Kafka in the Cloud: Why it’s 10x better with Confluent | Find out more
The Confluent for Startups AI Accelerator Program is a 10-week virtual initiative designed to support early-stage AI startups building real-time, data-driven applications. Participants will gain early access to Confluent’s cutting-edge technology, one-on-one mentorship, marketing exposure, and...
With AI model inference in Flink SQL, Confluent allows you to simplify the development and deployment of RAG-enabled GenAI applications by providing a unified platform for both data processing and AI tasks. Learn how you can use it to build a RAG-enabled Q&A chatbot using real-time airline data.
Build with Confluent helps system integrators develop joint solutions faster, including specialized software bundles, support from data streaming experts to certify offerings, and access to Confluent’s Go-To-Market (GTM) teams to amplify their offering in the market.
Our business at Loggi has grown a lot over the past few years, and with that expansion came the realization that our systems had to be more distributed. We pushed our architecture to a new level so we could keep up with the company's growth by building new event-driven systems and real-time data
Data streaming capabilities are transforming everything, from allowing you to see when your ride will arrive to powering curbside pickups of groceries. The immediacy and personalization of those commercial experiences are fast becoming the expectations when using public services and healthcare, too.
Companies in nearly every industry are using Apache Kafka to harness their streaming data and deliver rich customer experiences and real-time business insights. In fact, Kafka has become so widely accepted as the de facto technology for data streaming, that it’s now used by over 70% of the Fortune
Real-time fraud prevention has become a critical capability in the financial services industry. That’s especially true for digital-native banks like EVO Banco, which can be particularly vulnerable to common methods of financial fraud.
Capturing tech trends has become a bit tricky these days: whatever industry you’re in, uncertainty abounds. Planning has become harder, but businesses are finding new ways to innovate and respond quickly to fast-changing market conditions.
Spring has arrived in the northern hemisphere, and as we delight in the sight of flowers blossoming, trees budding, and greenery sprouting, we're reminded of the promise of brighter and warmer days ahead.
Over the last decade, financial services companies have doubled down on using real-time capabilities to differentiate themselves from the competition and become more efficient. This trend has had a huge impact on customer experience in banking especially, and home mortgage company Mr. Cooper
Confluent has successfully achieved Google Cloud Ready - AlloyDB designation for AlloyDB for PostgreSQL, Google Cloud’s newest fully managed PostgreSQL-compatible database service for the most demanding enterprise database workloads.
Who isn’t familiar with Michelin? Whether it’s their extensive product line of tires for nearly every vehicle imaginable (including space shuttles), or the world-renowned Michelin Guide that has determined the standard of excellence for fine dining for over 100 years, you’ve probably heard of them.
At Treehouse Software, when we speak with customers who are planning to modernize their enterprise mainframe systems, there’s a common theme: they are faced with decades of mission-critical and historical legacy mainframe data in disparate databases,
Capturing tech trends has become a bit tricky these days: whatever industry you’re in, uncertainty abounds. That’s made planning more difficult, but businesses are finding new ways to innovate with emerging technology and respond quickly to fast-changing market conditions.
Today, 92% of the world’s top 100 banks and 72% of the top 25 retailers use mainframes to deliver secure, highly reliable data for their customers. Citigroup even estimates that while banks spend over $200 billion a year on IT, nearly 80% of that money goes towards maintaining mainframe-dependent
Over the last decade, there’s been a massive movement toward digitization. Enterprises are defining their business models, products, and services to innovate, thrive, and compete by being able to quickly discover, understand, and apply their data assets to power real-time use cases.