications on Amazon Managed Service for Apache Flink, you have the unique benefit of taking advantage of its serverless nature. This means that cost-optimization exercises can happen at any time—they no longer need to happen in the planning phase. With Managed Service for Apache Flink, you can add and remove compute […]

Thousands of developers use Apache Flink to build streaming applications to transform and analyze data in real time. Apache Flink is an open source framework and engine for processing data streams. It’s highly available and scalable, delivering high throughput and low latency for the most demanding stream-processing applications. Monitoring and scaling your applications is critical […]

You can use several approaches to enrich your real-time data in Amazon Managed Service for Apache Flink depending on your use case and Apache Flink abstraction level. Each method has different effects on the throughput, network traffic, and CPU (or memory) utilization. For a general overview of data enrichment patterns, refer to Common streaming data enrichment patterns in Amazon Managed Service for Apache Flink. This post covers how you can implement data enrichment for real-time streaming events with Apache Flink and how you can optimize performance. To compare the performance of the enrichment patterns, we ran performance testing based on synthetic data. The result of this test is useful as a general reference. It’s important to note that the actual performance for your Flink workload will depend on various and different factors, such as API latency, throughput, size of the event, and cache hit ratio.