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Data streaming is a modern approach to data movement and processing that enables businesses to harness the value of data the moment it’s created. Rather than waiting for data to be collected and processed in batches, streaming makes it possible to react in real time.
At the core of most streaming systems is Apache Kafka®, the open-source technology that laid the foundation for real-time infrastructure. Built by the original creators of Kafka, Confluent is the category leader in data streaming.
Confluent’s data streaming platform extends Kafka with the enterprise-grade capabilities needed to connect systems, process data in motion, enforce governance, and operate reliably in any environment—cloud, on-prem, or hybrid.
In the context of data, "streaming" means processing and acting on a continuous flow of real-time information or data “events.” It mirrors how the real world works—constantly changing and always in motion. Streaming allows organizations to analyze and respond to this dynamic data without delays from traditional data processing methods.
Streaming systems handle data from a wide variety of formats and volumes—ranging from financial transactions and ecommerce activity to telemetry from devices or digital experiences—aggregating it into a unified, real-time view.
Streaming data refers to the continuous flow of data generated by countless sources: applications, databases, sensors, user interactions, logs, transactions, and more. Rather than waiting for data to accumulate before it’s processed, streaming data is captured and processed in real time—or near real time—to enable faster, smarter decisions.
Stream processing: The real-time processing and analysis of streaming data.
Batch processing vs. Real-time stream processing: Batch processing collects data over time and processes it in chunks (often with delays of hours, days, or even weeks). Stream processing processes data instantly, enabling faster insights and actions.
Event-driven architecture: A system design where events (such as user actions, system outputs, or state changes) trigger downstream processes automatically.
Event streaming: The practice of capturing and processing streams of events as they happen.
Real-time/Near real-time: The ability to process and act on data as soon as it's generated, with minimal latency.
Data products: Reusable data assets designed for consumption by teams, systems, or applications. Data streaming enables more reliable and easily accessible data products by ensuring fresh, consistent data is always available in real time.
Data streaming is being widely adopted across industries where real-time decisions and experiences matter most. These include:
Financial services: For fraud detection, payment processing, and real-time risk analysis
Retail and ecommerce: For personalized shopping experiences and dynamic inventory updates
Transportation and logistics: For GPS tracking, ETA predictions, and route optimization
Manufacturing: For predictive maintenance, quality control, automation, and supply chain visibility
Telecommunications: For optimizing network performance and customer service
Healthcare: For patient monitoring and system-wide data integration
Technology and software: For product telemetry, usage analytics, and system observability
Streaming data architectures, often built on platforms like Apache Kafka®, allow organizations to work with data the moment it's created. Instead of waiting to collect and process data in batches, these systems continuously capture, organize, and analyze information in real time—helping teams make faster, smarter decisions.
By separating where data comes from and where it’s used, streaming architectures provide the flexibility to move quickly, scale efficiently, and adapt to changing business needs. The result: faster innovation, more reliable data pipelines, and the ability to deliver real-time, personalized customer experiences at scale.
Leading companies like Victoria’s Secret have greatly benefited from adopting a streaming architecture that allows them to turn their data into reusable data products that fuel a variety of high-impact use cases:
“We can tap into real-time data and build a matrix out of it so that…teams can then feed into that data to provide financial metrics, customer metrics, marketing preferences, and personalized recommendations to the customers.”
- Harikumar Venkatesan, Platform Engineering Lead, Victoria’s Secret
Modern data streaming platforms offer both technical and business advantages. By enabling data to move and be acted upon in real time, streaming helps organizations become more agile, data-driven, and efficient.
Technical Benefits
Decouples systems: Producers and consumers can operate independently for more flexible architectures
Optimized for high throughput: Handles massive volumes of fast-moving data
Horizontally scalable: Easily scales across distributed infrastructure
Business Benefits
Faster decisions: Power fraud detection, recommendations, or alerts in milliseconds
Operational efficiency: Automate manual processes with real-time triggers
Improved customer experiences: Deliver personalized and context-aware interactions
AI and ML enablement: Feed models with up-to-date data for better accuracy and performance
Cost savings: Reduce the burden of siloed systems, data duplication, and batch inefficiencies
Data streaming powers a nearly limitless number of use cases that deliver real-world business value. Here are just a few examples:
Industry: Financial Services
Company: Citizens Bank
Use Case: Real-time data processing that reduced false positives for fraud detection and sped up loan processing
Results: Citizens Bank saw a 15% reduction in false positives for fraud detection, saving ~$1.2M annually. The company also achieved a ten-point increase in net promoter score (NPS), due in part to 40% faster loan processing times.
Industry: Retail & Ecommerce
Customer: Instacart
Use Case: Real-time data pipelines that enable fast onboarding of new customers and more accurate views of inventory availability
Results: Instacart scaled rapidly, onboarding 500,000 shoppers across the United States in just six weeks. The company is also better able to ensure customer order fulfillment due to real-time inventory tracking
Industry: Software (AI)
Company: Notion
Use Case: Instant processing and enrichment of data that enables advanced product features like generative, AI-powered Autofill
Results: Notion tripled its productivity, overcame scaling limitations, and unlocked new product capabilities
Industry: Manufacturing
Company: Michelin
Use Case: Real-time data streams that fuel accurate views of the company’s inventory across its entire ecosystem
Results: Michelin improved the reliability of reporting and monitoring for raw and semi-finished materials, enhancing the efficiency of its global supply chain. The company also saved 35% in operations costs after moving from self-managed Kafka to Confluent’s fully managed data streaming platform.
Industry: Media & Entertainment
Company: Audacy
Use Case: Streaming live audio to over 200 million listeners at scale with low latency and high reliability
Results: Audacy reduced latency of live streaming audio from 90 seconds to 30 seconds, increasing listener engagement and driving more monetization opportunities.
While Apache Kafka® has become the de facto standard for data streaming, operating it at scale is complex and costly—especially for enterprises.
Common Technical Challenges of Kafka
Scalability: As data volumes grow, maintaining reliability and performance becomes harder
See Apna’s story to discover how Confluent allowed them to overcome scaling challenges
Durability & Fault Tolerance: Streaming systems must be resilient to failures and prevent data loss
See Swiggy’s story to learn how Confluent provides enhanced reliability and resilience at scale
Latency Management: Streaming requires consistently low-latency processing and delivery
See Vimeo’s story to discover how Confluent enabled best-in-class user experiences with zero buffering
Operational Overhead: Managing Kafka requires teams to handle upgrades, monitoring, scaling, and more
See Booking.com’s story to learn more about the challenges of self-managed Kafka operations
Kafka is powerful but doesn’t provide out-of-the-box solutions for processing, security, governance, or integrations. Confluent goes beyond Kafka with a complete data streaming platform that includes:
Real-time stream processing with Apache Flink®
Over 120+ pre-built connectors to reduce custom coding and streamline system integrations
99.99% uptime SLA for mission-critical workloads that require always-on availability
Elastic scaling for effortless expansion as data volumes grow
Stream governance and data quality tools to ensure well-managed data across teams and environments
Enterprise-grade security and compliance for protecting sensitive data and meeting regulatory requirements
Multi-cloud and hybrid deployment flexibility
By reducing the financial and opportunity costs of self-managing Kafka, Confluent enables teams to focus on building business value—not infrastructure.
Early data streaming tools were powerful but incomplete. Kafka introduced durability, scalability, and data reusability—but running it at scale is difficult and resource-intensive. That’s where Confluent comes in.
Built by the original creators of Apache Kafka®, Confluent is the only data streaming platform that delivers the full range of enterprise capabilities organizations need to connect, process, and govern data in real time. With Confluent, you can:
Connect everything: Stream data across legacy systems, SaaS apps, cloud services, and databases
Process data in motion: Enrich, aggregate, and react to data in real time with Apache Flink®
Govern with confidence: Enforce data quality, traceability, and compliance from source to sink
Stream everywhere: Deploy natively across clouds and on-prem, with global availability
Used by companies like Walmart, Expedia, and Bank of America, Confluent helps leading enterprises accelerate digital initiatives, unlock AI, and deliver better customer experiences with data that’s always fresh and always flowing.
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