AWS Big Data Blog

Tag: Amazon Kinesis

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 […]

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Process Amazon Kinesis Aggregated Data with AWS Lambda

Ian Meyers is a Solutions Architecture Senior Manager with AWS Last year, we introduced the Amazon Kinesis Producer Library (KPL) to simplify the development of applications that need to send data to Amazon Kinesis Streams. Many customers use aggregation, which allows you to send multiple records to a single Amazon Kinesis Streams record.  Although the […]

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Querying Amazon Kinesis Streams Directly with SQL and Spark Streaming

Amo Abeyaratne is a Big Data consultant with AWS Professional Services Introduction What if you could use your SQL knowledge to discover patterns directly from an incoming stream of data? Streaming analytics is a very popular topic of conversation around big data use cases.  These use cases can vary from just accumulating simple web transaction […]

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Building a Near Real-Time Discovery Platform with AWS

Assaf Mentzer is a Senior Consultant for AWS Professional Services In the spirit of the U.S presidential election of 2016, in this post I use Twitter public streams to analyze the candidates’ performance, both Republican and Democrat, in a near real-time fashion. I show you how to integrate AWS managed services—Amazon Kinesis Firehose, AWS Lambda […]

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Integrating Splunk with Amazon Kinesis Streams

Prahlad Rao is a Solutions Architect wih AWS It is important to not only be able to stream and ingest terabytes of data at scale, but to quickly get insights and visualize data using available tools and technologies. The Amazon Kinesis platform of managed services enables continuous capture and stores terabytes of data per hour from […]

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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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