GOOGLE DRIVE → SNOWFLAKE
Sync CSV, TSV, Excel (.xlsx), or Google Sheets files from Drive into Snowflake on a schedule, with schema discovery and file-level incremental reads.
Configure one file type per source. CSV and TSV folders become tables; Excel and Google Sheets worksheets become tables. Supaflow re-reads files whose Drive modification time falls in the next sync window. Every connector is included on every Supaflow plan — you pay only for the compute your pipelines consume.
For finance and operations teams consolidating recurring Drive exports — with no per-row fees.
Compare Supaflow, Fivetran, Hevo, Airbyte, manual loads, and custom pipelines by source scope, update behavior, deployment model, and billing unit.
Compare Google Drive and Google Sheets ETL options→Verified connector behavior and known limits for this source.
| Feature | How it works | Limit / caveat |
|---|---|---|
| Supported file types | CSV, TSV, Excel (.xlsx), or native Google Sheets, each with format-specific parsing and type inference. | One file type per source. Configure a separate source for each additional file type. |
| Authentication | Choose a Supaflow-managed service account, user OAuth, or a service-account key that you provide. | For either service-account option, share the source folder with the service-account email. |
| File-level incremental reads | After the initial run, the connector uses each file’s Drive modification time to select files in the next sync window. | A selected file is re-read in full. The connector does not emit row-level changes or source-side delete events. |
| Schema discovery | For CSV and TSV, Supaflow inspects headers across all matching files and samples values from a configurable subset for type inference. Spreadsheet schema is inferred per worksheet. | Refresh schema after source columns change. Spreadsheet row 1 is always treated as the header row. |
| Rate limiting | The connector retries Google rate-limit responses and transient server failures with exponential backoff and jitter. | A persistent error is reported after the retry limit is reached. |
| File-level error handling | Choose whether a file error stops the sync or skips that file and continues. | Skipping a file leaves that file’s rows out of the current run; review the Job Details warning. |
| Audit metadata | Connector-added file name and file ID metadata support source lineage in Snowflake. | — |
| Destination | Discovered Drive folders or worksheets map to Snowflake tables through the pipeline. | Append, merge, reset, and schema-evolution behavior are pipeline and destination settings, not source-side change capture. |
Every Supaflow connector is included on every plan at no extra cost. You pay only for compute consumed, measured in Supaflow Credits (1 credit = 1 billable job hour on an S (Small) Agent node). No per-row fees.
The connector has separate paths for CSV, TSV, Excel (.xlsx), and Google Sheets. It resolves duplicate delimited-file headers, accepts the Drive MIME types used for TSV, and reads the cached results stored with spreadsheet formula cells.
For CSV and TSV, matching files in a folder contribute rows to one table. For Excel and Google Sheets, each non-empty worksheet becomes its own table. That mapping is visible during schema selection before the first load.
These source-specific cases affect discovery, parsing, or incremental reads. The notes below explain the failure mode and connector behavior.
Failure mode: An exported workbook can contain a formula without a readable cached numeric or string result.
Evidence: Supaflow reads the cached numeric or string value stored in the workbook; it does not recalculate formulas.
Fix: If neither cached result is available, the cell lands as null instead of stopping the file read.
Failure mode: A CSV header row can repeat a name, such as "amount,amount,amount", leaving the destination columns ambiguous.
Evidence: The connector’s header resolver preserves column order and assigns deterministic suffixes to repeated names.
Fix: Append positional suffixes: amount, amount_2, amount_3. All columns land in Snowflake; analysts see the deterministic suffix pattern.
Failure mode: Files in one CSV or TSV folder can have overlapping but non-identical headers.
Evidence: Schema discovery inspects headers across every matching file and samples a subset of file values for type inference.
Fix: Supaflow unions the discovered headers in first-seen order and fills missing values with null. Refresh schema when source columns change.
Failure mode: Drive can identify tab-separated files as either text/tab-separated-values or text/plain.
Evidence: The TSV discovery path includes both MIME types before applying the optional File Pattern.
Fix: Configure the source as TSV and use File Pattern when the folder also contains other text files.
Choose a Supaflow-managed service account, user OAuth, or your own service-account key. Then select one File Type: CSV, TSV, Excel, or Google Sheets.
Google Drive source docs→Select the folder containing the files for this source. Supaflow creates one table per worksheet for Excel and Google Sheets, or one table per folder for CSV and TSV.
Connect your Snowflake warehouse and review the discovered tables and fields before creating the pipeline.
Snowflake destination docs→Pick a cron or interval. After the initial run, file modification times determine which files are read again.
Schedules docs→Supaflow authenticates with a managed service account, user OAuth, or a service-account key. It discovers files of the configured File Type, maps folders or worksheets to tables, and loads selected objects into Snowflake. After the initial run, Drive modification times determine which files are read again.
Yes. Native Google Sheets, CSV, TSV, and Excel (.xlsx) are supported. Each source is configured for one file type, so use separate sources when a Drive folder contains multiple formats. Each non-empty Google Sheets worksheet becomes its own table.
Fivetran measures connector usage in Monthly Active Rows, while Hevo plans use Events. Every Supaflow connector is included on every plan at no extra cost. You pay only for compute consumed, measured in Supaflow Credits (1 credit = 1 billable job hour on an S (Small) Agent node). No per-row fees. Review each vendor's current pricing and your expected file-change pattern before comparing costs.
Yes, at the file level. After the initial run, Supaflow selects files by Drive modification time and re-reads each selected file in full. It does not capture row-level changes or emit delete events when source files or rows are removed.
Supaflow logs a warning and skips that worksheet when Google refuses the .xlsx export request. Check the folder and spreadsheet permissions first. If file size is the cause, split the spreadsheet and run the sync again.
Yes. Supaflow's sync agent can run in your own VPC so Drive file content and destination traffic use your data plane. The control plane remains managed.
Duplicate headers are resolved with positional suffixes: "amount", "amount_2", "amount_3". Every column lands in Snowflake deterministically, so analysts can distinguish them without rewriting the source file.
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Setup for managed service accounts, OAuth, service-account keys, file types, and troubleshooting.
Connect your Snowflake warehouse, role requirements, type mapping, and sync semantics.
Compare managed connectors, manual loads, and custom pipelines, then follow the Supaflow setup walkthrough.
Every connector is included on every plan. Pay only for compute consumed (Supaflow Credits).
Every connector is included on every Supaflow plan — you pay only for the compute your pipelines consume. Schedule file-level incremental reads and review schema changes before they load.