Drag-and-drop Visual ETL with AI field mapping. 7 source connectors. 9 destination types. Scheduled job execution. Claude AI in the SQL editor. Everything you need for data pipelines, native to macOS.
Most enterprise ETL tools assume cloud infrastructure (AWS Glue, Azure Data Factory) or Linux servers (Airflow, Prefect). The Mac-native data engineering category has been thin for years — DBeaver and DataGrip are SQL editors not ETL tools, Fivetran and Hightouch are cloud SaaS not desktop apps, and Jupyter is a notebook not a pipeline builder. QueryFlow fills the gap as a Mac-native, desktop-installed, locally-running ETL tool.
QueryFlow is built in pure Swift, compiled for Apple Silicon and Intel Macs, and uses native macOS frameworks (Liquid Glass surfaces, Metal rendering, Keychain for credentials, background task APIs for scheduling). Launching the app takes under a second. Memory usage is a fraction of equivalent JVM tools. The result: ETL workflows that feel like part of your Mac, not a foreign system.
Snowflake (SQL API v2 with PAT auth), Amazon Redshift (Data API with IAM), PostgreSQL (libpq, all variants including RDS/Aurora/Neon/Supabase), MySQL (3306 TCP, RDS/Aurora/PlanetScale), Salesforce (OAuth 2.0 with full API), Google Sheets (OAuth, any spreadsheet), and CSV/TSV files (local or SFTP). Sources can be combined in pipelines — join a CSV against a Postgres table, transform with Python, push to Salesforce.
Local file (CSV/JSON/Parquet), Amazon S3, SFTP server, Email attachment, Google Sheets tab, Salesforce object (Insert/Update/Upsert/Delete), Snowflake table (writeback), Redshift table (writeback), Postgres/MySQL table (writeback). Every destination supports the same scheduling, error handling, and observability layer.
Pipelines are built in a drag-and-drop canvas. Drag a source card. Drag a destination card. Click the connection line between them to open the Field Mapper. The Mapper shows source columns on the left, destination fields on the right, with bezier curves between mapped pairs. Hit AI Map and QueryFlow auto-matches common patterns using 25+ synonym groups (email_address → Email, fname → FirstName, company → Account).
When pipelines need transformation beyond simple field mapping, Flow Books handle the work. SQL cells and Python cells share state through pandas DataFrames. Run a SQL query, transform the result with pandas, write it to the destination. Common patterns: deduplication, complex joins, conditional logic, format conversion (timestamps, currencies, addresses), enrichment from third-party APIs.
Every pipeline can be scheduled — daily, hourly, every-N-minutes, cron expressions, weekly, monthly. The scheduler runs locally on your Mac using macOS background task APIs. No Airflow cluster, no AWS Lambda, no cron server. Failed runs surface in the Observatory dashboard with full error context and one-click retry.
On an M2 MacBook Pro, typical ETL workloads see this performance: 50K row Postgres-to-Salesforce sync in 2-4 minutes. 100K row Snowflake-to-CSV export in 90 seconds. 1M row Postgres-to-Snowflake replication in 5-8 minutes (warehouse-side bottleneck). The Mac itself is rarely the bottleneck — your source and destination databases set the pace.
QueryFlow is the most actively-developed and feature-complete native Mac ETL tool currently available. Other tools exist (some open-source projects, some enterprise tools with Mac builds) but most are cross-platform JVM apps rather than purpose-built for Mac.
For small-to-medium ETL workloads (under 1M rows per pipeline, fewer than 50 active pipelines, daily/hourly schedules), QueryFlow handles production reliably. For enterprise-scale workloads (multi-billion row pipelines, hundreds of pipelines, strict 24/7 SLAs), cloud orchestration platforms remain the right architecture.
Yes. QueryFlow ships as a universal binary supporting both Intel and Apple Silicon. Performance is noticeably better on Apple Silicon due to native ARM compilation, but Intel Macs are fully supported.
Airbyte is an open-source ELT tool that requires Docker to run locally on Mac. The setup and maintenance overhead is significant, and the UI runs in a browser. QueryFlow is a desktop app — no Docker, no browser, no setup complexity. Airbyte has 300+ source connectors versus QueryFlow's 7; for the most common modern data sources, QueryFlow is dramatically simpler.
Yes. Many users run QueryFlow on a Mac mini ($600 hardware) as an always-on ETL machine. The Mac mini stays awake 24/7, runs scheduled pipelines, and is accessed remotely via Screen Sharing when configuration is needed. This is often dramatically cheaper than equivalent AWS infrastructure for the same workload.
14-day free trial. Build your first native Mac data pipeline today.