Real-time Streaming Platform
Process streaming data at scale with sub-second latency. Built with inspiration from Snowflake's analytics, RisingWave's streaming SQL, and Databricks' ML capabilities.
What is Real-time Streaming?
Real-time streaming is the continuous processing of data as it arrives, enabling instant insights and actions. Unlike traditional batch processing, streaming processes data in real-time, making it perfect for applications that require immediate responses.
Real-time data processing pipeline
Benefits of Real-time Streaming in Dataverses
Unlock the power of real-time data processing with our comprehensive streaming platform designed for modern data-driven applications.
Real-time Streaming Features in Dataverses
Discover how our streaming platform combines the power of modern data processing with intuitive interfaces and enterprise-grade reliability.
Streaming Features
Dataverses streaming platform features
Data Sources
Database CDC, Kafka, Kinesis, Pulsar Database CDC, Kafka, Kinesis, Pulsar Database CDC, Kafka, Kinesis, Pulsar
Learn MoreStream Processing
Database CDC, Kafka, Kinesis, Pulsar Database CDC, Kafka, Kinesis, Pulsar Database CDC, Kafka, Kinesis, Pulsar
Learn MoreReal-time Analytics
Database CDC, Kafka, Kinesis, Pulsar Database CDC, Kafka, Kinesis, Pulsar Database CDC, Kafka, Kinesis, Pulsar
Learn MoreML on Streams
Database CDC, Kafka, Kinesis, Pulsar Database CDC, Kafka, Kinesis, Pulsar Database CDC, Kafka, Kinesis, Pulsar
Learn MoreStreaming Architecture Overview
High-performance streaming platform inspired by industry leaders
Real-World Streaming Use Cases
See how companies use Dataverses streaming to solve complex real-time challenges
E-commerce Personalization
Real-time product recommendations and dynamic pricing based on user behavior and inventory levels
Business Scenario:
Online retailer optimizing customer experience
Business Challenge:
"How can we provide personalized product recommendations in real-time as customers browse?"
Streaming Analytics Query:
-- Real-time user behavior analysis
SELECT
user_id,
product_category,
click_rate,
time_on_page,
purchase_intent_score
FROM user_behavior_stream
WHERE event_time >= NOW() - INTERVAL '5 minutes'
AND user_id = ?
-- AI-powered recommendation engine
WITH user_preferences AS (
SELECT user_id,
ARRAY_AGG(product_id ORDER BY interaction_score DESC) as top_products
FROM user_interactions
WHERE user_id = ? AND event_time >= NOW() - INTERVAL '1 hour'
GROUP BY user_id
)
SELECT p.product_name, p.price, p.availability,
CASE
WHEN p.availability < 10 THEN p.price * 0.95 -- Dynamic pricing
ELSE p.price
END as recommended_price
FROM products p
JOIN user_preferences up ON p.product_id = ANY(up.top_products)
WHERE p.availability > 0
ORDER BY p.interaction_score DESC
LIMIT 10Key Capabilities:
Real-time user behavior analysis with 50ms latency
Dynamic pricing based on inventory levels and demand
Personalized recommendations with 95% accuracy
Automated A/B testing for recommendation algorithms
Business Impact:
Increased conversion rates by 35% and average order value by 22%
E-commerce Personalization
Real-time product recommendations and dynamic pricing based on user behavior and inventory levels
Related Resources
Everything you need to get started with real-time streaming and build powerful applications.
Code Smarter, Not Harder: Meet the New Notebook Code Generation on Dataverses
Apache Iceberg 1.11.0 Release: Deletion Vectors, Variant Type, and V3 Maturity
Spark Declarative Pipelines in Apache Spark 4.1: A Complete Guide
Iceberg Summit 2026: The Open Table Format That's Powering the Next Generation of Data Lakehouses
Frequently Asked Questions
Everything you need to know about real-time streaming with Dataverses
Real-time streaming processes data as it arrives, enabling immediate insights and actions. Unlike batch processing which processes data in scheduled intervals, streaming provides continuous, low-latency data processing. This makes it ideal for applications requiring instant responses like fraud detection, real-time analytics, and IoT monitoring.
Dataverses supports a wide range of streaming data sources including Apache Kafka, Amazon Kinesis, Apache Pulsar, Google Pub/Sub, and Azure Event Hubs. We also support database change data capture (CDC) from PostgreSQL, MySQL, and MongoDB. Our platform provides native connectors for easy integration with minimal configuration.
Dataverses combines the best features from these industry leaders: Snowflake's powerful analytics capabilities, RisingWave's streaming SQL engine, and Databricks' ML integration. Our platform provides a unified solution that eliminates the need for multiple tools while offering superior performance and ease of use.
Dataverses streaming platform delivers sub-second latency, typically under 100 milliseconds for most operations. For high-frequency trading and other ultra-low latency applications, we can achieve microsecond-level processing. Our architecture is optimized for performance with auto-scaling capabilities to handle millions of events per second.
Yes! Dataverses provides a powerful streaming SQL engine inspired by RisingWave. You can write familiar SQL queries that process data as it streams in, including window functions, aggregations, joins, and complex event processing. Our SQL engine supports both stateless and stateful operations for comprehensive streaming analytics.
Dataverses integrates ML capabilities inspired by Databricks, allowing you to deploy machine learning models on streaming data for real-time predictions. You can train models on historical data and deploy them to process streaming data with automatic model updates and A/B testing capabilities.
Our platform provides comprehensive monitoring including real-time metrics, alerting, and dashboards. You can monitor throughput, latency, error rates, and resource utilization. We also provide detailed query performance analytics and automatic anomaly detection to help optimize your streaming applications.
Dataverses provides enterprise-grade fault tolerance with exactly-once processing guarantees. Our platform includes automatic failover, data replication, and checkpointing to ensure no data loss. We support both at-least-once and exactly-once semantics based on your application requirements.
Dataverses offers flexible pricing including a free tier for development and testing, pay-as-you-go for production workloads, and enterprise plans with dedicated resources. Our pricing is based on data throughput and compute resources used, with no hidden fees. Contact our sales team for custom enterprise pricing.
Getting started is easy! Sign up for our free tier, connect your data sources using our simple connectors, write your first streaming SQL query, and deploy your application. We provide comprehensive documentation, tutorials, and sample applications to help you get up and running quickly.
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