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wtf is airflow_dags?

mapsme/airflow_dags — explained in plain English

Analysis updated 2026-08-08 · repo last pushed 2020-10-29

Audience · dataComplexity · 3/5DormantSetup · hard

TL;DR

A collection of Apache Airflow workflow definitions used by the MAPS.ME team to automate scheduled data tasks like syncing map data and processing route information.

Mindmap

mindmap
  root((repo))
    What it does
      Scheduled data tasks
      Moves data between systems
      Keeps map data updated
    Tech stack
      Apache Airflow
      Python DAG files
    Use cases
      Sync map databases
      Process route data
      Generate reports
    Audience
      Data engineers
      Backend teams
      Mapping companies
    Limitations
      No setup docs
      Requires Airflow knowledge
      Tied to MAPS infrastructure

Code map

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Why would anyone build with this?

REASON 1

Keep map-related data synced between databases on a schedule

REASON 2

Automate nightly processing of user route data

REASON 3

Generate routine reports without manual intervention

What's in the stack?

Apache AirflowPython

How it stacks up

mapsme/airflow_dags000madz000/rfid-attendance00kaku/gallery-slider-block
LanguageTypeScriptJavaScript
Last pushed2020-10-292024-07-222021-05-19
MaintenanceDormantDormantDormant
Setup difficultyhardeasyeasy
Complexity3/52/52/5
Audiencedatadevelopergeneral

Figures from each repo's GitHub metadata at analysis time.

How do you spin it up?

Difficulty · hard Time to first run · 1day+

Requires an existing Apache Airflow deployment plus familiarity with the MAPS.ME data infrastructure to understand the pipelines.

Wtf does this do

This repository, called airflow_dags, is a collection of data workflow scripts used by the MAPS.ME team. It contains the instructions for automating routine data tasks, such as moving information between systems, processing it on a schedule, and keeping map-related data up to date without someone having to manually push buttons. The project is built on Apache Airflow, a tool that lets you define data pipelines as code. Each file in the repo represents a "DAG" (Directed Acyclic Graph), which is essentially a recipe of steps that run in a specific order. Airflow reads these recipes and executes them on a schedule, handling retries and alerting if something goes wrong. The repo itself doesn't run anything on its own, it just holds the definition files that an Airflow deployment would load and execute. This would be useful for data engineers or backend teams at a mapping or navigation company who need to keep data pipelines running reliably. For example, if MAPS.ME needs to nightly process user route data, sync map updates from one database to another, or generate reports, those tasks would live here as scheduled workflows. The README doesn't go into detail about specific pipelines, setup instructions, or how to deploy these workflows. It's essentially just the raw collection of workflow definitions with no additional documentation, so you'd need familiarity with Airflow and the MAPS.ME data infrastructure to make sense of what each individual pipeline does.

Yoink these prompts

Prompt 1
Write an Apache Airflow DAG in Python that syncs data from a PostgreSQL database to another database every night at 2 AM, including retry logic and failure alerts
Prompt 2
Create an Airflow DAG definition that processes daily user route data in batches and writes summary statistics to a reporting table
Prompt 3
Generate a scheduled Airflow workflow that moves files between an S3 bucket and a local server, retrying up to 3 times on failure

Frequently asked questions

wtf is airflow_dags?

A collection of Apache Airflow workflow definitions used by the MAPS.ME team to automate scheduled data tasks like syncing map data and processing route information.

Is airflow_dags actively maintained?

Dormant — no commits in 2+ years (last push 2020-10-29).

How hard is airflow_dags to set up?

Setup difficulty is rated hard, with roughly 1day+ to a first successful run.

Who is airflow_dags for?

Mainly data.

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