Category: Compute

In this post, we’ll show you how to deploy an Ethereum blockchain using the AWS Blockchain Templates, deploy a smart contract, and build a serverless analytics pipeline for that contract based around AWS Lambda, HAQM Kinesis, and HAQM Athena.

In this blog post, we focus on how to get analytics out of the rich set of data published by HAQM Connect. We make use of an HAQM Connect data stream and create an end-to-end workflow to offer an analytical solution that can be customized based on need.

In this post, I’ll show you how to use AWS Step Functions to orchestrate your Spark jobs that are running on HAQM EMR.

In this blog post, I present a solution that exports system tables from multiple HAQM Redshift clusters into an HAQM S3 bucket. This solution is serverless, and you can schedule it as frequently as every five minutes. The AWS CloudFormation deployment template that I provide automates the solution setup in your environment. The system tables’ data in the HAQM S3 bucket is partitioned by cluster name and query execution date to enable efficient joins in cross-cluster diagnostic queries.

This blog post demonstrates how to combine some of the OHDSI projects (Atlas, Achilles, WebAPI, and the OMOP Common Data Model) with AWS technologies. By doing so, you can quickly and inexpensively implement a health data science and informatics environment.

Data that describe processes in a spatial context are everywhere in our day-to-day lives and they dominate big data problems. Map data, for instance, whether describing networks of roads or remote sensing data from satellites, get us where we need to go. Atmospheric data from simulations and sensors underlie our weather forecasts and climate models. […]

In this post, we outline three Cassandra deployment options, as well as provide guidance about determining the best practices for your use case.

This solution provides a serverless approach to automatically assigning a friendly name for your EMR cluster for easy access to popular notebooks and other web interfaces.

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.