AWS Big Data Blog

Tag: Amazon Kinesis

Persist Streaming Data to Amazon S3 using Amazon Kinesis Firehose and AWS Lambda

Derek Graeber is a Senior Consultant in Big Data Analytics for AWS Professional Services Streaming data analytics is becoming main-stream (pun intended) in large enterprises as the technology stacks have become more user-friendly to implement. For example, Spark-Streaming connected to an Amazon Kinesis stream is a typical model for real-time analytics. But one area that […]

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Integrating Amazon Kinesis, Amazon S3 and Amazon Redshift with Cascading on Amazon EMR

This is a guest post by Ryan Desmond, Solutions Architect at Concurrent. Concurrent is an AWS Advanced Technology Partner. With Amazon Kinesis developers can quickly store, collate and access large, distributed data streams such as access logs, click streams and IoT data in real-time. The question then becomes, how can we access and leverage this […]

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Implementing Efficient and Reliable Producers with the Amazon Kinesis Producer Library

Kevin Deng is an SDE with the Amazon Kinesis team and is the lead author of the Amazon Kinesis Producer Library How do you vertically scale an Amazon Kinesis producer application by 100x? While it’s easy to get started with streaming data into Amazon Kinesis, streaming large volumes of data efficiently and reliably presents some […]

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Presto-Amazon Kinesis Connector for Interactively Querying Streaming Data

This is a guest post by Sivaramakrishnan Narayanan, Member of Technical Staff at Qubole, and Xing Quan, Director of Product Management at Qubole. Qubole is an AWS Advanced Technology Partner. Amazon Kinesis is a scalable and fully managed service for streaming large, distributed data sets. As applications (particularly on mobile and wearable devices) start to […]

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Processing Amazon Kinesis Stream Data Using Amazon KCL for Node.js

Manan Gosalia is an SDE for Amazon Kinesis This blog post shows you how to get started with the Amazon Kinesis Client Library (KCL) for Node.js. The Node.js framework uses an event-driven, non-blocking I/O model that makes it lightweight, efficient, and perfect for data-intensive, real-time applications that run across distributed devices. JavaScript is also simple […]

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Streaming Analytics with DataTorrent RTS and Amazon EMR

Nick Durkin is a Senior Solution Engineer for DataTorrent. DataTorrent is an AWS Technology Partner. In this blog post, we introduce fast big data and provide context about the DataTorrent RTS streaming analytics platform. In addition, we show you how to implement a real-time, streaming analytics application for capturing social media trends from Twitter using […]

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Snakes in the Stream – Feeding and Eating Amazon Kinesis Streams with Python

Markus Schmidberger is a Senior Consultant for AWS Professional Services The Internet of Things (IoT) is becoming increasingly popular, and it’s easy to see why: it generates new business value for your company by connecting all available machines and devices. The big challenge is real-time data processing and analysis. Cloud computing is an excellent way […]

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Getting HBase Running on Amazon EMR and Connecting it to Amazon Kinesis

Wangechi Doble is an AWS Solutions Architect Introduction Apache HBase is an open-source, column-oriented, distributed NoSQL database that runs on the Apache Hadoop framework. In the AWS Cloud, you can choose to deploy Apache HBase on Amazon Elastic Cloud Compute (Amazon EC2) and manage it yourself or leverage Apache HBase as a managed service on […]

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Visualizing Real-time, Geotagged Data with Amazon Kinesis

Nick Corbett is a Big Data Consultant for AWS Professional Services Amazon Kinesis is a fully managed service for processing real-time data at massive scale.  Whether you are building a system that collects data from remote sensors, aggregating log files from multiple servers, or creating the latest Internet of Things (IoT) solution, Amazon Kinesis lets […]

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Implement a Real-time, Sliding-Window Application Using Amazon Kinesis and Apache Storm

Rahul Bhartia is an AWS Solutions Architect Streams of data are becoming ubiquitous today – clickstreams, log streams, event streams, and more. The need for real-time processing of high-volume data streams is pushing the limits of traditional data processing infrastructures. Building a clickstream monitoring system, for example, where data is in the form of a continuous clickstream rather […]

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