Most organizations engaged in transformation today are moving from left to right in digitally-driven maturity models. The objectives are well known: increase agility, boost productivity, and provide seamless digital experiences for consumers.
Architects play a pivotal role as the curators of this transformation. In this session, Asanka will share his experience on how architects can contribute and introduce a framework to follow on refactoring enterprises.

The purpose of this effort is to collect, organize, and analyze data on remote learning at a school, teacher, and student levels across multiple platforms and activities to monitor, track, and report on remote/blended learning across all schools in the New York City.

-Ingest data from all learning tools and DOE enterprise systems
Support all data formats
-Access to system-wide data across all platforms, resources, school types 
-Ability to connect any additional data sources to central database 
-Supplemental learning resources by grade level
-Access to individual-level data, in order to create aggregates and cuts as needed (including by school, by demographics, etc.)

Should you really always run your cluster in multiple availability zones? How can a transition rule to Glacier double your storage costs? I want to monitor and understand my data transfer costs, where should I start? Why are backups eating my database budget? What, one more storage class on S3? Following so-called “best practices” works only when you fully understand the implications, costs included. We will address a few cloud anti-patterns, making your bill smaller and your deployment better.

Building on its rich history of innovation, Choice Hotels used a cloud-first approach to accelerate innovation and battle digital-native competitors. 

To escape the gravitational pull of data and make the cloud promise a reality, Choice Hotels brought on an API-first programmable data infrastructure platform to automate data compliance and data delivery. By doing so, the organization broke down data silos, allowing software teams to significantly improve the quality and speed of application testing and development.

In this fireside chat with Jason Simpson, VP of Engineering at Choice Hotels, you’ll discover: 

• Learnings from a migration of hundreds of applications from legacy systems to Amazon Web Services (AWS)

• Insights on leveraging data to minimize the risk of outages and delivering a frictionless experience for customers and franchisees across platforms

• Reaping the scalability of cloud to manage unexpected business shifts including impacts from COVID-19

• Perspectives on how Programmable Data Infrastructure can be leveraged for Migration, CI/CD Acceleration and even Service Restoration with APM Integration

Speakers: Jason Simpson, VP of Engineering at Choice Hotels & Alex Hesterberg, Chief Customer Officer at Delphix

Many enterprises want to become multi-cloud or have the ability to switch clouds. However, cloud providers try very hard to lock in customers with proprietary core services. Cloud Sidecar provides a simple way to take existing software and deploy it to different clouds without complex rewrites. Learn about Cloud Sidecar and how it can unlock your software from only working on one cloud.

Amazon and AWS have a long history with DevOps, averaging 1 production release a minute. While other companies may not release at that scale, it has become increasingly clear that DevOps is both a unique opportunity and a key challenge in the SDLC. In this session, we will go over DevOps at AWS and how our tooling supports our customer’s journey to DevOps Nirvana.

Provisioning AWS resources one by hand is tedious and cumbersome, especially in an environment where you have multiple similar set ups. Learn how you can use AWS CloudFormation to simplify setting your virtual machine up and how it can be used to even install and configure your software for you.

Streaming and Eventing platforms like RabbitMQ, Kafka and IBM MQ are key enablers of Messages-Oriented-Middleware & Event Driven Architectures which are foundational to Digital Transformation.
Performance management for interactions with these platforms is always challenging.
Latency could have wide ranging impact on business
Creating Machine Learning based models to predict latency related anomalies would help avoid business outages before they happen.