How Venerable Accelerated Growth by Modernizing its Mainframes on AWS

By Dustin Ward

AWS FeedHow Venerable Accelerated Growth by Modernizing its Mainframes on AWS By Krithika Palami Selvam, Sr. Modernization Architect – AWSBy Gary Crook, CEO – Heirloom ComputingBy Niranjan Kulkarni, Project Manager – CognizantBy Rohit Darji and Ward Britton, Sr. Modernization Consultants – AWS Venerable needed to modernize a core business application for managing agent commissions. This…

Secure multi-tenant data ingestion pipelines with Amazon Kinesis Data Streams and Kinesis Data Analytics for Apache Flink

By Dustin Ward

AWS FeedSecure multi-tenant data ingestion pipelines with Amazon Kinesis Data Streams and Kinesis Data Analytics for Apache Flink When designing multi-tenant streaming ingestion pipelines, there are myriad ways to design and build your streaming solution, each with its own set of trade-offs. The first decision you have to make is the strategy that determines how…

AWS Contact Center Day – July 2021

By Dustin Ward

AWS FeedAWS Contact Center Day – July 2021 Earlier this week, I ordered from Amazon.fr a box of four toothpaste tubes, but only one was in the box. I called Amazon’s customer center. The agent immediately found my order without me having to share the long order number. She issued a refund and told me…

Data Caching Across Microservices in a Serverless Architecture

By Dustin Ward

AWS FeedData Caching Across Microservices in a Serverless Architecture Organizations are re-architecting their traditional monolithic applications to incorporate microservices. This helps them gain agility and scalability and accelerate time-to-market for new features. Each microservice performs a single function. However, a microservice might need to retrieve and process data from multiple disparate sources. These can include…

Use Amazon SageMaker Feature Store in a Java environment

By Dustin Ward

AWS FeedUse Amazon SageMaker Feature Store in a Java environment Feature engineering is a process of applying transformations on raw data that a machine learning (ML) model can use. As an organization scales, this process is typically repeated by multiple teams that use the same features for different ML solutions. Because of this, organizations are…