How do serverless systems handle streaming data?

Serverless systems handle streaming data by leveraging event-driven architectures and managed services designed for real-time processing. When data streams in (like logs, sensor readings, or user activity), serverless platforms use services such as AWS Kinesis, Azure Event Hubs, or Google Cloud Pub/Sub to ingest and buffer the data. These services act as the backbone, managing the flow of records and ensuring durability. Serverless functions (e.g., AWS Lambda, Azure Functions) are then triggered in response to incoming data batches, processing each chunk without requiring persistent infrastructure. For example, Kinesis might invoke a Lambda function every time 100 records accumulate or after a 1-minute window, balancing latency and efficiency.

The key advantage is automatic scaling. Serverless functions spin up instances dynamically to match the streaming workload. If data volume spikes, the platform provisions more function instances to process parallel shards or partitions from the stream. For instance, a Kinesis stream divided into 10 shards can trigger up to 10 Lambda functions concurrently, each handling a shard. This parallelism ensures throughput without manual configuration. However, scaling is constrained by service limits (e.g., AWS Lambda’s regional concurrency cap), so systems must balance shard counts and batch sizes to avoid bottlenecks. Retries and error handling are managed by the streaming service, which replays failed batches until processing succeeds.

Real-world use cases include IoT telemetry processing, real-time analytics, and log aggregation. For example, a fleet of sensors might send GPS data to Kinesis, triggering Lambda functions to compute location trends and alert for anomalies. Similarly, a video streaming platform could use Azure Functions with Event Hubs to track user engagement metrics in real time. Most serverless streaming setups prioritize simplicity over fine-grained control—functions are stateless, and ordering guarantees are limited to individual shards. Developers must design around these constraints, using tools like windowing (grouping data over time) or checkpointing (tracking processed offsets) to ensure accuracy. While cold starts can introduce latency, streaming workloads often tolerate minor delays due to their continuous nature.