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Documentation Index

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Data Integration is how raw data from any source — Shopware, Shopify, a PIM, a CMS, or a custom API — turns into clean, typed, semantically meaningful records your experience can build against.

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What’s involved

Ingest Data

Connectors that pull data from external sources, normalize it, and land it in Data Feeds ready for the next step.

Data Storages

Where your data lives in its shaped, semantic form. Each Data Storage has a schema and receives records from a feed through a Data Sync.

Value Composer

The transformation layer inside every Data Sync. Reshape, combine, filter, and clean raw feed records into the shape the target Data Storage expects.

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How it all connects

Set up a data source

Add a Connector for your commerce system, PIM, CMS, or any REST endpoint. The Connector carries credentials and tells Frontic how to connect and stay in sync with the source.

Raw data lands in a Feed

Each Connector provides one or more Data Feeds — one per resource type. A typical e-commerce integration might expose a products feed and a categories feed. For built-in connectors, the feed records are already normalized — keys, translations, currencies, and parent/child relationships are in place.

A Data Sync transforms and maps

A Data Sync connects a Feed to a Data Storage. It maps the source’s scopes, regions, and locales onto yours, and uses the Value Composer to define how each target field gets its value.

Clean data arrives in a Data Storage

The transformed records are stored in a Data Storage — your project’s semantic layer. This is where “a product” looks like a product, not like a row in someone else’s database.
Once records are in a Data Storage, the API Builder takes over: Detail Blocks, Search Listings, and Page URLs shape that data into the exact API your experience needs.

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Why this matters

Source systems structure data for their own purposes — inventory management, order processing, content editing. That structure carries legacy fields and internal clutter your frontend doesn’t need and shouldn’t see. Data Storages flip that perspective. They’re structured for the consumer — your storefront, your agent, your app — regardless of where the data originally came from. The source’s footprint is gone. What remains is a clean data model shaped for the experience you’re building. This separation has real consequences:
  • Swap sources without breaking frontend code. Your app doesn’t know or care whether product content comes from Shopware, Akeneo, Storyblok, or a custom API. Change the source, update the Data Sync, and the frontend never notices.
  • Keep AI agents focused. A typical source record from a commerce platform can run to thousands of tokens — full of internal metadata, deprecated fields, and noise that invites model drift. A Frontic Data Storage schema for the same product is a fraction of that size, with only the fields that matter.
  • One shape, many surfaces. Whether a human renders a product card or an AI agent composes a recommendation, they read from the same typed, meaningful structure. And because the data is already clean and well-typed, indexing it for semantic search (coming soon) produces rich embeddings that make LLM-powered discovery work out of the box.

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Combining data from multiple sources

Each Data Storage is connected to exactly one Data Feed through a Data Sync. But most shopping experiences pull data from more than one system — products from Shopware, editorial content from Storyblok, reviews from Reviews.io. There are two ways to bring them together:
  • A Data Storage field can reference data from another storage by a shared key. For example, your Products storage could pull in feature descriptions from a ProductFeatures storage that syncs from Storyblok, while the product itself comes from Shopware. See mapped fields for how to set this up.
  • Keep each source in its own Data Storage and combine them at query time. Your frontend queries one endpoint and gets the composed result — products with their reviews, editorial content, and whatever else you need — without the storages knowing about each other. See how to nest blocks and listings inside a block.

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Where to go next

Set up Ingest

Connect your first source and land records in a Data Feed.

Shape your data

Define a Data Storage schema and wire a Data Sync.

Value Composer

How the per-field transformation works — static, mapped, and computed values.