Real-Time Applications with
Continuous Data Streams

Turn live data streams into applications, actionable insights, and automated actions from the moment you start to detect events, trends, and patterns —with no lengthy setup, and separate tools required

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Simplified Streaming
for Precision and Speed


Process millions of events per second with low latency, powerful pattern detection, and automated observability—all designed to simplify your real-time streaming to deliver faster, smarter results

Real-Time Event Detection

Identify patterns, anomalies, and trends instantly with powerful complex event processing capabilities, ensuring timely responses to dynamic data

Low-Latency, High-Throughput Processing

Handle millions of events per second with ultra-low latency, enabling fast and accurate data-driven decisions

Zero DevOps Complexity

Deploy, monitor, and scale streaming jobs without manual configurations as we manage failover, resource allocation, and high availability

Agentic Assistant

Leverage your personal assistant with specialized LLM Agents to rapidly create and modify stream processing applications

Full Observability

Monitor every step of your stream processing applications with out-of-the-box dashboards for real-time performance and observability metrics

Cutting-Edge Capabilities for Streaming Data

Achieve real-time operational excellence with event-driven applications, low-latency processing, and advanced rule-based automation—all built for maximum efficiency

Event-Driven Applications

Design real-time solutions with support for Kafka, RabbitMQ, HTTP, and more to enable dynamic, event-driven workflows with powerful integration and processing

High Availability

Maintain continuous operations with distributed architecture, failover, and state restoration to ensure reliability under high event throughput

Stream Data Analytics

Process and analyze streams with sub-second latency using advanced filtering, aggregation, and window-based analytics for operational insights

Complex Event Processing

Detect patterns, trends, and anomalies with constructs like sequence, patterns, and time-based rules for real-time actionable insights

Advanced Pattern Matching

Identify critical or missing events in streams with sophisticated rule-based pattern detection to automate workflows

No-Code Drag & Drop

Build streaming workflows visually with a no-code interface while retaining full-code flexibility for advanced customizations

Real-Time Zero-ETL

Transform and enrich data inline directly from sources, eliminating pre-processing and reducing pipeline latency

Stateful Stream Processing

Enable low-latency processing with in-memory tables, indexed lookups, and dynamic queries for consistent stateful operations

Rule-Based Decisioning

Execute static and dynamic real-time decisions with filters, match functions, and trend detection, automating dynamic workflows

Transforming Data Streams
into Actions and Insights

Alert Generation with Threshold-Based Monitoring

Managing API usage or monitoring dynamic systems requires robust alerting capabilities based on static and dynamic thresholds.

Stream processing enables real-time throttling by monitoring events and triggering alerts when thresholds are breached. This ensures controlled API usage, operational stability, and proactive issue detection.

Gain better control over system behavior and reduce risks with intelligent threshold-based alerting.

Data Preprocessing for Fault Tolerant Systems

Streaming data often contains noise and requires preprocessing for meaningful analysis and fault tolerance in critical scenarios like healthcare monitoring.

Real-time preprocessing cleanses data by removing irrelevant attributes and reshaping it for downstream analytics. Fault tolerance ensures uninterrupted data flow and accurate anomaly detection, such as glucose monitoring in patients.

Ensure data reliability and operational continuity in high-stakes environments.

Pattern Detection and Trend Analysis

Identifying patterns and trends over time is critical for optimizing business operations, such as resource allocation or demand forecasting.

Stream processing analyzes event trends, like rising rider requests in specific locations, and automates resource distribution, ensuring higher efficiency and revenue generation.

Stay ahead of demand with actionable insights from real-time trend analysis.

Rule-Based Decision Making

Businesses require dynamic rule execution for automated decision-making based on predefined or database-stored rules.

Stream processing executes static rules stored in relational databases, dynamically injecting runtime variables for real-time decisions without redeployment. Modify rules effortlessly to align with evolving business goals.

Streamline decision-making processes with adaptive rule management.

Long-Running Time-Based Aggregations

Aggregating large-scale data over time is vital for identifying trends, spotting anomalies, and making informed decisions.

Stream processing supports continuous aggregation over time, enabling insights into key metrics like sales trends in shopping malls, helping businesses optimize operations and strategies.

Unlock meaningful insights with advanced aggregation over long-running periods.

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