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Banking Spreadsheet Apps

Sync Banking to Google Sheets

Banking Google Sheets

Automatically sync your Banking data into Google Sheets with Flatly's no-code, triggerless, turnkey integration service.

Historical & Dynamic Data
One-way Sync
For Non-technical Users
Connect Banking to Google Sheets Which Banking API does Flatly use?

Source: Banking

Flatly connects to almost 10,000 financial institutions in the US and Canada via Plaid.

Auth: Plaid (OAuth)

Available datasets

  • Balances
  • Balances-Recent
  • Transactions-AllAvailable
  • Transactions-Last X days Date-ranged
  • Transactions-AllAvailable+Accounts Combo
  • Transactions-Last X days+Accounts Combo Date-ranged
  • InvestmentTransactions-All
  • InvestmentTransactions-Last X days Date-ranged
  • InvestmentTransactions-All+Accounts Combo
  • InvestmentTransactions-Last X days+Accounts Combo Date-ranged
  • InvestmentHoldings
  • Liabilities-Credit
  • Liabilities-Mortgage
  • Liabilities-Student

Destination: Google Sheets

Multiple Sheet Tabs in the same Sheet are supported. Google Sheets is an alias for Google Drive which supports multiple formats.

Auth: OAuth

Supported file formats

  • Google Sheets (Parsed)
  • Google Sheets (Raw)
  • XLSX (Parsed)
  • XLSX (Raw)
  • CSV (Parsed)
  • CSV (Raw)
  • NDJSON
  • Markdown (Table)
  • Markdown (Key-Value)

How it works

  1. 1

    Connect Banking

    Authenticate Banking (Plaid (OAuth)) and Google Sheets (OAuth) in the Flatly dashboard. You only have to do this once.

  2. 2

    Choose your datasets

    Pick which Banking datasets to sync and set your sync frequency. The frequencies available to you depend on your Flatly plan.

  3. 3

    Data lands in Google Sheets

    Flatly uploads your Banking data to Google Sheets automatically and refreshes it based on your sync frequency.

What Banking Transactions look like in Google Sheets

Flatly writes a header row to Google Sheets, then one row per record. Nested Banking fields are flattened into their own columns, so the Transactions-AllAvailable dataset arrives as 54 columns.

Banking Transactions-AllAvailable
Sample Banking Transactions-AllAvailable rows in Google Sheets, showing 8 of 54 columns
account_idamountiso_currency_codecounterparties_1_namecounterparties_1_typecounterparties_1_logo_urlcounterparties_1_websitedate
BxBXxLj1m4HMXBm9WZZmCWVbPjX16EHwv99vp28.34USDDoorDashmarketplacehttps://plaid-counterparty-logos.plaid.…doordash.com2023-09-28
BxBXxLj1m4HMXBm9WZZmCWVbPjX16EHwv90vp68.02USDDoorDash 2marketplacehttps://plaid-counterparty-logos.plaid.…doordash.com2023-09-25
BxBXxLj1m4HMXBm9WZZmCWVbPjX16EHwv91vp96.36USDDoorDash 3marketplacehttps://plaid-counterparty-logos.plaid.…doordash.com2023-09-22
Transactions
Example data. Showing 8 of 54 columns.
All 54 Banking Transactions-AllAvailable columns
  • account_id
  • account_owner
  • amount
  • iso_currency_code
  • unofficial_currency_code
  • check_number
  • counterparties_1_name
  • counterparties_1_type
  • counterparties_1_logo_url
  • counterparties_1_website
  • counterparties_1_entity_id
  • counterparties_1_confidence_level
  • counterparties_2_name
  • counterparties_2_type
  • counterparties_2_logo_url
  • counterparties_2_website
  • counterparties_2_entity_id
  • counterparties_2_confidence_level
  • date
  • datetime
  • authorized_date
  • authorized_datetime
  • location_address
  • location_city
  • location_region
  • location_postal_code
  • location_country
  • location_lat
  • location_lon
  • location_store_number
  • name
  • merchant_name
  • merchant_entity_id
  • merchant_category_code
  • logo_url
  • website
  • payment_meta_by_order_of
  • payment_meta_payee
  • payment_meta_payer
  • payment_meta_payment_method
  • payment_meta_payment_processor
  • payment_meta_ppd_id
  • payment_meta_reason
  • payment_meta_reference_number
  • payment_channel
  • pending
  • pending_transaction_id
  • personal_finance_category_primary
  • personal_finance_category_detailed
  • personal_finance_category_confidence_level
  • personal_finance_category_icon_url
  • transaction_id
  • transaction_code
  • transaction_type

Google Sheets

Your data, live in Google Sheets

Flatly refreshes multiple sheets in your Google Drive using isolated folder permissions while still allowing custom naming, moving and sharing.

Multiple Sheet Tabs Supported.
Each synced data set gets its own named tab inside a single Google Sheet, so everything stays organized and easy to reference from formulas or Looker Studio.
Minimal Drive permissions.
Flatly only has access to its own App Folder — it cannot read or modify anything else in your Google Drive. It also requests your account email to track which Drive is connected to each job.
Looker Studio ready.
Google Looker Studio reads Google Sheets files directly from Google Drive, making it easy to build dashboards on top of your synced data without any additional connectors.

FAQ: Banking

What is the difference between "banking" and "Plaid"?

Plaid is a trusted proxy for banking integrations. Flatly uses "Plaid" and "Banking" synonymously because, importantly, many users are unfamiliar with Plaid and simply want to find a data integration solution that works with their bank. In the data sync context Banking, Finance and Investments are all aliases for Plaid.

More information:

FAQ: Banking to Google Sheets

How frequently can I sync data from Banking to Google Sheets?
Flatly allows you to set custom sync frequencies ranging from every 10 minutes to once daily, depending on your Flatly pricing plan.
Does Flatly modify my Banking data during the sync?
No. Flatly provides a strict one-way sync. It extracts your historical and dynamic data from Banking and securely loads it into Google Sheets without altering your original source data.
Do I need to write code to connect Banking to Google Sheets?
No code is required. Flatly is a turnkey, no-code integration platform designed for non-technical users to securely connect their data sources and destinations in minutes.

“The interface is unbelievable. Flatly is so easy to use. After years of struggling with Zapier this is life changing.”

Melissa Cable
Project Manager, Aeolidia

Full data refresh

Maintain up-to-date flat files when any nested or bundled data changes in your source app.

Connection Management

Manage multiple tenants, with connection health monitoring.

Bulk Job Management

Control multiple jobs at the same time, with live progress monitoring.

Merged datasets

Export left-joined combo objects like deals+products.

Write options

Refresh one flat file or create a stack of timestamped versions.

Filterability

Include only the columns or views essential for your reporting.

Notifications

Get alerts about your syncs via email, webhooks or Slack notifications.

Logging

Access up to one thousand log entries for your sync operations.

Custom Field Enrichment

Get useful values in your data instead of identifiers or definitions.

Metadata Attachment

See file statistics alongside your flat files to keep track of their origins.

Dark UI Mode

Reduce eye strain and save energy by using our built in dark mode.

Programmatic Jobs

Queue jobs by making API calls or agent requests.

Time Stamp Enrichment

Get human-readable dates and times from UNIX timestamps.

High frequency

10-minute refresh cycles mean your data store is near real-time.

High capacity

Reliably flatten billions of cells to CSV files without hiccups.

Advanced Security

Two-factor authentication and Destination Lock controls.

Stripe-powered Billing

Reliable payments provider with automatic, familiar invoicing and 1% of Sales to Stripe Climate.

Mobile access

Manage your sync operations from your phone if you are traveling.

Connect Banking to Google Sheets

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