← Glossary / Webhook Data Push

What is Webhook Data Push?

Webhook data push is an event-driven delivery mechanism where a scraping pipeline actively sends extracted records to a client-specified HTTP endpoint the moment they are parsed and validated. Unlike batch delivery to S3 or polling a REST API, webhooks eliminate polling latency and decouple the extraction lifecycle from the ingestion schedule. For time-sensitive datasets like spot pricing, inventory alerts, or live odds, it is the only architecture that guarantees sub-second data freshness.

Event-DrivenData DeliveryLow LatencyHTTP POSTReal-Time
// 02 — definitions

Push, don't
poll.

The mechanics of event-driven data delivery, why polling is an anti-pattern for live data, and how to handle ingestion at scale.

Ask a DataFlirt engineer →

TL;DR

Webhooks deliver scraped records via HTTP POST requests immediately after extraction and validation. They reduce data latency to milliseconds but require the receiving infrastructure to handle unpredictable traffic spikes, implement idempotent ingestion, and manage retry queues when endpoints go down.

01Definition & structure
A webhook data push is a user-defined HTTP callback. When a scraping pipeline successfully extracts and validates a record, it immediately constructs a JSON payload and sends an HTTP POST request to a URL provided by the client. The structure typically includes the data payload, metadata about the extraction job (timestamp, source URL, schema version), and cryptographic headers to verify authenticity.
02Polling vs. Push
Polling requires the client to repeatedly ask the server, "Is there new data?" This creates a tradeoff: poll frequently and waste compute on empty responses, or poll infrequently and accept stale data. Push architecture (webhooks) shifts the burden to the provider. The client server sits idle until the exact moment data is ready, ensuring zero polling overhead and absolute minimum latency.
03Idempotency and Retries
Because networks are unreliable, webhook delivery systems must implement retries. If a client endpoint returns a 503 or times out, the payload is queued and retried later. This introduces the risk of duplicate delivery (e.g., the client processed the data but the network dropped their 200 OK response). Therefore, webhook ingestion endpoints must be idempotent — processing the same payload twice must not result in duplicate database rows.
04How DataFlirt handles it
We treat webhook delivery as a first-class infrastructure tier. Our dispatcher is decoupled from the scraping workers via Kafka, ensuring that a slow client endpoint never blocks the extraction pipeline. We enforce a strict 3-second timeout on client responses, automatically handle 429 rate limits via Retry-After headers, and provide a dashboard where clients can view delivery logs, inspect failed payloads, and manually trigger replays.
05The synchronous processing trap
The most common mistake teams make when receiving webhooks is processing the data synchronously. If your endpoint receives the POST, parses the JSON, queries a database, runs an ML model, writes to disk, and then returns a 200 OK, you will inevitably suffer timeouts during traffic spikes. The correct pattern is to accept the request, push the raw payload to an internal queue (like RabbitMQ or SQS), and immediately return a 202 Accepted.
// 03 — delivery math

How fast is
webhook delivery?

Webhook latency is a function of network distance, payload size, and receiver processing time. DataFlirt monitors delivery latency per endpoint to ensure real-time SLAs are met.

End-to-end latency = L = Textract + Tvalidate + Tnetwork
Target < 200ms for live feeds. Network transit is often the largest variable. DataFlirt real-time SLO
Throughput capacity = C = Workers × (1000 / Endpoint_Latency_ms)
If your endpoint takes 500ms to reply, 10 workers can only push 20 records/sec. Queue backpressure model
Retry backoff = Tretry = Base × 2Attempt
Exponential backoff prevents our pipeline from DDoS-ing your recovering server. Standard delivery protocol
// 04 — delivery trace

A live record
hitting your endpoint.

Trace of a single product price update being pushed to a client's ingestion API, including HMAC signature generation, a rate-limit failure, and a successful retry.

HTTP POSTHMAC-SHA256JSON payload
edge.dataflirt.io — live
CAPTURED
// payload generation
record.id: "prod_8921a"
record.price: 149.99

// signature
hmac.secret: loaded
x-dataflirt-signature: "sha256=a9b8c7f2..."

// dispatch attempt 1
POST https://api.client.com/ingest/webhooks
content-type: application/json
content-length: 412

// response
status: 429 Too Many Requests
retry-after: 5

// retry queue
action: queued for retry in 5000ms

// dispatch attempt 2
POST https://api.client.com/ingest/webhooks
status: 201 Created
delivery.latency: 5120ms
// 05 — failure modes

Where webhooks
break down.

Ranked by frequency across DataFlirt's real-time delivery pipelines. In a push architecture, the receiver's infrastructure is almost always the bottleneck.

PIPELINES MONITORED ·   140+ real-time
DELIVERY VOLUME ·  ·  ·   85M/day
UPDATED ·  ·  ·  ·  ·  ·  2026-05-19
01

Receiver rate limits (429s)

% of failures · Client endpoint cannot handle burst traffic
02

Endpoint timeouts (>10s)

% of failures · Synchronous database writes blocking the HTTP response
03

Invalid HMAC validation

% of failures · Mismatched secrets or incorrect payload hashing
04

Unhandled schema evolution

% of failures · Client drops payload due to strict internal parsing
05

DNS resolution failures

% of failures · Client infrastructure routing issues
// 06 — DataFlirt delivery

Guaranteed at-least-once delivery,

with cryptographic proof of origin.

DataFlirt's webhook infrastructure is built on distributed message queues. If your endpoint goes down, we don't drop the data. We queue it, apply exponential backoff, and retry for up to 72 hours. Every payload is signed with an HMAC-SHA256 hash of the body, allowing your ingestion layer to cryptographically verify that the data originated from our pipeline and hasn't been tampered with in transit. We require endpoints to respond within 3 seconds; anything slower is treated as a timeout and re-queued.

Webhook Dispatcher Status

Live metrics from a high-frequency pricing delivery queue.

queue.depth 0 records
delivery.rate 412 req/sec
p95.latency 114ms
auth.method HMAC-SHA256
retry.backlog 14 records
endpoint.status healthy

Stay ahead of the pipeline

Data engineering
intel, weekly.

Anti-bot shifts, scraping infrastructure updates, dataset delivery patterns, and business outcomes from our pipelines. Short, technical, no fluff.

By submitting, you acknowledge our Privacy Notice.

// 07 — FAQ

Common
questions.

About event-driven ingestion, payload security, retry logic, and how DataFlirt ensures your real-time data actually arrives in real time.

Ask us directly →
Why use webhooks instead of polling a REST API? +
Polling wastes resources and introduces artificial latency. If you poll every 60 seconds, your data is on average 30 seconds stale. If you poll every 1 second, 99% of your requests return empty arrays. Webhooks invert the model: you do nothing until the exact millisecond we have new data, at which point we hand it to you. It is the only way to achieve sub-second data freshness.
What happens if my ingestion server crashes? +
We queue the data. DataFlirt's delivery layer uses exponential backoff to retry failed deliveries (500s, 502s, 503s, 504s, and connection refused) for up to 72 hours. Once your server recovers, it will receive the backlog. You must ensure your ingestion logic is idempotent, as network partitions can occasionally result in the same payload being delivered twice.
How do I secure my webhook endpoint from malicious payloads? +
Never rely on obscurity (a secret URL). We sign every payload using an HMAC-SHA256 hash with a shared secret, passed in the X-DataFlirt-Signature header. Your server should compute the hash of the incoming raw request body using the same secret and compare it to our header. If they match, the payload is authentic. We also provide static egress IPs for firewall whitelisting.
Can webhooks handle high-volume batch data? +
They can, but they shouldn't. Webhooks are optimized for low-latency, record-by-record delivery. If a pipeline extracts 5 million records in a daily batch run, sending 5 million individual HTTP POST requests is incredibly inefficient for both our egress and your ingress. For high-volume batch data, S3/GCS delivery or bulk NDJSON files are the correct architectural choice.
How should my endpoint respond to a webhook? +
Acknowledge receipt immediately with a 2xx status code (usually 200 or 201). Do not perform synchronous database writes, heavy processing, or downstream API calls before returning the HTTP response. If your endpoint takes longer than 3 seconds to reply, our dispatcher considers it a timeout, severs the connection, and pushes the record to the retry queue. Put the payload in a local queue (like Redis or Kafka) and return 200 immediately.
Is webhook delivery GDPR compliant? +
The delivery mechanism itself is transport-agnostic regarding privacy. However, because webhooks push data directly into your infrastructure, you become the data controller the moment the HTTP request completes. We enforce TLS 1.3 for all webhook transit to ensure data is encrypted over the wire, satisfying the secure transit requirements of GDPR and CCPA.
$ dataflirt scope --new-project --target=webhook-data-push READY

Tell us what
to extract.
We do the rest.

20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous feed across millions of records — we scope, build, and operate the pipeline.

Start a pipeline → View pricing
hello@dataflirt.com  ·  Bengaluru  ·  IST  ·  typical reply < 4h
// 08 — keep reading

Related glossary terms

DevTools

REST API Data Delivery

The pull-based alternative to webhooks, better suited for on-demand historical queries.

Read →
Data Delivery

S3 Data Delivery

The standard delivery mechanism for high-volume, non-real-time batch datasets.

Read →
Data Engineering

Data Latency

The metric webhooks are designed to minimize — the time between extraction and usability.

Read →
Data Engineering

Real-Time Data Pipeline

The end-to-end architecture that requires webhook delivery to function effectively.

Read →