AWS Big Data Blog

Tag: Amazon Kinesis Firehose

Creating customized Vega visualizations in Amazon Elasticsearch Service

This post shows how to implement Vega visualizations included in Kibana, which is part of Amazon Elasticsearch Service (Amazon ES), using a real-world clickstream data sample. Vega visualizations are an integrated scripting mechanism of Kibana to perform on-the-fly computations on raw data to generate D3.js visualizations. For this post, we use a fully automated setup using AWS CloudFormation to show how to build a customized histogram for a web analytics use case. This example implements an ad hoc map-reduce like aggregation of the underlying data for a histogram.

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Stream data to an HTTP endpoint with Amazon Kinesis Data Firehose

The value of data is time sensitive. Streaming data services can help you move data quickly from data sources to new destinations for downstream processing. For example, Amazon Kinesis Data Firehose can reliably load streaming data into data stores like Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Elasticsearch Service (Amazon ES), and Splunk. […]

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How FactSet automated exporting data from Amazon DynamoDB to Amazon S3 Parquet to build a data analytics platform

This is a guest post by Arvind Godbole, Lead Software Engineer with FactSet and Tarik Makota, AWS Principal Solutions Architect. In their own words “FactSet creates flexible, open data and software solutions for tens of thousands of investment professionals around the world, which provides instant access to financial data and analytics that investors use to […]

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Your guide to Amazon Kinesis sessions, chalk talks, and workshops at AWS re:Invent 2018

AWS re:Invent 2018 is almost here! This post includes a list of Amazon Kinesis sessions, chalk talks, and workshops at AWS re:Invent 2018. You can choose the link next to each session description for the session schedule. Use the information to help schedule your conference week in Las Vegas to learn more about Amazon Kinesis. Sessions ANT208 – […]

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How I built a data warehouse using Amazon Redshift and AWS services in record time

Over the years, I have developed and created a number of data warehouses from scratch. Recently, I built a data warehouse for the iGaming industry single-handedly. To do it, I used the power and flexibility of Amazon Redshift and the wider AWS data management ecosystem. In this post, I explain how I was able to build a robust and scalable data warehouse without the large team of experts typically needed.

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Optimize Delivery of Trending, Personalized News Using Amazon Kinesis and Related Services

Gunosy aims to provide people with the content they want without the stress of dealing with a large influx of information. We analyze user attributes, such as gender and age, and past activity logs like click-through rate (CTR). We combine this information with article attributes to provide trending, personalized news articles to users. In this post, I show you how to process user activity logs in real time using Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, and related AWS services.

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Power data ingestion into Splunk using Amazon Kinesis Data Firehose

With Kinesis Data Firehose, customers can use a fully managed, reliable, and scalable data streaming solution to Splunk. In this post, we tell you a bit more about the Kinesis Data Firehose and Splunk integration. We also show you how to ingest large amounts of data into Splunk using Kinesis Data Firehose.

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Visualize and Monitor Amazon EC2 Events with Amazon CloudWatch Events and Amazon Kinesis Firehose

Monitoring your AWS environment is important for security, performance, and cost control purposes. For example, by monitoring and analyzing API calls made to your Amazon EC2 instances, you can trace security incidents and gain insights into administrative behaviors and access patterns. The kinds of events you might monitor include console logins, Amazon EBS snapshot creation/deletion/modification, […]

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Analyzing VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight

Many business and operational processes require you to analyze large volumes of frequently updated data. Log analysis, for example, involves querying and visualizing large volumes of log data to identify behavioral patterns, understand application processing flows, and investigate and diagnose issues. VPC flow logs capture information about the IP traffic going to and from network […]

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Derive Insights from IoT in Minutes using AWS IoT, Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight

Ben Snively is a Solutions Architect with AWS Speed and agility are essential with today’s analytics tools. The quicker you can get from idea to first results, the more you can experiment and innovate with your data, perform ad-hoc analysis, and drive answers to new business questions. Serverless architectures help in this respect by taking […]

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