Sync Investments Data
to Flat Files
& Spreadsheets
Automatically sync your Investments data to cloud storage and spreadsheets with Flatly's no-code, triggerless, turnkey integration service.
About the integration: Flatly connects to almost 10,000 financial institutions in the US and Canada via Plaid. Flatly integrates with Investments's official APIs.
Available Datasets
Flatly can sync the following datasets from Investments.
- 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
Send Investments data to
Flatly can sync Investments 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.
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:
Flatly vs Tiller
Tiller is built for personal budgeting in a spreadsheet. Flatly is built for moving raw bank data into the tools a business already reports from.
| Tiller | Flatly (Paperweight) | |
|---|---|---|
| Price | $99 a year | $199 a year, or $19 a month |
| Sync frequency | About once a day | Every 6 hours |
| Destinations | Google Sheets, Excel | Google Sheets, Excel Online, BigQuery, Looker Studio, Power BI, SFTP, S3 and 17 more |
| Data | Bank, card and investment accounts | Bank, card and investment accounts, plus 70+ business apps |
| Bank connections | Yodlee, with Plaid as a backup | Plaid |
| Categorization | Tiller AutoCat rules, plus budgeting templates | Plaid's personal finance categories on every transaction, primary and detailed. Raw data only, ready for your own models |
Choose Tiller for a household budget in Google Sheets. Choose Flatly when bank data needs to land in a warehouse or BI tool, refresh more than once a day, or sit next to data from Stripe, QuickBooks or payroll.
Prices as of October 2026. See Tiller's pricing.
Querying Investments - 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.