ETLai 0.1.1

Local, folder-driven Dagster application for CSV transformations with hot-folder sensors and Tkinter configuration UI.

Air-Gapped Desktop Automation Engine

A local Dagster application for folder-driven CSV transformations. Source files, saved column choices, generated outputs, and Dagster run storage remain on the machine.

How it works

Each business pipeline has an inbox and a Dagster sensor. The sensor waits for the required number of stable files, moves them to staging, and launches a job. The job prepares atom parameters, executes the transformation, then moves source files to processed/ or rejected/.

An atom is a reusable transformation module with an execute(params_json) -> result_json interface. Atoms are domain-agnostic — they have no opinion on which column or business context. They do read and write files, so they are not side-effect-free.

A business pipeline applies an atom to specific domain data with its own folder lifecycle, sensor, and saved configuration.

Registered jobs

Job	Files needed	Description
vlookup_rollnumber	2 CSV	Left-join on user-chosen columns (first run: Tkinter picker, then saved)
groupby_religion	1 CSV	Group by user-chosen column with counts, sorted descending
mock_generator	1+ CSV	Generate synthetic data from file headers using Faker
vlookup_then_groupby	2 CSV	Composite: VLOOKUP → GroupBy in a single job

Each job has a same-named sensor. Sensors must be enabled in Dagster UI before they process inbox files.

Installation

Python 3.10+ required.

python3 -m venv .venv
source .venv/bin/activate       # Windows: .venv\Scripts\activate
python -m pip install -r requirements.txt

Tkinter must be available in the Python installation for first-run config.

Run

DAGSTER_HOME="$(pwd)" dagster dev -m definitions

Open http://localhost:3000, enable the desired sensor, and place files in pipelines/<job-name>/inbox/.

Manual execution without UI:

DAGSTER_HOME="$(pwd)" dagster job execute -m definitions -j vlookup_rollnumber

Source modules

Entry points

File	Role
definitions.py	Registers all jobs and sensors with Dagster
pipeline.py	Builds single-atom business jobs via the factory
runners/composite.py	Defines the vlookup_then_groupby composite job

logic/atoms/

Module	Purpose
vlookup.py	Left join two CSVs on specified columns with dtype validation
groupby.py	Group by one column, count, sort descending
mock_generate.py	Infer Faker generators from headers, write synthetic CSVs

runners/

Module	Purpose
pipeline_factory.py	Assembles load → preprocess → execute Dagster jobs
atom_runner.py	Reusable load/execute ops with processed/rejected/notification lifecycle
ops.py	First-run config ops for VLOOKUP, GroupBy, and mock generator
composite.py	VLOOKUP → GroupBy chain with shared config

sensors/

Module	Purpose
hot_folder_sensor.py	Per-pipeline sensor factory: stability check, staging, sweeper

helpers/

Module	Purpose
folders.py	PipelineFolders class — per-pipeline directory lifecycle
config_store.py	JSON config read/write per pipeline
column_picker.py	Tkinter UI for VLOOKUP join/output column selection
notifier.py	macOS/Windows toast notifications, opens output folder on success
file_picker.py	Standalone Tkinter file dialog (not used by registered jobs)
mock_generator.py	Standalone CLI mock generator (separate from the atom/job)

Runtime data

• pipelines/<job>/ — inbox, staging, processed, rejected, output, config.json
• dagster.yaml — SQLite storage configuration
• dagster_storage/ — Dagster run history (persists across restarts)

File and configuration lifecycle

inbox/ → sensor stability check → staging/ → job runs
                                    ├→ processed/ + output/       (success)
                                    └→ rejected/ + *.error.txt    (failure)

Non-matching files (not *.csv/*.xlsx) are swept to rejected/ after 3 minutes with an error description.

Config init pattern

1. First run: pre-process op detects no config.json → shows Tkinter UI → saves
2. Future runs: loads config silently, fully automated
3. To reconfigure: delete pipelines/<job>/config.json

Saved config fields

Job	Fields
vlookup_rollnumber	left_column, right_column, left_output_columns, right_output_columns
groupby_religion	group_column
vlookup_then_groupby	All VLOOKUP fields + group_column
mock_generator	None

Important behavior

• Two-file jobs: lexical filename order determines left/right assignment.
• Atom failures and config cancellation move staged files to rejected/.
• Unexpected crashes may leave files in staging/ — recover manually.
• Notifications are best-effort. Linux has no implementation.
• On success, macOS/Windows opens the output folder immediately (not a click action).

Adding a new business pipeline

1. Create or reuse an atom in logic/atoms/ with execute(params_json) -> str
2. Create a pre-process op in runners/ops.py (with config init pattern)
3. Register in pipeline.py:

   my_pipeline = build_business_pipeline(
       pipeline_name="my_pipeline",
       atom_module=my_atom,
       atom_label="My Pipeline",
       pre_process_op=my_pre_process_op,
   )
   

4. Add sensor in definitions.py:

   my_sensor = build_hot_folder_sensor("my_pipeline", "my_pipeline", min_files=1)
   

5. Folders are created automatically on import.

Development status

No automated tests, lint, or pinned dependency versions. Validate by loading definitions and exercising jobs with disposable CSV inputs.