From Batch to Real-Time: The Streaming Revolution
Traditional batch processing runs models nightly or hourly. Data arrives throughout the day, but analysis happens later. For many applications, waiting overnight is unacceptable. Fraud detection must flag suspicious transactions immediately. Recommendation engines must update in seconds. Anomaly detection must alert instantly.
Streaming pipelines ingest data continuously and process it with minimal latency (milliseconds to seconds). This enables real-time responses to events as they occur.
Components of a Streaming AI Pipeline
Data Ingestion Layer
Sources like Apache Kafka, AWS Kinesis, or Google Pub/Sub receive events. These message brokers buffer incoming data, enabling producers (event sources) to operate independently from consumers (AI systems).
Kafka can handle millions of events per second reliably. Pub/Sub provides managed simplicity on cloud platforms. Choose based on scale, latency requirements, and operational complexity tolerance.
Stream Processing Layer
Processes streaming data before AI inference. Apache Flink, Spark Streaming, or Kafka Streams transform raw events into features suitable for model inference. This layer handles:
- Filtering: Discard irrelevant events.
- Transformation: Convert raw data to model-ready features.
- Aggregation: Combine multiple events (last 5 minutes of user behavior) into single feature vector.
- Stateful Processing: Maintain context (user session, running totals) across events.
AI Inference Layer
Runs trained models on processed features. Inference servers like Triton or vLLM handle model serving. Must support:
- Low latency (< 100ms typically).
- High throughput (thousands of predictions/second).
- Automatic batching to improve efficiency.
Action Layer
Takes predictions and performs actions: update dashboards, trigger alerts, make API calls, update databases. Actions should be idempotent (processing the same event twice produces same result).
| Stage | Tool Examples | Typical Latency |
|---|---|---|
| Ingestion | Kafka, Kinesis, Pub/Sub | Milliseconds |
| Processing | Flink, Spark Streaming, Kafka Streams | 10 to 100ms |
| Inference | Triton, vLLM, model servers | 10 to 50ms |
| Action | Custom code, webhooks, databases | 10 to 100ms |
Building a Streaming AI Pipeline: Step by Step
Step 1: Define Your Events and Features
What data arrives as streams? User clicks, sensor readings, transactions? What features do models need? Define feature transformations required at each stage.
Step 2: Choose Your Technology Stack
Evaluate based on: throughput requirements, latency tolerances, operational expertise, and total cost of ownership.
Simple pipelines: Kafka or Pub/Sub directly feeding inference servers (good if transformation is minimal).
Complex pipelines: Kafka plus Flink/Spark for complex feature engineering then inference servers.
Step 3: Build the Ingestion Layer
Set up message broker. Configure partitioning for parallelism. Ensure high availability through replication.
Step 4: Implement Stream Processing
Write transformation logic. Handle stateful operations (maintain session state, rolling windows). Test thoroughly with realistic data volumes.
Step 5: Connect to Inference
Route processed features to inference server. Handle batching, error cases, and timeouts. Implement circuit breakers to prevent cascading failures.
Step 6: Implement Actions
Define what happens to predictions. Update databases, send alerts, call APIs. Make actions idempotent to handle duplicate processing.
Step 7: Monitor End-to-End
Track latency at each stage. Monitor for processing delays or inference failures. Alert if latencies exceed thresholds.
Real-World Streaming Examples
Fraud detection: Credit card transactions flow into Kafka. Stream processor checks transaction amount, time, and user history. Inference model scores fraud risk. High-risk transactions trigger action (hold transaction, alert user). Latency: 100 to 500ms.
Recommendation engine: User clicks events flow through stream processor computing user features. Inference server generates personalized recommendations. Results serve to user. Latency: 50 to 100ms for perceived instant recommendations.
Anomaly detection: Sensor data arrives continuously. Stream processor aggregates into 1-minute windows. Inference model detects anomalies. Alerts trigger if anomalies detected. Latency: 1 to 2 seconds.