Confluent

Alight Solutions, a leading provider of technology-enabled health, wealth, and human capital management solutions, faced a significant challenge. The company needed to integrate data from numerous back-end systems into a forward cache to enhance performance, simplify the development of consumer-facing applications, and reduce the demand on expensive mainframes. This integration was crucial to align the company’s internal organization with its next-generation digital strategy. The strategy was adopted to eliminate barriers to enterprise innovation and speed up the rate at which Alight brings new digital products to the market.

KeyBank, one of the nation's largest bank-based financial services companies, embarked on a national digital bank initiative following the acquisition of Laurel Road, a digital consumer lending business. The initiative aimed to build a digital bank focused on healthcare professionals looking to refinance student loans and buy homes. A significant challenge was reducing the time to market for new products by democratizing data and decoupling systems across the IT landscape. Like many large enterprises, KeyBank had a variety of vendor applications, custom applications, and other systems that were tightly coupled to one another. New projects often required developing specific point-to-point integrations for exchanging data, which did not address the needs of other downstream systems that could benefit from the same data.

8x8, a leading cloud communications platform provider, was faced with the challenge of delivering real-time analytics for its cloud-based communication services in a mission-critical, multi-tenant environment. The company provides voice, video, chat, and contact center solutions on a unified global platform, complemented by real-time analytics that help enterprises accelerate their businesses. Advanced analytics are critical in a contact center environment, where supervisors utilize real-time dashboards with hundreds of metrics. These dashboards provide insight into key customer experience metrics such as customer wait time and first-call resolution rates. As a fast-growing, multi-tenant SaaS contact center solution, 8x8 needed a way to rapidly and cost-effectively add new customers. The platform needed to be multi-tenant, providing strict data separation—a key customer requirement—and it had to be highly resilient given its mission-critical nature. Robust security was also a must, since 8x8 handles sensitive caller information.

SunPower, a leading residential and commercial renewable energy company, monitors over 286,000 systems and 6 million devices in the United States. The company aimed to differentiate itself by creating a software offering for consumers that pairs with their SunPower Equinox® solar and SunVault™ storage systems. This software would provide consumers with rich access to their own energy usage data, control over their SunVault™ system, and allow SunPower to optimize its service and offerings. However, this required SunPower to collect, aggregate, and provide access to real-time data from 6 million devices, a task that was beyond the capabilities of its legacy system. SunPower wanted to utilize the vast amount of data it generated on energy usage across its existing fleet to build more efficient products, make data more accessible and actionable by different parts of the business, and offer customers a more instantaneous energy management experience.

Storyblocks, a rapidly growing media company, faced challenges with its monolithic application and synchronous REST API calls between services. As the company transitioned from a disruptor to a major industry player, it began to experience issues with the application they had built when the company was started. The issues persisted even after they had split the monolith into microservices. Developers and data engineers were unable to resolve issues quickly or iterate on their search functionality with sufficient agility. The increasing amount of data threatened to slow down productivity and time to market for new features. The initial solution, an AWS Kinesis data pipeline dumping raw data into Amazon S3, began to fail due to lack of scalability and suitability for fully decoupling services.