What is Data Latency?

Data latency is the total time elapsed between a state change on a target website and that change being reflected in your database. It is a composite metric made up of fetch time, extraction overhead, validation delays, and network transit. In a batch pipeline, latency is measured in hours. In a streaming pipeline, it is measured in milliseconds.

Latency accumulates at three distinct stages. Fetch latency is the network round trip to the target server, heavily influenced by proxy routing and anti-bot challenges. Processing latency is the time spent parsing the DOM, coercing types, and validating schemas. Delivery latency is the time spent queuing the record and writing it to the final destination, such as an S3 bucket or a PostgreSQL table.

Your architecture dictates your latency floor. Batch processing waits for a full crawl to finish before extracting data, resulting in hours of delay. Micro-batching processes chunks of records every few minutes. Stream processing evaluates each HTTP response individually as it arrives, pushing the extracted record to a message queue immediately. Streaming is the only way to achieve sub-second latency.

We build streaming pipelines by default. Our extraction workers parse HTML in memory the moment the socket closes. We validate the schema inline and push the structured record to a Kafka topic. This bypasses disk I/O entirely. For clients requiring real-time pricing intelligence, we deliver webhook payloads in under 800 milliseconds from the initial request.

Many data buyers ask for zero-latency feeds. This is physically impossible. Network transit, DNS resolution, TLS handshakes, and database write locks all take time. A highly optimized scraping pipeline can achieve 200ms latency. Anything faster requires direct access to the target's internal message bus, which is not web scraping.