AWS Marketplace

Category: Technical How-to

Building a real-time recommendation engine with Amazon MSK and Rockset

Building a real-time recommendation engine with Amazon MSK and Rockset

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.

Effective cloud remediation for Security Hub findings with Tamnoon.io

Effective cloud remediation for Security Hub findings with Tamnoon.io

In this blog post, we show you how you can efficiently scale the remediation process using Tamnoon.io, to prioritize alerts and findings, conduct impact analysis of findings, and fix misconfigurations with minimal production impact.

Transform enterprise search and knowledge discovery with Glean and Amazon Bedrock

Transform enterprise search and knowledge discovery with Glean and Amazon Bedrock

In this blog post, we introduce you to Glean – an enterprise-ready search and knowledge discovery solution that’s tailor-made for the enterprise workplace. Glean has been adopted by leading enterprise customers, including Databricks, Okta, and Grammarly, to solve their internal search and knowledge discovery needs. Now available in AWS Marketplace, Glean uses powerful large language models (LLMs) hosted by Amazon Bedrock to deliver generative AI solutions to the millions of customers building on AWS.

New 2023: Best practices guide to successfully list your SaaS contract solution in AWS Marketplace

New 2023: Best practices guide to successfully list your SaaS contract solution in AWS Marketplace

In this post, I aim to help you create a SaaS contract product in AWS Marketplace. I take the current SaaS pricing tiered model and match it with the specific AWS Marketplace SaaS contract pricing model. I also explain the AWS Marketplace features that are relevant based on sales motions as well as best practices for structuring your product detail page.

Using PredictHQ data from AWS Data Exchange for demand forecasting

Food supply chain optimization using PredictHQ intelligent event data from AWS Data Exchange for demand forecasting

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.

Simplifying buyer procurement workflow integration with AWS Marketplace

Simplifying buyer procurement workflow integration with AWS Marketplace

In this post, we share an approach using selected AWS features that help govern how buyers use your organization’s procurement workflows for their AWS Marketplace subscriptions. We show you how to integrate these features into your procurement workflow with AWS Marketplace subscriptions with minimal effort.

Streamlining Third-party add-on management in Amazon EKS cluster using Terraform and Amazon EKS add-on catalog

Streamlining Third-party add-on management in Amazon EKS cluster using Terraform and Amazon EKS add-on catalog

In this blog post, you can learn how to use Terraform, a popular infrastructure as code (IaC) tool, for creating and managing the lifecycle of add-ons in an EKS cluster. In this post, I will show you how to find, install, and delete Amazon EKS third-party add-ons using Terraform.

Accelerate self-service analytics integrating on-premises and third-party data with AWS Data Exchange and Dremio

Accelerate self-service analytics integrating on-premises and third-party data with AWS Data Exchange and Dremio

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.

Join third-party data in Amazon Redshift with Snowflake using Amazon Athena

Join third-party data in Amazon Redshift with Snowflake using Amazon Athena  

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.

Quant research at scale using AWS and Refinitiv data

Quant research at scale using AWS and Refinitiv data

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.