Category: AWS Step Functions

Organizations have a requirement to implement data governance due to the presence of personally identifiable information. The process of sharing and operationalizing data movement becomes an involved task, as it must encompass data governance, data security, and include audit capabilities. Learn about a modern process LTIMindtree has introduced for data transfer using AWS Step Functions and for sharing transformed data to another account regardless of regional location.

Multi-region architectures improve latency for applications with global user bases and optimize RTO across the broadest set of potential failure events. However, creating and maintaining these architectures can add significant cost and complexity. Learn how to build and deploy a multi-region serverless application while minimizing cost and complexity using AWS Step Functions and Fauna, an AWS Partner and distributed document-relational database delivered as a cloud API.

Explore a solution Contino built for a client to automate security scanning on software packages. This allowed developers to continue building, without added friction, and helped security teams be confident that vulnerabilities are discovered early and didn’t make their way to production. Learn about the context Contino was working with and the approach it took, and the technical challenges faced. After reading this post, you’ll understand how to develop a similar solution for your organization and some pitfalls to avoid.

Learn about a multi-regional first-line of support solution Chaos Gears put together using a serverless application running AWS Lambda under the baton of AWS Step Functions to relieve a customer’s engineers from some of their operational burdens. The approach described in this post can be adapted to serve other needs or cover different cases, as AWS Step Functions’ visual workflows allow for a quick translation of business requirements to technical ones.

Moving data between multiple data stores requires an extract, transform, load (ETL) process using various data analysis approaches. ETL operations form the backbone of any modern enterprise data and analytics platform. AWS provides a broad range of services to deploy enterprise-grade applications in the cloud. This post explores a strategic collaboration between Tech Mahindra and a customer to build and deploy an enterprise data repository on AWS and create ETL workflows using a serverless architecture.

Many enterprises have .NET Framework legacy applications they need modernize and convert to cloud-native. One of the largest product feed automation and optimization platforms, GoDataFeed has a back-end platform that ingests millions of items of catalog data from multiple sources. Learn how Kloia addressed GoDataFeed’s challenges by transforming their legacy .NET Framework monolithic application into a .NET Core-based decoupled architecture.

Deploying AI solutions with ML models into production introduces new challenges. Machine Learning Operations (MLOps) has been evolving rapidly as the industry learns to marry new ML technologies and practices with incumbent software delivery systems and processes. WordStream is a SaaS company using ML capabilities to help small and mid-sized businesses get the most out of their online advertising. Learn how Slalom developed ML architecture to help WordStream productionize their machine learning efforts.

One of the key value propositions WP Engine makes to customers is high availability of websites (uptime), regardless of the stress those sites may encounter. Delivering a performant, reliable, digital experience, even under heavy load, is key to customers’ success. WP Engine’s WordPress Digital Experience Platform gives enterprises and agencies the agility, performance, intelligence, and integrations they need to prepare for and handle heavy traffic loads.