Category: Analytics

In this post, we share a solution that can be adopted by data providers to enable specific data subscribers to access their data products without requiring data subscribers to own an AWS account. Data providers can choose which data product(s) the data subscriber will be entitled to access and use.

This post will show you how to take your CloudWatch metrics to the next level by using your AWS resource tags in your monitoring setup.

This post demonstrates how to generate revenue from your APIs by metering API usage through Moesif and Amazon API Gateway, and use to invoice and collect payment for the API usage.

In this post, Ramya and I introduce you to the new SPG dashboard as a single pane of glass for your Marketplace transactions. You can view this dashboard without having AWS Identity and Access Management (IAM) permissions or technical proficiency on the underlying AWS services. We show how you can use the SPG dashboard for a simplified view of your AWS Marketplace subscriptions for spend management and usage tracking.

In this post, we demonstrated how to build a real-time product recommendation engine by leveraging the fully managed streaming capabilities of Amazon MSK, along with the real-time analytics and SQL capabilities of Rockset.

In this post, we will show how it may be possible to avoid wastage and better forecast food needs using PredictHQ’s dataset with  Amazon Forecast and other machine learning services.

In this blog post, we explore how businesses can use AWS Data Exchange with their on-premises Hive-compliant data source using Dremio to integrate third-party and on-premises data without moving or copying data. We also demonstrate how customers can use the consolidated data for business intelligence (BI) and exploratory analytics.

In this blog post, we demonstrate joining data from Snowflake with data shared from a third party provider via AWS Data Exchange in Amazon Redshift. This solution lets you access and combine data from all these resources without needing to build and maintain complex data pipelines.

In this blog post, Alex, Pramod, and I will show how to install and use the infrastructure we built to perform quant research at scale. We made the stack and examples available in the public repository so you can use it in your own investment research. This solution uses Apache Spark, Amazon EMR on EKS, Docker, Karpenter, EMR Studio Notebooks, and AWS Data Exchange for Amazon S3.

In this post, Brian, Snehanshu, and I’ll show you how to mask healthcare data for regulatory compliance using Amazon HealthLake and DataMasque.