You've decided to buy web data. Now comes a question that quietly shapes how useful it'll actually be: how should it be delivered? The same data can arrive as a real-time API, a scheduled dataset file, or a ready-made dashboard — and the wrong choice means your team either can't use it or drowns in it. This guide helps you pick.
Real-time API. Your systems query the data on demand and get fresh results back. Built for automation and integration.
Scheduled dataset. Structured files (JSON, CSV, Excel) delivered on a cadence — daily, hourly — to a location your team pulls from. Built for analysis at rest.
Dashboard. A ready-made visual interface where people view and explore the data without touching code. Built for humans, not systems.
The key question is who or what consumes the data:
| If the consumer is... | Best delivery |
|---|---|
| A software system (repricer, app) | Real-time API |
| An analytics/BI team | Scheduled dataset |
| A non-technical business team | Dashboard |
| A pipeline needing everything | Dataset + API |
| Ops needing alerts | API + webhooks |
Choose it when data must flow into a system automatically and freshness is critical — powering a repricing engine, an app, or a live dashboard you build. Needs engineering to integrate.
Choose it when analysts work with data at rest — loading into BI tools, running models, doing analysis. Simpler than an API, perfect for "give me the full picture each morning."
Choose it when the consumers are people who need to see and act, not integrate — category managers, ops teams, executives. Zero technical lift; less flexible for custom analysis.
| Team | Use case | Right delivery |
|---|---|---|
| Pricing engineering | Feed repricing engine | Real-time API |
| Category analytics | Weekly competitiveness review | Scheduled dataset |
| Retail ops | Daily stockout action | Dashboard + webhook alerts |
| Data science | Train & refresh a model | Dataset (+ API for live) |
Most mature setups end up combining methods: a dataset for analysis, an API for systems, and alerts for exceptions.
Whatever the base delivery, threshold-based webhook alerts are what turn data into action. A daily dataset tells you everything; an alert tells you the one thing that needs a decision right now. The best setups pair a comprehensive feed with sharp alerts.
There's no single "best" delivery — only the best match. API for systems, dataset for analysis, dashboard for people, alerts for action. Start from who consumes the data and work backward, and you'll get data your team actually uses instead of data that sits unopened.
An API is queried on demand by systems for fresh data; a dataset is delivered as files on a schedule for analysis at rest.
When non-technical people need to view and act on the data without integrating it into systems.
Yes — most mature setups combine a dataset for analysis, an API for systems, and webhook alerts for exceptions.
Actowiz Solutions delivers web data via real-time API, scheduled datasets, dashboards, and webhook alerts — whatever fits your team.
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