141 Confluent Testimonials

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  • “Handing off the operational responsibility for Kafka to someone was, to King, comparable to leaving your kid at kindergarten for the first time. You really need to trust that other party to take good care of your baby.”

  • "Confluent and Google Cloud enabled us to address our large database footprint and retire our legacy data platform, which was in many ways our Achilles heel. After moving to real-time streaming on a cloud-based modern architecture, we can now deliver new features and capabilities to our customers and know that they won’t be slowed by an outdated architecture."

  • "We chose event-driven architecture as the core of our platform. For which we needed a messaging service that gave us all the guarantees not to mention that it had to be extremely scalable, highly available, and simple to use."

  • ”Data streaming helps us power key use cases—including real-time data warehousing for real-time analytics and adaptive bitrate streaming—where we can use real-time data to make timely decisions and deliver better experiences for our users. Ultimately, it makes us more profitable, so data streaming is extremely critical to our business and has a huge impact.”

  • “This resulted in several challenges related to broker version upgrades, scaling, and data observability—resulting in unnecessary engineering bandwidth spent on managing the infrastructure versus time spent on working on the product,”

  • “Confluent is critical for how Vimeo instruments our product and how we make that information available across a multitude of different surface areas. Whether data is fed back into the product or into a pipeline for ML and AI teams, or the product team’s self-service tools to better understand how our customers are experiencing the product. There are always opportunities for improvement, where analysts and data scientists can curate, measure, identify usage patterns on the user journey.”

  • "With Confluent Cloud, we have high-performance event streaming, with the ability to store events securely. This makes it possible for our deterministic matching engine and settlement system to go back in time and replay a sequence in order if we need to."

  • "Our experience with Confluent Support has been very positive. The response times have been quite fast. We’ve been able to isolate and resolve issues substantially faster than we could on our own, which definitely has helped our velocity."

  • “The strength of the engineering team at Ladder really lies in making a great customer experience and automating business processes, rather than running a Kafka cluster. Putting our data in motion with Confluent allows us to focus on differentiating Ladder with automated underwriting built around machine learning models fed by real-time data.”

  • "With our Kafka-backed data pipeline, we are able to support our partners, who every year create more services, more features, more data instrumentation, and even more granular data than the year before."

  • "Using Kafka and Confluent, Walmart has initiated a digital transformation and modern omnichannel experience that allows customers to interact with Walmart.com seamlessly, order groceries online or in a mobile app, and either pick them up or have them delivered. Walmart’s investment in event streaming with Confluent has contributed to business innovation as well as company growth in the public market."

  • "Confluent simplifies our processes and reduces complexity, allowing us to be more effective."

  • "A key advantage of Confluent Cloud in delivering AutoExtract is time to market. We didn’t have to set up a Kafka cluster ourselves or wait for our infrastructure team to do it for us. With Confluent Cloud we quickly had a state-of-the-art Kafka cluster up and running perfectly smoothly. And if we run into any issues, we have experts at Confluent to help us look into them and resolve them. That puts us in a great position as a team and as a company."

  • "Fans care about what seats are still available, how much do they cost, and what packages they can get with the tickets. And the venue wants to know who’s buying the tickets, are we having problems converting people, do we need to change the offering, or do we need to put another tour date on the calendar because this one is super popular. These are all different ways of looking at the same data. So we put all of this into an inventory stream which gets placed in a different data store for the various uses of that data. Confluent and Kafka have allowed us to get to the position where it’s now fairly low-friction for the data science team to roll out new capabilities with our data."

  • “To have data streaming implemented at a global scale at L'Oréal called for a platform that’s reliable, can auto-scale depending on our need and can cater to our future growth—all while ensuring we get the utmost support we need. That’s why we invested in Confluent.”