“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.”
"This solution uses multiple features of Confluent Platform. We structure the data from our Cassandra databases using a model stored in Schema Registry, and we use Confluent Replicator to replicate topics across multiple datacenters."
"We had no technologies to replace or augment because we’d never used event streaming technology before. Some of our members from different groups had experience handling Apache Kafka, but they weren’t part of the bigger project to build a data streaming platform. Confluent had an ability to immediately offer enterprise-level support and that’s why we decided to team up with them."
“When we came to the point where we had to stand up an enterprise-grade streaming data platform, we looked at multiple options and Confluent was a clear winner here.”
“We started out managing our own Kafka clusters, but as soon as we got to scale, we realised it would be too inefficient and expensive for us to keep self-managing. Thankfully, with Confluent, we get fully managed products and services that go beyond just infrastructure; they help us scale Kafka, handle upgrades, and solve problems when required.”
"Data streaming is quickly becoming the central nervous system of our infrastructure as it powers real-time customer experiences across our 12 countries of operations. Stream Designer’s low-code, visual interface will enable more developers, across our entire organization, to leverage data in motion. With a unified, end-to-end view of our streaming data pipelines, it will improve our developer productivity by making real-time applications, pipeline development, and troubleshooting much easier."
“Banks can’t expect to go on operating on outdated technology. Traditional banks can’t expect to move into the future without using data in real time. Confluent helps us set our data in motion.”
“What’s needed is a highly scalable and agile data platform with loosely coupled architecture that will enable you to build applications on top of it with the resilience and reliability that banks need and regulators require"
“First, the cost and risk of making changes to large monolithic applications is prohibitive. Second, you end up with your data locked up in a particular technology and vendor, which can be very challenging to evolve.”
“When it comes to batch processing versus stream processing, both of them work. But one is a much more efficient way of doing it. A lot of the cost savings over the last twelve months has come from moving into a more efficient way of developing applications with stream processing.”
“We looked at building our own architecture, but we’d much rather consume services than build our own complex Kafka infrastructure. And you [Confluent] invented Kafka, so obviously we just went with that.”
"In addition to providing the performance, scalability, and resiliency that we require for this critical piece of our architecture, Confluent provides the expertise and operational support that lets us focus on building features that are going to help our customers sell more vehicles."
"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."
"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."
"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."