Sync Shopify Data
to Flat Files
& Spreadsheets
Automatically sync your Shopify data to cloud storage and spreadsheets with Flatly's no-code, triggerless, turnkey integration service.
About the integration: Shopify is a complete commerce platform that lets you start, grow, and manage a business. Flatly integrates with Shopify's official APIs.
Available Datasets
Flatly can sync the following datasets from Shopify.
- ApplicationCharges-All
- ApplicationCredits-All
- Blogs
- Blogs+Articles Combo
- Carrier Services
- Custom Collections
- Customers-All
- Customers-Last X days Date-ranged
- Customers+Metafields Combo
- Customers+Metafields-Last X days Combo Date-ranged
- InventoryLocations
- Marketing Events
- Metafields
- Orders-All
- Orders-Last X days Date-ranged
- Orders-All+Metafields Combo
- Orders-Last X days+Metafields Combo Date-ranged
- Orders+Fulfillments-All Combo
- Orders+Fulfillments-Last X days Combo Date-ranged
- Orders+Refunds-All Combo
- Orders+Refunds-Last X days Combo Date-ranged
- Orders+Transactions-All Combo
- Orders+Transactions-Last X days Combo Date-ranged
- Pages-OnlineStore
- PaymentsBalance
- PaymentsPayouts-All
- PaymentsPayouts-Last X days Date-ranged
- PaymentsPayouts-Last X days+Transactions Combo Date-ranged
- Policies-OnlineStore
- PriceRules
- PriceRules+Discounts Combo
- Products-All
- Products+Variants Combo
- Products+Metafields Combo
- Recurring Application Charges
- Redirects
Send Shopify data to
Flatly can sync Shopify data to any of these destinations.
- Airtable Airtable
- Amazon S3 XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Azure Storage XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Box XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- CSV
- Cloud Storage
- Dropbox XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Excel
- Excel Online XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- FTP XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Google BigQuery NDJSON
- Google Cloud Storage XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Google Drive Google Sheets (Parsed) Google Sheets (Raw) XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Google Looker Studio
- Google Sheets Google Sheets (Parsed) Google Sheets (Raw) XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Looker NDJSON
- Markdown
- Microsoft Power BI
- OneDrive XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- OneDrive for Business XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- SFTP XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- SharePoint XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Smartsheet Smartsheet
- Zoho Sheet Zoho Sheet
Note: Some destinations are aliased, for all intents and purposes they are functionally equivalent.
Shopify
What are Metafields and Metaobjects?
Some of the Shopify data sets Flatly offers include metafields and metaobjects automatically blended in with each parent data set (example: Orders-Last X Days+MetaFields). Since these metas can contain nested data, Flatly outputs these data sets in a left-joined format. The parent to each meta has its fields repeated in each row, whereas each meta is unique to each row. The labels from each meta are presented as column headings, to make the data intelligible for human users.
A non-left-joined option (high column count) is available as well. First, experiment with the existing Flatly Shopify data sets and check if your business intelligence platform can properly relate repeated parents to unique metas without any additional setup. Reach out to Flatly Support for more details.
Querying Shopify - AI or ETL?
Many SaaS applications and databases can be directly queried within AI chat apps using connectors. For ad-hoc, single-user requests, this direct architecture is superior—it offers real-time visibility, high customizability, and bypasses the need for complex data pipelines.
However, ETL (Extract, Transform, Load) can be more favorable when certain requirements exist. You might consider transitioning to an ETL system and a centralized data store when:
- Collaboration is required: Multiple stakeholders (colleagues, partners) need consistent access to the same shared single source of truth.
- Downstream audiences: The queried data must be routed into other internal IT systems, published to team dashboards, or preserved in official records rather than remaining isolated in a chat window.
- Data is complex: The raw data needs heavy cleaning or joining before an AI or non-technical user can accurately understand it.
- Internal capabilities are limited: Your organization lacks the in-house programming resources or infrastructure necessary to reliably build, deploy, and maintain custom agentic workflows or complex data integrations from scratch.
- Numerous accounts: When a high volume of identities or accounts within a data source need to be analyzed in bulk, a sophisticated connection manager is needed.
- Concurrency & infrastructure: If many data sets need to be analyzed in parallel, concurrency can become an issue. The device hosting an AI agent can become saturated without concurrency controls in place, whereas an ETL pipeline is built to handle parallel workloads reliably.