Tag: HAQM Kinesis Analytics

This post demonstrates how to implement a dynamic rules engine using HAQM 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.

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 […]

August 30, 2023: HAQM Kinesis Data Analytics has been renamed to HAQM Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. HAQM Keyspaces (for Apache Cassandra) is a scalable, highly available, and managed Apache Cassandra–compatible database service. With HAQM Keyspaces you don’t have to provision, patch, or manage […]

In this post, we discuss how you can use Apache Flink and HAQM Kinesis Data Analytics for Java Applications to address these challenges. We explore how to build a reliable, scalable, and highly available streaming architecture based on managed services that substantially reduce the operational overhead compared to a self-managed environment.

April 2024: The content of this post is no longer relevant and deprecated. August 30, 2023: HAQM Kinesis Data Analytics has been renamed to HAQM Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Clickstream events are small pieces of data that are generated continuously with high speed […]

February 9, 2024: HAQM Kinesis Data Firehose has been renamed to HAQM Data Firehose. Read the AWS What’s New post to learn more. AWS re:Invent 2018 is almost here! This post includes a list of HAQM Kinesis sessions, chalk talks, and workshops at AWS re:Invent 2018. You can choose the link next to each session description for the […]

Whether you’ve just signed up for your first AWS account or you’ve been with us for some time, there’s always something new to learn as our services evolve to meet the ever-changing needs of our customers. To help ensure you’re set up for success as you build with AWS, we put together this quick reference guide for Big Data training and resources available here on the AWS site.

Gunosy aims to provide people with the content they want without the stress of dealing with a large influx of information. We analyze user attributes, such as gender and age, and past activity logs like click-through rate (CTR). We combine this information with article attributes to provide trending, personalized news articles to users. In this post, I show you how to process user activity logs in real time using HAQM Kinesis Data Firehose, HAQM Kinesis Data Analytics, and related AWS services.

Kinesis Analytics now gives you the option to preprocess your data with AWS Lambda. This gives you a great deal of flexibility in defining what data gets analyzed by your Kinesis Analytics application. In this post, I discuss some common use cases for preprocessing, and walk you through an example to help highlight its applicability.

You can now provision and manage resources for HAQM Kinesis Analytics applications using AWS CloudFormation. Kinesis Analytics is the easiest way to process streaming data in real time with standard SQL, without having to learn new programming languages or processing frameworks.