Presentation

AWS makes it easy to build and operate a highly scalable and flexible data platforms to collect, process, and analyze data so you can get timely insights and react quickly to new information. In this session, we will demonstrate how you can quickly build a fully managed data platform that transforms, cleans, and analyses incoming data in real time and persist the cleaned data for subsequent visualizations and through exploration by means of SQL. To this end, we will build an end-to-end streaming data solution using Kinesis Data Streams for data ingestion, Kinesis Data Analytics for real-time outlier and hotspot detection, and show how the incoming data can be persisted by means of Kinesis Data Firehose to make it available for Amazon Athena and Amazon QuickSight for data exploration and visualization. ...

In the fast-paced world of mortgage lending, speed and accuracy are crucial. To support their underwriters, Vontive transformed written rules for loan eligibility from a Google Doc into SQL queries for evaluation in a Postgres database. However, while functional, this setup struggled to scale with business growth, resulting in slow, cumbersome processing times. Executing just a handful of loan eligibility rules could take up to 27 seconds–far too long for user-friendly interactions. ...

There are various strategies for securely connecting to Kafka clusters between different networks or over the public internet. Many cloud providers even offer endpoints that privately route traffic between networks and are not exposed to the internet. But, depending on your network setup and how you are running Kafka, these options … might not be an option! In this session, we’ll discuss how you can use SSH bastions or a self managed PrivateLink endpoint to establish connectivity to your Kafka clusters without exposing brokers directly to the internet. We explain the required network configuration, and show how we at Materialize have contributed to librdkafka to simplify these scenarios and avoid fragile workarounds. ...

Over time, deploying and running Kafka became easier and easier. Today you can choose amongst a large ecosystem of different managed offerings or just deploy to Kubernetes directly. But, although you have plenty of options to optimize your Kafka configuration and choose infrastructure that matches your use case and budget, it’s not always easy to tell how these choices affect overall cluster performance. In this session, we’ll take a look at Kafka performance from an infrastructure perspective. How does your choice of storage, compute, and networking affect cluster throughput? How can you optimize for low cost or fast recovery? When is it better to scale up rather than to scale out brokers? ...

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. ...

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. ...

In this chalk talk, we discuss the benefits of different AWS streaming services and walk through some use cases for each. We share best practices based on real customer examples and discuss a framework that you can use to determine which set of services best suit your specific use case. Finally, we show some interactive examples, so come ready with your real-life scenarios that we can discuss live. https://d1.awsstatic.com/events/reinvent/2019/Choosing_the_right_service_for_your_data_streaming_needs_ANT316.pdf

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. ...

One of the big visions of Apache Beam is to provide a single programming model for both batch and streaming that runs on multiple execution engines. In this session, we explore an end to end example that shows how you can combine batch and streaming aspects in one uniform Beam pipeline: We start with ingesting taxi trip events into an Amazon Kinesis data stream and use a Beam pipeline to analyze the streaming data in near real time. We then show how to archive the trip data to Amazon S3 and how we can extend and update the Beam pipeline to generate additional metrics from the streaming data moving forward. We subsequently explain how to backfill the added metrics by executing the same Beam pipeline in a batch fashion against the archived data in S3. Along the way we furthermore discuss how to leverage different execution engines, such as, Amazon Kinesis Data Analytics for Java and Amazon Elastic Map Reduce, to run Beam pipelines in a fully managed environment. ...

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. ...