[Blog] New in Confluent Cloud: Queues for Kafka, new migration tooling, & more | Read Now

WHY CONFLUENT

Unlock Enterprise Data, Unleash Innovation

Each time data lags behind demand, your organization loses momentum.

Customers like BMW Group, Citizens Bank, Notion and Booking.com use streaming to set data in motion and choose Confluent to make data quality, accessibility, and AI readiness the new status quo. See how it works.

Why Is Data Streaming Essential for Unlocking Data Value?

Confluent gives developers, data engineers, and architects the tools to build applications and pipelines that can move, transform, and use data in milliseconds. All so you can build an organization that’s always ready to act on opportunities, eliminate inefficiencies, and mitigate risks.

That’s not the norm for modern organizations, which are slowed down waiting on batch processes and dealing with inevitable data sprawl. Ignoring the ever-growing “data monster” within your organization isn’t an option, but the underlying technical challenges make the path forward difficult to navigate.

The modern enterprise is a mess of fragile, point-to-point connections that make moving and using data a technical and logistical headache. That’s how “data silos” block your path to launching and iterating on mission-critical platforms, products, and services.

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These data challenges affect every line of business, organization, and industry.

Stock image - frustrated developer

Developers can’t get the data they need, and project timelines spin out.

Stock image - loss of revenue

Legacy systems break under high demand, causing customer and revenue loss.

Stock image - system error

Batch jobs fail, and it takes hours to spot via dashboard and service errors.

Enter Apache Kafka®


This open source streaming engine makes it possible to decouple systems and move data between them in real time, replacing 1:1 connections with a common integration layer.

Untangling your data estate with data streaming is just the first step. Founded by the original co-creators of Kafka, our team has built a complete data streaming platform for turning data streams into universal data products.


Compare Confluent vs. Apache Kafka



A Self-managed Kafka Platform Confluent Data Streaming Platform
AI Readiness Self-managed, Kafka-native integration and processing

Other data management capabilities, including advanced governance and processing, need to be built or integrated from an assortment of third-party solutions
Complete platform for serving AI-ready data:
Innovation Multiplier Requires in-house development and management of data catalog, lineage, and advanced processing capabilities Out-of-the-box tooling to create, discover, and share universal data products across lines of business, accelerating analytics and AI use cases and unlocking new revenue opportunities
Operational Efficiency Infrastructure, operations, and platform development costs are on the business

In-house expertise and open source community resources
Up to 70% reduced total cost of ownership (TCO) over self-managed Kafka

Designed to help cut data quality issues by up to 60% and cut data reprocessing costs by 30%

24x7 customer support from the world’s foremost Kafka experts with 3 million+ support hours and a rich partner ecosystem
Enterprise Reslience Manually provision, configure, and scale Kafka clusters based on expected load

Self-monitoring for service disruptions and unexpected changes to workloads
Autoscale clusters up and down from 0 to GBps without over-provisioning infrastructure or introducing risk of an outage

Deploy in any environment, with global resilience backed by a 99.99% uptime SLA on AWS, Azure, and Google Cloud

How Does Confluent Help You to Take Control of Your Data?

Engineering and data teams choose Kafka for its unique ability to handle high-volume workloads in real time. But being responsible for scaling, maintaining, and monitoring Kafka deployments forces you to invest limited talent, time, and resources into managing and adding features.

Confluent offloads and streamlines Kafka operations and provides essential capabilities for real-time analytics and building context-aware AI out-of-the-box.

How Does the Data Streaming Platform Work?

Streaming your data with Confluent means your technical teams can:
  • Stream data as events or as analytics tables
  • Connect systems and services quickly and securely
  • Govern streams for consistency and compatibility
  • Process streams to create reusable data products
  • Enrich AI systems with business context

The more your teams stream and access data through Confluent, the more use cases and business value they can unlock.

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How Does Your Organization Get Started With Confluent?

Developers & Data Engineers

Start streaming with a free trial for 30 days

Architects & Tech Leads

Identify use cases & deployments for migration

Business Leaders

Explore a recorded session from the Data Visionaries Summit

Why Confluent | FAQs

What is Confluent’s role in a modern data architecture?

Confluent provides a centralized streaming layer that allows organizations to continuously move, share, and govern data across systems.

Instead of building and maintaining hundreds of fragile, point-to-point integrations, teams use event streams to create a shared data foundation and reusable data products. This simplifies architecture and allows data to be accessed consistently and continuously across applications, analytics, and AI initiatives.

For deeper technical details, see our data streaming platform overview.

How is Confluent related to Apache Kafka®?

Apache Kafka is the core streaming technology. Confluent’s platform builds on our cloud-native distribution of Kafka to make it production-ready, more scalable, and more cost-effective for enterprise environments.

Organizations that adopt Confluent benefit from managed operations, advanced governance capabilities, unified stream processing, and global resilience—without having to assemble and maintain those components themselves.

If you’re evaluating the differences more closely, visit our Confluent vs. Kafka comparison page.

Why do enterprises move from self-managed Kafka to Confluent?

Many teams begin with open source Kafka. As adoption expands, operational complexity increases.

Enterprises often transition to Confluent when they need:

  • Greater reliability for mission-critical workloads
  • Stronger governance and security controls
  • Multi-region or multicloud scalability
  • Reduced operational burden on engineering teams

The decision is less about replacing Kafka and more about evolving how it’s operated at scale.

What business problems does Confluent solve?

Confluent helps organizations address foundational data challenges, including:

  • Data silos created by point-to-point integrations
  • Latency introduced by batch processing
  • Inconsistent data across business units
  • Operational strain from managing distributed systems
  • Difficulty orchestrating and deploying AI with batch data in production

By establishing streaming as shared infrastructure, organizations can respond faster to market opportunities while maintaining control and consistency.

Is Confluent only for large enterprises?

No. While many global enterprises use Confluent for mission-critical systems, organizations of all sizes adopt streaming to modernize data architecture.

Teams typically start with a single operational use case—such as microservice orchestration, change data capture pipelines, or operational monitoring—and expand over time as more teams and business units see the need for and the value of data streaming.

How does Confluent support AI and real-time analytics strategies?

AI systems require timely, reliable, and governed data.

Confluent enables organizations to continuously stream trusted data into analytics platforms and AI models. This allows AI systems to operate on current context instead of static snapshots.

Streaming becomes the foundation that keeps operational and analytics estates and AI systems aligned. For architecture-specific guidance, explore our use case resources.

How does Confluent improve operational efficiency?

Without a shared streaming foundation, organizations rely on custom integrations and batch pipelines that require constant maintenance. Confluent reduces complexity by:

  • Standardizing how data is shared and discovered
  • Reducing infrastructure management overhead
  • Reducing the need for and costs of data reprocessing
  • Providing enterprise-grade reliability

This allows engineering teams to focus on building capabilities that drive revenue rather than maintaining infrastructure.

When is the right time to evaluate Confluent?

Teams typically evaluate Confluent when:

  • Reliability and uptime become business-critical
  • Cloud modernization initiatives are underway
  • Kafka adoption is expanding into new business units
  • AI or real-time initiatives require trusted, continuous data
  • Operational costs and platform management are growing

Confluent often becomes relevant when streaming transitions from experimentation to core infrastructure.

Can Confluent work with our existing systems and cloud providers?

Yes. Confluent is designed to integrate across hybrid, multicloud, and on-premises environments. It supports major cloud providers and connects with widely used data platforms and applications.

This allows organizations to modernize incrementally rather than replacing existing investments.

What’s the best way to get started?

Getting started depends on your role:

  • Developers and engineers often begin with a free trial to explore use cases.
  • Architects evaluate migration paths and platform consolidation strategies.
  • Business leaders review strategic value, ROI analysis, and modernization roadmaps.

You can try Confluent for free, speak with a streaming expert, or explore customer success stories to see how similar organizations adopted streaming.