Data Delivery

API Integration

DataWeBot's RESTful API gives engineering teams direct, real-time access to ecommerce data. Connect via webhooks, SDKs for major languages, and seamless integration with your existing tech stack.

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<100ms

Avg Response

99.99%

API Uptime

10K+

Requests/min

REST

Architecture

API-First Data Delivery

Why API-First Matters for Ecommerce Data

DataWeBot's API-first approach to data delivery gives engineering teams programmatic, on-demand access to ecommerce intelligence without manual file handling or dashboard interaction. Instead of waiting for batch exports or downloading spreadsheets, applications can query product prices, competitor data, and stock levels in real time — enabling automated repricing engines, dynamic catalog management, and live competitive dashboards. This is the approach used by DataWeBot's custom API integration service to connect extracted data directly to clients' existing systems.

The choice between real-time and batch data delivery depends on your use case. Real-time APIs with webhook push notifications are ideal for time-sensitive applications like dynamic pricing, where a competitor price change needs to trigger an automated response within minutes. Batch delivery through scheduled API pulls suits analytical workloads where data freshness of hours or days is sufficient. Most teams benefit from combining both: real-time webhooks for critical alerts and periodic batch pulls for comprehensive analysis. For teams that prefer visual exploration over programmatic access, DataWeBot's dashboard and file download options provide complementary delivery channels.

API Capabilities

Enterprise-grade API infrastructure for programmatic access to your ecommerce data

RESTful API

Clean, well-documented RESTful API endpoints for accessing your scraped ecommerce data. Standard HTTP methods, JSON responses, and predictable URL patterns.

GET, POST, PUT, DELETE methods
JSON request/response format
Cursor-based pagination
Rate limit headers

Webhooks

Receive real-time push notifications when new data is available, prices change, or stock status updates. Configure webhooks for any event type.

Real-time push notifications
Configurable event types
Retry with exponential backoff
Webhook signature verification

Authentication & Security

Secure API access with API keys, OAuth 2.0, and IP whitelisting. All requests are encrypted with TLS 1.3 and logged for audit purposes.

API key authentication
OAuth 2.0 support
IP whitelisting
Request audit logging

Real-Time Data Sync

Access the latest scraped data as soon as extraction jobs complete. Polling and webhook options ensure your systems always have the freshest ecommerce data.

Instant data availability
Polling endpoints
Delta/incremental updates
Batch retrieval support

API Endpoints

Clean, predictable endpoints for every ecommerce data need

GET

/api/v1/products

Retrieve a paginated list of scraped products with optional filters for category, price range, and marketplace.

GET

/api/v1/products/{id}

Get detailed data for a specific product including price history, availability, reviews, and all extracted attributes.

GET

/api/v1/prices

Access price data across products and competitors. Supports date range filtering and aggregation queries.

GET

/api/v1/competitors

Retrieve competitor data including product assortments, pricing strategies, and marketplace presence.

POST

/api/v1/jobs

Create new scraping jobs on demand. Specify target URLs, data fields, and delivery preferences.

GET

/api/v1/webhooks

Manage webhook endpoints. Create, update, and test webhook configurations for event-driven data delivery.

Quick Start Example

Start pulling ecommerce data in minutes with a few lines of code

example.py

import datawebot
              client = datawebot.Client(api_key="your_api_key")

              # Fetch products with filters
              products = client.products.list(
                  marketplace="amazon",
                  category="electronics",
                  min_price=50,
                  max_price=500,
                  limit=100
              )

              for item in products.data:
                  print(f"{item.title}: ${item.price}")
                  print(f"  Rating: {item.rating}/5")
                  print(f"  Stock: {item.stock_status}")

              # Set up a webhook for price changes
              client.webhooks.create(
                  url="https://your-app.com/webhook",
                  events=["price.changed", "stock.updated"]
              )

Available SDKs

Pythonpip install datawebot

Node.jsnpm install @datawebot/sdk

PHPcomposer require datawebot/sdk

Rubygem install datawebot

Getting Started

From API key to production in four steps

Get API Key

Explore Endpoints

Browse DataWeBot's interactive API documentation. Test endpoints directly in the browser with sample requests and responses.

Integrate

Use DataWeBot's SDKs or make direct HTTP requests to pull ecommerce data into your application, database, or BI tool.

Automate

Set up webhooks for real-time notifications and schedule recurring data pulls. Build automated data pipelines.

Wondering how APIs compare to traditional web scraping for ecommerce data? Read our analysis on web scraping vs. official APIs for ecommerce, and learn how DataWeBot's AI-powered extraction platform delivers structured data through the delivery channel that fits your workflow.

Maximizing the Value of API-Based Data Delivery

DataWeBot's API-based data delivery is the preferred method for businesses that need to integrate ecommerce intelligence directly into their operational workflows and automated decision-making systems. Unlike batch file downloads that introduce latency between data collection and action, DataWeBot's API delivery enables real-time or near-real-time access to pricing changes, inventory updates, and competitive movements as soon as they are detected. RESTful API endpoints allow development teams to query specific products, categories, or competitors on demand, while webhook integrations push critical alerts and data updates to downstream systems automatically. This combination of pull and push delivery mechanisms ensures that every team in the organization, from pricing analysts to supply chain managers, always has access to the freshest available data through their preferred tools and interfaces. For agentic commerce systems that make autonomous decisions in real time, DataWeBot's API-based data feeds are the essential delivery mechanism.

The technical design of a data delivery API significantly impacts how effectively teams can consume and act on ecommerce intelligence. DataWeBot's API features well-structured endpoints with consistent response schemas, comprehensive filtering and pagination options, and clear rate limit documentation that reduce integration development time from weeks to hours. Support for bulk queries enables efficient full-catalog refreshes, while incremental change feeds minimize bandwidth and processing requirements for systems that only need to track what has changed since their last sync. Response caching, compression, and CDN distribution ensure fast response times regardless of query complexity or client geography. For organizations building competitive intelligence into their core operations, the quality of DataWeBot's API delivery layer determines whether ecommerce data becomes a strategic asset driving daily decisions or an underutilized resource trapped in periodic reports.

API Integration FAQs

Common questions about rate limits, pagination, webhooks, versioning, and sandbox access.

DataWeBot's default rate limits are 1,000 requests per minute on Pro and 10,000 per minute on Enterprise. These can be increased on request with no hard ceiling for enterprise customers. Rate limit headers are included in every response so your application can manage throttling gracefully.

DataWeBot uses cursor-based pagination, which is more reliable than offset pagination for large datasets. Each response includes a next_cursor field when more records exist. Simply pass this cursor as a parameter in your next request. Cursors remain valid for 24 hours, so you can safely pause and resume large data pulls.

DataWeBot's webhook deliveries use an exponential backoff retry strategy with up to 10 retries over 24 hours. If your endpoint remains unreachable after all retries, the event is logged and you can retrieve missed events through the events API endpoint within a 7-day window.

Yes. DataWeBot's API supports rich server-side filtering so you only receive the data you need. You can filter by marketplace, category, price range, date range, product attributes, and custom fields. Server-side filtering reduces bandwidth consumption and response times compared to pulling full datasets and filtering client-side.

Yes. DataWeBot provides every account with access to a sandbox environment populated with realistic synthetic data. The sandbox mirrors the production API exactly, so you can build and test integrations without consuming real credits or affecting live data. Sandbox credentials are separate from production credentials.

DataWeBot versions all APIs (currently v1) and maintains backward compatibility within major versions. Breaking changes always require a new major version. DataWeBot provides a minimum of 6 months notice before deprecating any API version and keeps old versions running for at least 12 months after deprecation.

A RESTful API follows the Representational State Transfer architectural style, using standard HTTP methods (GET, POST, PUT, DELETE) to perform operations on resources identified by URLs. Unlike SOAP APIs which use complex XML messaging, REST APIs are stateless, use lightweight JSON responses, and leverage existing web infrastructure. This simplicity makes REST the most widely adopted API style for web services, with easier integration and better caching characteristics.

Offset-based pagination uses page numbers and skips a set number of records (e.g., page 5 with 100 records per page skips the first 400). Cursor-based pagination uses an opaque pointer to the last record returned and fetches the next batch from that position. Cursor-based pagination is more reliable for large or frequently changing datasets because it is not affected by records being added or removed between requests, which can cause offset-based pagination to skip or duplicate records.

An idempotent API operation produces the same result regardless of how many times it is called with the same parameters. GET requests are naturally idempotent because reading data does not change it. For write operations, idempotency prevents duplicate actions when network issues cause retries — for example, ensuring a webhook subscription is created only once even if the creation request is sent twice due to a timeout. This property is critical for building reliable automated data pipelines.

An SDK (Software Development Kit) is a pre-built library in a specific programming language that wraps API calls into simple function calls, handling authentication, request formatting, error handling, retries, and pagination automatically. Instead of constructing HTTP requests manually, developers call methods like client.products.list() and receive typed response objects. SDKs reduce integration time from days to hours and prevent common implementation mistakes.

Webhook signature verification is a security mechanism where each webhook delivery includes a cryptographic signature generated using a shared secret key. The receiving server recalculates the signature from the payload and compares it to the one included in the request. If they match, the webhook is authentic. This prevents attackers from sending fake webhook payloads to your endpoints, which could trigger incorrect business actions like fraudulent price changes or fake stock alerts.

Real-time streaming pushes data to your system the instant it becomes available, using technologies like WebSockets or Server-Sent Events that maintain persistent connections. Polling requires your system to repeatedly check for updates at fixed intervals, which introduces latency equal to your polling interval and wastes resources on empty checks. Streaming is ideal for time-critical applications like price monitoring, while polling suits batch analytics where immediate delivery is not required.