Data Streaming Services
Leverage the power of real-time data with our robust data streaming services. Built for high throughput, low latency, and massive scalability, these services enable you to process and analyze data as it's generated, unlocking immediate insights and enabling responsive applications.
Key Features
- ⚡Real-time Processing: Ingest and process data streams with sub-second latency.
- 🔗Scalable Ingestion: Handle fluctuating data volumes effortlessly, from kilobytes to petabytes.
- 📊Advanced Analytics: Integrate with analytics engines for real-time dashboards and predictive modeling.
- 🔒Secure & Reliable: End-to-end encryption and guaranteed delivery mechanisms.
- 🛠️Integration: Seamless integration with other App Services and popular third-party tools.
Core Components
Our data streaming ecosystem is comprised of several key components designed to work harmoniously:
1. Event Hubs (Ingestion & Buffering)
A highly scalable data streaming platform and event ingestion service. It can capture millions of events per second so you can develop and run real-time analytics and big data streaming applications. Event Hubs is designed for high throughput, low latency, and durability, making it ideal for telemetry, log collection, and event sourcing patterns.
# Example: Sending data to Event Hubs
curl -X POST \
https://your-event-hub.service.azure.net/your-event-hub-name/messages \
-H 'Content-Type: application/json' \
-H 'Authorization: SharedAccessSignature ...' \
-d '{
"deviceId": "device001",
"timestamp": "2023-10-27T10:00:00Z",
"temperature": 25.5,
"humidity": 60.2
}'
2. Stream Analytics (Processing & Transformation)
A fully managed, serverless real-time analytics service that helps you analyze and process high volumes of streaming data from sources like Event Hubs and IoT Hub. It provides a low-code, declarative query language (similar to SQL) to define processing logic, windowing functions, and complex event processing (CEP) patterns.
-- Example: Aggregating temperature readings over a 5-minute window
SELECT
System.Timestamp AS WindowEnd,
AVG(temperature) AS AverageTemperature
INTO
output-stream
FROM
input-stream TIMESTAMP BY timestamp
GROUP BY
TumblingWindow(minute, 5)
3. Data Lake Storage (Long-term Storage)
A scalable and secure data lake solution for cloud analytics. It's optimized for storing and processing massive amounts of structured, semi-structured, and unstructured data. Data from streaming services can be archived here for historical analysis, batch processing, and machine learning model training.
Use Cases
- 📱IoT Telemetry: Collect and process data from millions of connected devices.
- 🛒E-commerce Analytics: Track user behavior, analyze sales trends in real-time.
- 🏦Financial Services: Monitor market data, detect fraud, and process transactions.
- 🎮Gaming: Analyze player engagement, detect anomalies, and personalize experiences.
- 🚦Log & Telemetry: Ingest and analyze application logs and system metrics for operational insights.