Category: AWS Lambda

Kinesis Analytics now gives you the option to preprocess your data with AWS Lambda. This gives you a great deal of flexibility in defining what data gets analyzed by your Kinesis Analytics application. In this post, I discuss some common use cases for preprocessing, and walk you through an example to help highlight its applicability.

In this post, we walk you through a solution that implements a continuous integration and deployment pipeline supported by AWS services. You can use the sample template and Spark application shared in this post and adapt them for the specific needs of your own application.

Deriving insights from large datasets is central to nearly every industry, and life sciences is no exception. To combat the rising cost of bringing drugs to market, pharmaceutical companies are looking for ways to optimize their drug development processes. They are turning to big data analytics to better quantify the effect that their drug compounds […]

Nowadays, it’s common for a web server to be fronted by a global content delivery service, like Amazon CloudFront. This type of front end accelerates delivery of websites, APIs, media content, and other web assets to provide a better experience to users across the globe. The insights gained by analysis of Amazon CloudFront access logs […]

In the healthcare field, data comes in all shapes and sizes. Despite efforts to standardize terminology, some concepts (e.g., blood glucose) are still often depicted in different ways. This post demonstrates how to convert an openly available dataset called MIMIC-III, which consists of de-identified medical data for about 40,000 patients, into an open source data […]

In this post, I discuss some of the practical challenges faced during the implementation of the data lake for Eliza and the corresponding details of the ways we solved these issues with AWS. The challenges we faced involved the variety of data and a need for a common view of the data.

In this post, I walk you through an architectural approach as well as a sample implementation on how to collect, process, and analyze data for event-driven applications in AWS.

In this post, I show you how to gain valuable insight into your network by using Amazon EMR and Amazon VPC Flow Logs. The walkthrough implements a pattern often found in network equipment called ‘Top Talkers’, an ordered list of the heaviest network users, but the model can also be used for many other types of network analysis.

NOTE: Amazon Redshift now supports creating an automatic snapshot schedule using the snapshot scheduler. For more information, please review this “What’s New” post. ———————————- Ian Meyers is a Solutions Architecture Senior Manager with AWS Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that makes it simple and cost-effective to analyze all your data […]

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. Babu Elumalai is a Solutions Architect with AWS Organizations are generating tremendous amounts of data, and they increasingly need tools and systems that help them use this data to make decisions. The […]