AWS re:Invent 2017

From 2014 through 2017, TomTom successfully migrated its major business systems to the AWS Cloud. This migration helped TomTom's vision of real-time mapmaking become a reality, and it put TomTom significantly ahead of the competition in the domain of location technology. In this session, we explore the practical aspects of migrating to the cloud, including technological challenges as well as the necessary shifts in mindset to successfully get us through the migration journey. We discuss how the migration was done gradually due to huge risk exposure all the while maintaining 24/7 system uptime. We explore how the expected benefits of the cloud came to life, and we elaborate on the unexpected benefits, such as cost of ownership and increased awareness in the teams, as well as upper-stack benefits and the possibility to fit services and hardware while scaling the system—benefits that we could not have realized with on-premises infrastructure.

As GE Transportation moved their applications to the cloud, they faced operational challenges with monitoring their applications and platforms for availability, performance, and compliance. To address their exact requirements, GE developed a cost effective, scalable monitoring and alerting solution based on fully-managed AWS services. In this session, GE Transportation reviews this capability and discusses its process for implementing the solution. Attendees also learn reusable design patterns using AWS managed services that can be applied in a self-service model to scale efficiently across the enterprise.

When critical business applications move to the AWS Cloud, the business needs to be assured that applications will migrate rapidly and that performance will be as good or better than on-premises. This session covers a proven solution to evaluate, move, and compare migrated applications and assure they meet user expectations. The session also covers how to monitor and intelligently remediate applications on an ongoing basis, so user experience is consistent and can scale and heal accordingly. You see Cisco CloudCenter in action, along with discovery and third-party migration tools used to understand applications and move them to AWS. With AppDynamics and CloudCenter working together, you can see before and after examples of a business application running as good as or better than when on-premises. We also share advanced use cases of AppDynamics, providing user experience analytics and directing CloudCenter to scale applications. Session sponsored by Cisco

With increasing competition and shrinking budgets across the industry, Oil and Gas companies around the world are looking to optimize their oil well production, knowing that even single-digit efficiency gains could have a financial impact in the range of hundreds of millions of dollars. To do so, complex reservoir models and other compute-intensive simulations need to be run on the fly, requiring more compute and storage resources than ever before. In this session, you will learn how customers are running complex reservoir simulations in a scalable and cost-effective way using Spot instances and such AWS HPC technologies as cfnCluster, EnginFrame, DCV, as well as the myHPC cluster management solution. We will share reference architectures and provide best practices and considerations for instance and storage selection to optimize performance and minimize cost for the most demanding HPC workloads.

With geoseismic datasets that are petabytes in size and growing, finding tomorrow's energy is increasingly data and compute intensive. Hess Corporation, a global energy company, needed to be able to respond quickly to changing oil market demands, while minimizing costs. By migrating petabytes of data and running high performance computing (HPC) workloads on AWS, Hess reduced compute costs and accelerated time in which geologists received results. In this session, you will learn how Hess built a GeoSeismic data repository on AWS, by leveraging S3 and EFS, and processes that data by building HPC clusters on-demand using the GPU-enabled P2 instance family. Additionally, you will learn how the Hess subsurface computing team was able to move from running on premise cap-ex driven GPU clusters to an op-ex driven on-demand model in the AWS cloud.

Supervisory Control and Data Acquisition (SCADA) systems are critical real-time software applications used to manage nearly any form of upstream, midstream, and downstream processes in the energy industry. Traditionally, these technologies have been deployed on premises and managed separately from core IT, to ensure security, availability and consistent performance.  As energy and utility companies expand geographically, and the number and types of sensors in each location grow, disparate and growing data streams are becoming increasingly complex and challenging to manage. It is estimated that up to 95% of valuable device and sensor information is left stranded in the field, information that could prove valuable to machine learning, predictive analytics, and process optimization.    In this session, energy and utility customers will learn how easy it is to implement IIoT on AWS, so they can easily extract value from additional devices and sensors, and innovate faster. We will dive into a reference architecture for accessing current mission critical SCADA data as well as previously stranded data into AWS using Kinesis and DynamoDB, ultimately enabling customers to reduce downtime, increase efficiencies, improve reliability, and gain more business insights through connected data.

What if your utilities company could fix your hot water service before you knew it was broken, or introduced novel pricing models to improve global sustainability? Centrica, a global utility company with notable brands like British Gas, is a market leader in connected home products that help customers manage their energy use. With millions of customers and thousands of device installations a week, the business was outgrowing their on-premises data center despite ongoing investments, so they needed a reliable and elastic architecture that could quickly scale to meet demand. They also needed an agile and compliant IoT platform to manage the explosion of data resulting from more customers, more devices, and more sensors. With AWS IoT, they can focus on delivering better customer experiences while generating valuable business insights to optimize energy usage, reduce costs, and enable global sustainability. In this session, participants learn how Centrica seamlessly migrated to AWS IoT, and how they are modernizing their platform to deliver the future of energy.

At AWS, security is job zero. Our infrastructure is architected for the most data-sensitive, financial services companies in the world. We have worked with global enterprises to meet their respective security requirements and have learned that there are best practices and pitfalls to avoid. In this session, we provide a guided tour of governance patterns to avoid – ones that may seem logical at first, but that actually impede your ability scale and realize business agility. We also cover best practices, such as setting up key preventative and detective controls for implementing 360-degrees of security coverage, practicing DevSecOps on a massive scale, and leveraging the AWS services (such as Amazon VPC, IAM, Amazon EMR, Amazon S3, Amazon CloudWatch, and AWS Lambda) to meet the most strict and robust enterprise security requirements.

For many securities organizations, post-trade processing is expensive, cumbersome, and time-consuming. This is in part due to the massive volumes of data required for processing a trade and the limited agility of the technology on which many organizations rely today. In order to create efficiencies and move faster, many financial services organizations are working with AWS to implement post-trade solutions built with AWS storage services (Amazon S3 and Amazon Glacier) and big data capabilities (Amazon Athena, Amazon EMR, Amazon Redshift, and Amazon QuickSight ). In this session, we walk through a trade capture and regulatory reporting solution that uses the aforementioned AWS services. We also provide guidance around obtaining data-driven insights (from pixels to pictures); bolstering encryption with AWS KMS; and maintaining transparency and control with Amazon CloudWatch and Amazon CloudTrail (which also helps meet SEC Rule 613 that requires the creation of comprehensive consolidated audit trails).

Financial institutions today must manage multiple data types from a wide variety of sources. Among these various data types, archive data presents a particular challenge: it is invisible to much of the organization and not easily leveraged by the lines of business for analytics, insight, and product innovation. Faced with massive volumes of archive data, Financial Services organizations are finding that delivering insights in a timely manner requires a data storage and analytics solution with more agility and flexibility than traditional data management systems can provide. In this session, we will discuss a design pattern that (1) brings this data into a highly available, lower-cost queryable archive within AWS than you currently have and (2) migrates that data to a data lake that the entire organization can use to extract insight and drive innovation. We will walk through a strategy that addresses the following topics: storing archive data in compressed, cost-effective, and readily available formats; creating lifecycle policies to archive older data sets and make them easily accessible; fully utilizing the features of object storage to enrich the data lake; and applying AWS analytics tools to gather business insights.