Issue link: https://read.uberflip.com/i/1407728
A STRATEGIC PLAYBOOK FOR DATA, ANALYTICS, AND MACHINE LEARNING INNOVATION SPOTLIGHT Equinox Media As COVID-19 bore down in the spring of 2020, fitness brand Equinox recognized the need to fast-track the launch of its Variis online fitness streaming platform. With the company anticipating a surge in demand for in-home fitness services, the development team looked to learnings from Equinox's Fitness environment to develop a modern and scalable data infra- structure based on an Amazon S3 data lake, and serverless architecture including AWS Lambda, Amazon DynamoDB, and Amazon Athena. By developing Variis from the ground up with a cloud-native infrastructure, Equinox Media was able to accelerate the new platform's launch and quickly scale up to meet demand, says Elliott Cordo, Equinox Media's Vice President of Technology Insights. Leveraging the highly scalable, serverless data platform, "we ramped from beta users to launch in just a matter of weeks," says Cordo. "We launched a startup at very low cost with the confidence that we could scale it with reliable cost prediction," he says. "The ability to handle explosive growth clearly demonstrates the advantages of modern data engineering and cloud-native design." The benefits of a cloud-based data foundation A modern, cloud-based data infrastructure is essential to gain the flexibility and scalability needed to react quickly to changing business needs. With a modern data architecture, there are virtually no limits to how much and what kind of data an organization can store and manage, opening up myriad possibilities for leveraging information in new and better ways across the business. "Decades ago, databases were designed to be opti- mized for storage because the costs were so high," explains Herain Oberoi, Director of Product Marketing, Databases, Analytics, and Blockchain, AWS. "Cloud economics have removed the constraints of having to decide what data to store and what to discard. Now the default is, let's just store everything because we might not know what we want to do with the data, what questions we want to ask, or what insights we might get down the road." Unlike the rows/columns/table structure of a tradi- tional data warehouse, a modern data architecture can store all types of semi-structured or unstructured data such as web logs and images, eliminating the need for separate data silos. A centralized data lake also enables the ability to tag and catalog data to make it discover- able, ingest and process data in real time via streaming technologies, and apply security controls and permis- sions to the data to promote governance and maintain compliance. Elasticity is another advantage of a modern cloud- based data infrastructure. A traditional environment requires investment in software licenses, infrastructure, and data center horsepower to drive big analytics workloads, even if they are temporary to accommodate a surge or a specific use case. Not so in the cloud, where you can spin up or scale back infrastructure based on the needs of the analytics workload. That provides an opportunity to start small and go big or front-load horsepower to handle a temporary surge period and only pay for the capacity used. "Fundamentally, cloud is about low-cost and elastic storage and compute," says Oberoi. "That makes analytics the perfect workload for cloud." u t 5