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Unleash the power of streaming data

No matter where your data comes from and where it needs to go, use StreamNative’s data streaming platform to transform your data into trustworthy data streams and unlock limitless use cases.

Empower Your Use Cases with Data Streaming Architecture

Event-driven Applications

Develop event-driven microservices and custom applications that are scalable, context-aware, and designed for resilience.

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Streaming Data Pipelines

Stream trustworthy, context-aware data in real-time across your operational systems, analytical platforms, and SaaS applications.

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Data Lakehouse

Unify your data seamlessly. Our platform converts Pulsar/Kafka topics into Lakehouse tables, enabling smooth streaming, connectivity, and governance.

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Generative AI

Simplify your data architecture with a unified data platform that manages data for both traditional and real-time model training.

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Boost Your Business Results with Data Streaming

Break the data silos. Distribute trustworthy data in real-time throughout your enterprise and ecosystem to create connected experiences, enhance business efficiencies, and foster innovation.

Transform Your Customer Experiences

Connect with your customers in the most impactful ways in real time. Use a premier data streaming platform to hyper-personalize interactions at every touchpoint with all your customer data.

Enhance Your Backend Operations

From automating insights and making context-aware decisions to boosting the agility and connectivity of your operations, empower your business on demand with real-time data streams.

Launch New Business Services

Tap into new monetization opportunities and revenue channels. Streamline the process of secure real-time data sharing to develop innovative products and services.

Industry

Unlock the Potential of Data Streaming in Your Industry

Learn how to transform data in real-time that deliver instant benefits and address a range of use cases within your sector.

Retail and E-commerce

Transform data in real-time to optimize inventory management, personalize customer experiences, and boost sales efficiency in the fast-paced world of retail and e-commerce.

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TeleCommunication

Enhances network performance monitoring, improves customer service responsiveness, and enables predictive maintenance in the telecommunications sector.

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Automotive

Accelerate advancements in connected vehicles, enhances real-time diagnostics, and enables predictive maintenance to drive innovation in the automotive industry.

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Gaming

Elevate player experiences, enable dynamic in-game adjustments, and optimize content delivery for seamless gaming experiences.

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Manufacturing

Revolutionizes production line efficiency, enables predictive maintenance, and enhances supply chain visibility to empower manufacturing processes.

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Technology

Efficiently manage vast amounts of real-time data. From monitoring application performance and user behavior to optimizing data processing pipelines and ensuring seamless integration with cloud environments, data streaming revolutionizes data management strategies for enhanced operational efficiency and innovation.

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Partners

Implement Data Streaming with our Partners

Enhance your current data investments using StreamNative to deliver trusted data throughout your entire stack in real time.

Cloud Partners
Technology Partners
System Integrators
Cloud Partners
Technology Partners
System Integrators
Use cases

Explore Popular Data Streaming Use Cases

Real-Time Data Analysis
Continuous ETL
CEP
Streaming ML
Real-Time Alerts
Real-Time Data Processing and Analytics

Apache Pulsar's pub-sub messaging system is pivotal in handling massive streams of data from diverse sources in real-time. Complemented by Spark Structured Streaming, organizations can process these streams with minimal latency, facilitating real-time analytics, monitoring, and alerting. Together, Pulsar and Spark form robust pipelines for immediate insights and actionable intelligence from streaming data.

Continuous ETL (Extract, Transform, Load)

In modern data architectures, continuous ETL processes are indispensable. Apache Pulsar simplifies data ingestion from various sources, while Apache Spark empowers efficient transformations through its batch and streaming engines. This synergy allows organizations to ingest data streams via Pulsar and leverage Spark for real-time transformations, enrichments, and aggregations before loading data into downstream systems or data stores.

Complex Event Processing (CEP)

Complex Event Processing demands real-time identification of patterns and correlations in event streams. Apache Pulsar excels in managing high-throughput streams, complemented by Spark's comprehensive stream processing APIs. This combination is ideal for deploying CEP applications, enabling organizations to ingest event streams through Pulsar and utilize Spark to analyze, detect anomalies, and identify trends swiftly, empowering proactive decision-making and rapid response strategies.

Machine Learning on Streaming Data

As organizations integrate machine learning for real-time decision-making, Apache Pulsar and Apache Spark integration becomes pivotal. Pulsar facilitates the ingestion of continuous data streams from sensors, IoT devices, or application logs, while Spark's MLlib library offers scalable machine learning algorithms for streaming data. This integration enables organizations to build and deploy real-time machine learning models for tasks like anomaly detection, predictive maintenance, and personalized recommendations.

Real-Time Monitoring and Alerting

Effective monitoring and alerting systems rely on processing large volumes of streaming data in real-time. Apache Pulsar acts as a robust messaging backbone for collecting and distributing event streams, while Apache Spark analyzes incoming data streams to detect anomalies and trigger alerts based on predefined criteria. This joint solution empowers organizations to monitor systems, applications, and infrastructure in real-time, ensuring prompt detection and response to potential issues or failures.

Use cases

Explore Popular Data Streaming Use Cases

Real-Time Data Analysis
Fuel your AI models with real-time data streams.
Continuous ETL
Combine the power of streaming and lakehouses for unified data management.
CEP
Build resilient, scalable, and event-driven architectures.
Streaming ML
Create flexible, high-performance data pipelines for real-time and batch workloads.
Real-Time Alerts
Create flexible, high-performance data pipelines for real-time and batch workloads.
Real-Time Data Processing and Analytics
Apache Pulsar's pub-sub messaging system is pivotal in handling massive streams of data from diverse sources in real-time. Complemented by Spark Structured Streaming, organizations can process these streams with minimal latency, facilitating real-time analytics, monitoring, and alerting. Together, Pulsar and Spark form robust pipelines for immediate insights and actionable intelligence from streaming data.
Continuous ETL (Extract, Transform, Load)
In modern data architectures, continuous ETL processes are indispensable. Apache Pulsar simplifies data ingestion from various sources, while Apache Spark empowers efficient transformations through its batch and streaming engines. This synergy allows organizations to ingest data streams via Pulsar and leverage Spark for real-time transformations, enrichments, and aggregations before loading data into downstream systems or data stores.
Learn more →
Complex Event Processing (CEP)
Complex Event Processing demands real-time identification of patterns and correlations in event streams. Apache Pulsar excels in managing high-throughput streams, complemented by Spark's comprehensive stream processing APIs. This combination is ideal for deploying CEP applications, enabling organizations to ingest event streams through Pulsar and utilize Spark to analyze, detect anomalies, and identify trends swiftly, empowering proactive decision-making and rapid response strategies.
Learn more →
Machine Learning on Streaming Data
As organizations integrate machine learning for real-time decision-making, Apache Pulsar and Apache Spark integration becomes pivotal. Pulsar facilitates the ingestion of continuous data streams from sensors, IoT devices, or application logs, while Spark's MLlib library offers scalable machine learning algorithms for streaming data. This integration enables organizations to build and deploy real-time machine learning models for tasks like anomaly detection, predictive maintenance, and personalized recommendations.
Learn more →
Real-Time Monitoring and Alerting
Effective monitoring and alerting systems rely on processing large volumes of streaming data in real-time. Apache Pulsar acts as a robust messaging backbone for collecting and distributing event streams, while Apache Spark analyzes incoming data streams to detect anomalies and trigger alerts based on predefined criteria. This joint solution empowers organizations to monitor systems, applications, and infrastructure in real-time, ensuring prompt detection and response to potential issues or failures.
Learn more →