AWS Big Data Blog
Category: Amazon Kinesis
Writing SQL on Streaming Data with Amazon Kinesis Analytics – Part 2
This post introduces you to the different types of windows supported by Amazon Kinesis Analytics, the importance of time as it relates to stream data processing, and best practices for sending your SQL results to a configured destination.
Monitor Your Application for Processing DynamoDB Streams
In this post, I suggest ways you can monitor the Amazon Kinesis Client Library (KCL) application you use to process DynamoDB Streams to quickly track and resolve issues or failures so you can avoid losing data. Dashboards, metrics, and application logs all play a part. This post may be most relevant to Java applications running on Amazon EC2 instances.
Writing SQL on Streaming Data with Amazon Kinesis Analytics – Part 1
This post introduces you to Amazon Kinesis Analytics, the fundamentals of writing ANSI-Standard SQL over streaming data, and works through a simple example application that continuously generates metrics over time windows.
Process Large DynamoDB Streams Using Multiple Amazon Kinesis Client Library (KCL) Workers
Asmita Barve-Karandikar is an SDE with DynamoDB Introduction Imagine you own a popular mobile health app, with millions of users worldwide, that continuously records new information. It sends over one million updates per second to its master data store and needs the updates to be relayed to various replicas across different regions in real time. […]
How SmartNews Built a Lambda Architecture on AWS to Analyze Customer Behavior and Recommend Content
This is a guest post by Takumi Sakamoto, a software engineer at SmartNews. SmartNews in their own words: “SmartNews is a machine learning-based news discovery app that delivers the very best stories on the Web for more than 18 million users worldwide.” Data processing is one of the key technologies for SmartNews. Every team’s workload […]
Analyze Realtime Data from Amazon Kinesis Streams Using Zeppelin and Spark Streaming
This post shows you how you can use Spark Streaming to process data coming from Amazon Kinesis streams, build some graphs using Zeppelin, and then store the Zeppelin notebook in Amazon S3.
Processing Amazon DynamoDB Streams Using the Amazon Kinesis Client Library
Asmita Barve-Karandikar is an SDE with DynamoDB Customers often want to process streams on an Amazon DynamoDB table with a significant number of partitions or with a high throughput. AWS Lambda and the DynamoDB Streams Kinesis Adapter are two ways to consume DynamoDB streams in a scalable way. While Lambda lets you run your application […]
Real-time in-memory OLTP and Analytics with Apache Ignite on AWS
February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. Babu Elumalai is a Solutions Architect with AWS Organizations are generating tremendous amounts of data, and they increasingly need tools and systems that help them use this data to make decisions. The […]
Analyze a Time Series in Real Time with AWS Lambda, Amazon Kinesis and Amazon DynamoDB Streams
This is a guest post by Richard Freeman, Ph.D., a solutions architect and data scientist at JustGiving. JustGiving in their own words: “We are one of the world’s largest social platforms for giving that’s helped 26.1 million registered users in 196 countries raise $3.8 billion for over 27,000 good causes.” Introduction As more devices, sensors […]
Optimize Spark-Streaming to Efficiently Process Amazon Kinesis Streams
Rahul Bhartia is a Solutions Architect with AWS Martin Schade, a Solutions Architect with AWS, also contributed to this post. Do you use real-time analytics on AWS to quickly extract value from large volumes of data streams? For example, have you built a recommendation engine on clickstream data to personalize content suggestions in real time […]