Apache Flink on Steffen Hausmann

Making it Easier to Build Connectors with Apache Flink: Introducing the Async Sink

Wed, 23 Nov 2022 00:00:00 +0000

Apache Flink is a popular open source framework for stateful computations over data streams. It allows you to formulate queries that are continuously evaluated in near real time against an incoming stream of events. To persist derived insights from these queries in downstream systems, Apache Flink comes with a rich connector ecosystem that supports a wide range of sources and destinations. However, the existing connectors may not always be enough to support all conceivable use cases. Our customers and the community kept asking for more connectors and better integrations with various open source tools and services.

One Sink to Rule Them All: Introducing the New Async Sink

Wed, 03 Aug 2022 00:00:00 +0000

Next time you want to integrate with a new destination for a demo, concept or production application, the Async Sink framework will bootstrap development, allowing you to move quickly without compromise. In Flink 1.15 we introduced the Async Sink base (FLIP-171), with the goal to encapsulate common logic and allow developers to focus on the key integration code. The new framework handles things like request batching, buffering records, applying backpressure, retry strategies, and at least once semantics. It allows you to focus on your business logic, rather than spending time integrating with your downstream consumers. During the session we will dive deep into the internals to uncover how it works, why it was designed this way, and how to use it. We will code up a new sink from scratch and demonstrate how to quickly push data to a destination. At the end of this talk you will be ready to start implementing your own Flink sink using the new Async Sink framework.

Flink Improvement Proposal 171: Async Sink

Wed, 09 Jun 2021 00:00:00 +0000

Apache Flink has a rich connector ecosystem that can persist data in various destinations. Flink natively supports Apache Kafka, Amazon Kinesis Data Streams, Elasticsearch, HBase, and many more destinations. Additional connectors are maintained in Apache Bahir or directly on GitHub. The basic functionality of these sinks is quite similar. They batch events according to user defined buffering hints, sign requests and send them to the respective endpoint, retry unsuccessful or throttled requests, and participate in checkpointing. They primarily just differ in the way they interface with the destination. Yet, all the above-mentioned sinks are developed and maintained independently.

Building real-time applications using Apache Flink

Thu, 10 Dec 2020 00:00:00 +0000

Build real-time applications using Apache Flink with Apache Kafka and Amazon Kinesis Data Streams. Apache Flink is a framework and engine for building streaming applications for use cases such as real-time analytics and complex event processing. This session covers best practices for building low-latency applications with Apache Flink when reading data from either Amazon MSK or Amazon Kinesis Data Streams. It also covers best practices for running low-latency Apache Flink applications using Amazon Kinesis Data Analytics and discusses AWS’s open-source contributions to this use case.

Streaming ETL with Apache Flink and Amazon Kinesis Data Analytics

Fri, 21 Feb 2020 00:00:00 +0000

This post looks at how to use Apache Flink as a basis for sophisticated streaming extract-transform-load (ETL) pipelines. Apache Flink is a framework and distributed processing engine for processing data streams. AWS provides a fully managed service for Apache Flink through Amazon Kinesis Data Analytics, which enables you to build and run sophisticated streaming applications quickly, easily, and with low operational overhead.

https://aws.amazon.com/blogs/big-data/streaming-etl-with-apache-flink-and-amazon-kinesis-data-analytics/

Build real-time analytics for a ride-sharing app (ANT401)

Mon, 02 Dec 2019 00:00:00 +0000

In this session, we walk through how to perform real-time analytics on ride-sharing and taxi data, and we explore how to build a reliable, scalable, and highly available streaming architecture based on managed services. You learn how to deploy, operate, and scale an Apache Flink application with Amazon Kinesis Data Analytics for Java applications. Leave this workshop knowing how to build an end-to-end streaming analytics pipeline, starting with ingesting data into a Kinesis data stream, writing and deploying a Flink application to perform basic stream transformations and aggregations, and persisting the results to Amazon Elasticsearch Service to be visualized from Kibana.

Build and run streaming applications with Apache Flink and Amazon Kinesis Data Analytics (FF)

Tue, 08 Oct 2019 00:00:00 +0000

Stream processing facilitates the collection, processing, and analysis of real-time data and enables the continuous generation of insights and quick reactions to emerging situations. Yet, despite these advantages compared to traditional batch-oriented analytics applications, streaming applications are much more challenging to operate. Some of these challenges include the ability to provide and maintain low end-to-end latency, to seamlessly recover from failure, and to deal with a varying amount of throughput.

Streaming Analytics Workshop

Thu, 20 Jun 2019 00:00:00 +0000

In this workshop, you will build an end-to-end streaming architecture to ingest, analyze, and visualize streaming data in near real-time. You set out to improve the operations of a taxi company in New York City. You’ll analyze the telemetry data of a taxi fleet in New York City in near-real time to optimize their fleet operations.

You will not only learn how to deploy, operate, and scale an Apache Flink application with Kinesis Data Analytics for Java Applications, but also explore the basic concepts of Apache Flink and running Flink applications in a fully managed environment on AWS.v

Build and run streaming applications with Apache Flink and Amazon Kinesis Data Analytics

Tue, 16 Apr 2019 00:00:00 +0000

Stream processing facilitates the collection, processing, and analysis of real-time data and enables the continuous generation of insights and quick reactions to emerging situations. This capability is useful when the value of derived insights diminishes over time. Hence, the faster you can react to a detected situation, the more valuable the reaction is going to be. Consider, for instance, a streaming application that analyzes and blocks fraudulent credit card transactions while they occur. Compare that application to a traditional batch-oriented approach that identifies fraudulent transactions at the end of every business day and generates a nice report for you to read the next morning.

Amazon Kinesis Analytics Taxi Consumer

Fri, 15 Mar 2019 00:00:00 +0000

Sample Apache Flink application that can be deployed to Kinesis Analytics for Java. It reads taxi events from a Kinesis data stream, processes and aggregates them, and ingests the result to an Amazon Elasticsearch Service cluster for visualization with Kibana.

https://github.com/aws-samples/amazon-kinesis-analytics-taxi-consumer

Build a Real-time Stream Processing Pipeline with Apache Flink on AWS (FF)

Wed, 13 Sep 2017 00:00:00 +0000

The increasing number of available data sources in today’s application stacks created a demand to continuously capture and process data from various sources to quickly turn high volume streams of raw data into actionable insights. Apache Flink addresses many of the challenges faced in this domain as it’s specifically tailored to distributed computations over streams. While Flink provides all the necessary capabilities to process streaming data, provisioning and maintaining a Flink cluster still requires considerable effort and expertise. We will discuss how cloud services can remove most of the burden of running the clusters underlying your Flink jobs and explain how to build a real-time processing pipeline on top of AWS by integrating Flink with Amazon Kinesis and Amazon EMR. We will furthermore illustrate how to leverage the reliable, scalable, and elastic nature of the AWS cloud to effectively create and operate your real-time processing pipeline with little operational overhead.

Build a Real-time Stream Processing Pipeline with Apache Flink on AWS

Fri, 21 Apr 2017 00:00:00 +0000

In today’s business environments, data is generated in a continuous fashion by a steadily increasing number of diverse data sources. Therefore, the ability to continuously capture, store, and process this data to quickly turn high-volume streams of raw data into actionable insights has become a substantial competitive advantage for organizations.

Apache Flink is an open source project that is well-suited to form the basis of such a stream processing pipeline. It offers unique capabilities that are tailored to the continuous analysis of streaming data. However, building and maintaining a pipeline based on Flink often requires considerable expertise, in addition to physical resources and operational efforts.