Tag: HAQM Kinesis

NOTE: As of November 2018, you can run Apache Flink programs with HAQM Kinesis Analytics for Java Applications in a fully managed environment. You can find further details in a new blog post on the AWS Big Data Blog and in this Github repository. ————————– September 8, 2021: HAQM Elasticsearch Service has been renamed to HAQM OpenSearch Service. See details. […]

Customers with strict compliance or data security requirements often require data to be encrypted at all times, including at rest or in transit within the AWS cloud. This post shows you how to build a real-time streaming application using Kinesis in which your records are encrypted while at rest or in transit. HAQM Kinesis overview […]

February 2, 2022: Blog updated by Chaitanya Shah. February 9, 2024: HAQM Kinesis Data Firehose has been renamed to HAQM Data Firehose. Read the AWS What’s New post to learn more. Organizations of different size who migrate their applications in cloud or applications born in cloud makes use of various cloud services to innovate and […]

Applications log a large amount of data that—when analyzed in real time—provides significant insight into your applications. Real-time log analysis can be used to ensure security compliance, troubleshoot operation events, identify application usage patterns, and much more. Ingesting and analyzing this data in real time can be accomplished by using a variety of open source […]

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 HAQM Web Services Starting today, you can easily scale your HAQM Kinesis streams to respond in real time to changes in your streaming data needs. Customers use HAQM 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 HAQM Kinesis Analytics.

This post introduces you to the different types of windows supported by HAQM 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.

This post introduces you to HAQM 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.

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 […]