Category: Amazon Kinesis

All software developers strive to build products that are functional, robust, and bug-free, but video game developers have an extra challenge: they must also create a product that entertains. When designing a game, developers must consider how the various elements—such as characters, story, environment, and mechanics—will fit together and, more importantly, how players will interact […]

Are you trying to move away from a batch-based ETL pipeline? You might do this, for example, to get real-time insights into your streaming data, such as clickstream, financial transactions, sensor data, customer interactions, and so on.  If so, it’s possible that as soon as you get down to requirements, you realize your streaming data […]

Allan MacInnis is a Kinesis Solution Architect for Amazon Web Services Starting today, you can easily scale your Amazon Kinesis streams to respond in real time to changes in your streaming data needs. Customers use Amazon Kinesis to capture, store, and analyze terabytes of data per hour from clickstreams, financial transactions, social media feeds, and […]

In this post, I show an analytics pipeline which detects anomalies in real time for a web traffic stream, using the RANDOM_CUT_FOREST function available in Amazon Kinesis Analytics.

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.

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.

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.

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. […]

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 […]

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.