Drag a CSV onto QueryFlow, connect Salesforce, hit AI Map, schedule if you want it recurring. No Java runtime, no Windows VM, no $299-per-month subscription.
Marketing exports a list of webinar attendees. A vendor sends a CSV of new contacts. Sales hands you a spreadsheet of accounts from a conference. Each one needs to land in Salesforce, mapped to the right object, with the right fields populated. Multiply this by 5-20 times per week and you get the daily grind of a Salesforce admin's job. The tools that exist to do this — native Data Loader, dataloader.io, Jitterbit (RIP on Mac) — are all clunky in their own way.
Step 1: Drag your CSV file onto QueryFlow's Connections panel. It auto-detects the delimiter, parses headers, and infers types. Step 2: Click your existing Salesforce connection (or add one if you haven't). Step 3: Open the Visual ETL pipeline, drag both onto the canvas, click the line between them to open the Field Mapper. Step 4: Hit AI Map to auto-match common field patterns, review the suggestions, click Run.
CSV files come from everywhere with every naming convention. Marketing sends fname instead of FirstName. Sales sends Email Address (with a space) instead of Email. Some files use company while Salesforce expects Account. QueryFlow's AI Map handles 25+ common synonym groups out of the box: email_address → Email, fname → FirstName, lname → LastName, phone_cell → MobilePhone, company → Account (with a lookup), and many more. You can also save custom mappings per CSV source for recurring loads.
Choose your load mode based on what the CSV contains. Insert creates new records — use this for fresh data like new leads. Update modifies existing records using a Salesforce ID — use this for batch updates to known records. Upsert uses an external ID field as a matching key — use this for recurring syncs where the same CSV might contain a mix of new and existing records. The Field Mapper lets you specify the external ID field for Upsert.
For weekly or monthly CSV deliveries from vendors, QueryFlow can watch a folder. Drop the new CSV into your designated folder, and the scheduled pipeline runs against it automatically. Combine this with macOS Hot Folders or a Dropbox sync, and you get a fully automated pipeline: vendor uploads to Dropbox, your Mac syncs the folder, QueryFlow processes the new file and pushes to Salesforce — all without you touching anything.
Salesforce validation errors are notoriously cryptic. QueryFlow surfaces row-level errors in the Observatory dashboard with the Salesforce error message and the source CSV row. You can copy the failed rows back out, fix them in your CSV editor, and re-run just the corrections. This is the workflow that's been missing from every Mac Data Loader since Jitterbit died.
Tools like XL-Connector keep your CSV data inside Excel and write to Salesforce from there. That works, but it locks the workflow to one spreadsheet at a time, makes scheduling impossible, and adds Excel as a required tool. QueryFlow treats CSVs as first-class data sources alongside warehouses and databases — you can join a CSV against a Postgres table, transform with Python, and push the result to Salesforce in one pipeline.
QueryFlow auto-detects comma-delimited (CSV), tab-delimited (TSV), pipe-delimited, and semicolon-delimited files. It handles quoted fields, escaped quotes, and embedded line breaks. UTF-8 encoding is the default, with UTF-16 and Latin-1 fallback for files from older systems.
QueryFlow processes CSV files efficiently up to roughly 1 million rows per file. For larger files, the Bulk API v2 batch size handles chunking automatically. For truly massive imports (10M+ rows), the official Salesforce Data Loader using Bulk API v2 is still the recommended tool for one-time loads.
Yes. For lookup fields like Account on Contact or Owner on Opportunity, you can specify either the Salesforce ID directly or a unique lookup field (such as Account.Name or Owner.Email). QueryFlow resolves the lookup during the load. For non-unique lookup matches, you get a clear error in the Observatory.
QueryFlow handles Unicode correctly. Names with accents, emoji in text fields, Asian characters, and right-to-left scripts all load correctly into Salesforce text fields as long as the destination field has sufficient length. The auto-detected encoding handles most cases; if you have an encoding mismatch, you can override it in the connection settings.
Yes. Click Test Connection on the CSV source to preview the first 100 rows. You can verify the parsing, see the inferred column types, and check that the data looks right before running the full load. The Field Mapper also shows a live sample of the first few rows from each source column.
14-day free trial. Drag your next CSV onto QueryFlow and watch it land in Salesforce in under 5 minutes.