Problem: Accessing weather data involves inconsistent APIs, formats, and units—slowing down data engineering and causing hard‑to‑reproduce pipelines.

Solution: Wetterdienst unifies open weather data access behind a consistent Python API and CLI, returning Polars DataFrames by default for performance and ergonomics. It provides a declarative request pattern, robust caching, retries, and humanized parameter naming with unit conversion.

Core concepts:

• Polars‑first: All new data operations use Polars (v1.15+); pandas supported for some IO.
• Unified request pattern: Build a request (provider/network/parameters, time window), filter stations, fetch values; get tidy/long data by default.
• Sensible defaults: UTC timestamps, SI units, and humanized parameter names.
• Reliability: diskcache caching, stamina‑based retries, timezone handling.
• Provider architecture: Consistent interfaces across DWD, ECCC, EA, NOAA/NWS, Geosphere, IMGW, Eaufrance Hubeau, WSV, etc.
• Multiple interfaces: Python API, CLI, and an optional REST API.

Examples (live demo):

• Station metadata: discovery and filtering by station id/region/parameter.
• Timeseries retrieval: selecting daily/hourly parameters, time windows, and exporting.
• Settings: toggling humanization and unit conversion; switching to wide vs. long shape when needed.
• Integration: using CLI for quick fetches; REST API for cross‑language consumption.

Ecosystem & exports:

• Extras for databases (DuckDB, PostgreSQL, MySQL, CrateDB), Pandas/Xarray/Zarr exports, plotting (matplotlib/plotly), and SQL querying.
• Works well in ETL/ML: cache for speed, schema‑stable outputs, and consistent units.

Performance patterns:

• Prefer Polars transformations; leverage caching; batch requests per provider.
• Use parallel processing where beneficial; handle slow/remote datasets with retries.

Limitations & trade‑offs:

• Some providers have rate limits or latency; caching and incremental retrieval recommended.
• Not all providers expose identical parameters—Wetterdienst normalizes interfaces but respects data source constraints.

Proposed Outline (30 minutes)

• 0:00–3:00 The problem: fragmented weather APIs and inconsistent data
• 3:00–7:00 Wetterdienst in 5 minutes: concepts, request pattern, settings
• 7:00–17:00 Live demo: stations → values (Polars), caching, units, CLI/REST
• 17:00–23:00 Integrations and exports (DuckDB/DBs, Pandas/Xarray, plotting)
• 23:00–27:00 Performance patterns, pitfalls, provider nuances
• 27:00–30:00 Q&A

Target Audience Data engineers, scientists, and platform teams who need reliable weather data for analytics, ML, and operations.

Prerequisites Basic Python and DataFrame experience (Polars or pandas); familiarity with ETL/ML pipelines helpful.

Key Takeaways

• A unified, Polars‑first workflow to access and normalize open weather data.
• Practical patterns for station discovery, timeseries retrieval, unit conversion, and caching.
• How to integrate Wetterdienst via Python, CLI, and REST, and export to common formats and databases.

Links

• Repo: https://github.com/earthobservations/wetterdienst
• Docs: https://wetterdienst.readthedocs.io/
• REST API: https://wetterdienst.eobs.org/
• Examples: https://github.com/earthobservations/wetterdienst/tree/main/examples